http://wiki.math.uwaterloo.ca/statwiki/api.php?action=feedcontributions&user=Ali.MSH&feedformat=atomstatwiki - User contributions [US]2022-01-21T14:33:03ZUser contributionsMediaWiki 1.28.3http://wiki.math.uwaterloo.ca/statwiki/index.php?title=genetics&diff=26354genetics2015-11-17T02:36:43Z<p>Ali.MSH: /* Experimental Validation */</p>
<hr />
<div>'''<br />
== Genetic Application of Deep Learning ==<br />
'''<br />
This paper presentation is based on the paper [Hui Y. Xiong1 ''et al'', Science '''347''', 2015] which reveals the importance of deep learning methods in genetic study of disease while using different types of machine-learning approaches would enable us to precise annotation mechanism. These techniques have been done for a wide variety of disease including different cancers which has led to important achievements in mutation-driven splicing. t reach to this goal, various intronic and exonic disease mutations have taken into account to detect variants of mutations. This procedure should enable us to prognosis, diagnosis, and/or control a wide variety of diseases. <br />
<br />
<br />
<br />
<br />
'''<br />
== Introduction ==<br />
'''<br />
It has been a while since whole-genome sequencing been used to detect the source of disease or unwanted malignancies genetically. The idea is to find a hierarchy of mutations tending to such diseases by looking at alterations via genetic variations in the genome and particularly when they occur outside of those domains in which protein-coding happens. In the present paper, a computational method is given to detect those genetic variants which influence RNA splicing. RNA splicing is a modification of pre-messenger RNA (pre-mRNA) when introns are removed and makes the exons joined. Any type of interruptions on this important step of gene expression would lead to various kind of disease such as cancers and neurological disorders.<br />
<br />
[[File:Stat1.jpg]]<br />
<br />
'''<br />
<br />
== Rationale ==<br />
'''<br />
<br />
Deep learning algorithm is used to construct a computational model in which DNA sequences are inputs to predict splicing in human textures. In this model, test variants up to 300 nucleotides into an intron, can then be used to derive a score for variant alterations for splicing.<br />
<br />
[[File:Stat3.jpg]]<br />
<br />
<br />
<br />
'''<br />
== Materials and Methods ==<br />
'''<br />
<br />
The human splicing regulatory model is analyzed by Baysian machine learning method. 10,698 cassette exons has considered in this study as a training case. The goal is to maximize an information-theoretic code quality measure <math>CQ=\sum_e \sum_t D_{KL} (q_{t,e} | r_t ) - D_{KL} (q_{t,e} | p_{t,e} ) </math> where <math>q_{t,e}</math> is the target splicing pattern for exon in tissue t, <math> r_t </math> is the optimized guesser's prediction ignoring possible RNA features, <math>p_{t,e}</math> is the non-trained regulatory prediction on exons, and <math>D_{KL}</math> is the Kullback-Leibler between two distributions. CQ is, in fact, a likelihood function of <math>p_{t,e} </math>. <br />
<br />
The structure of each model is a two-layer neural network of units which are sigmoidal hidden within a considered tissue. In our special case study, nonlinear and texture-dependent correlation between the RNA features and the splicing has considered. In such a model, RNA features provide the inputs to 30 hidden variables at most. Each hidden variable is a sigmoidal non-linearity of its corresponding input. Then by applying a softmax function, the non-linear hidden variable are used to prepare the prediction. Moreover, tissues are also trained jointly as disjoint output units.<br />
<br />
Regarding the complexity of this approach, considering maximum likelihood learning method an overfitting is done for each model. The main learning algorithm applied in this paper are from <ref><br />
Xiong H.Y. ''et al'', Baysian Prediction of tissue-regulated splicing using RNA sequence and cellular context, Bioiformation 27, pp. 2554-2562, 2011.<br />
</ref>. As a generalization of logistic regression, the multinomial regression model has considered linear in log odds ratio domain and without hidden variables. Then the model is trained by taking into account the same objective function, RNA features, splicing patterns, and partitioning the dataset as the Baysian neutral network described in above.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
'''<br />
== Experimental Validation ==<br />
'''<br />
<br />
<br />
To check the accuracy of the suggested splicing regulatory model, in this research, experimental results of several data bases are used including RNA-seq data, ET-PCR data, RNA binding protein affinity data, splicing factor knockdown data, and phenotypic/genotypic data. <br />
<br />
[[File:Stat2.jpg]]<br />
<br /><br />
[[File:Stat6.jpg]]<br />
<br />
<br />
<br />
'''<br />
<br />
== Genome-wide Analysis ==<br />
'''<br />
<br />
As an important implications of genetic variation of splicing regulation, 658420 SNVs mapped to exonic and intronic sequences. Then the effect of each SNV on splicing regulation scored by applying the regulatory model of finding the largest value of the difference in predicted splicing level <math>\nabla \psi</math> across tissues.<br />
<br />
[[File:Stat5.jpg]]<br />
<br />
[[File:Stat8.jpg]]<br />
<br />
<br />
'''<br />
<br />
== Conclusion ==<br />
'''<br />
<br />
The method introduced in this paper represents a technique for disease-causing variants classification and for aberrant splicing malignancies. This computational model was trained to predict DNA sequence splicing in the absence of disease annotations or other existing population data and thus can be compared as a naive approach to the experimental data. Thus this model provides a method to understand the genetic basis of various diseases.<br />
<br />
[[File:Stat7.jpg]]<br />
<br />
'''<br />
== References ==<br />
'''<br />
<br />
[1] Hui Y. Xiong1 ''et al'', The human splicing code reveals new insights into the genetic determinants of disease, Science '''347''', 2015.<br />
<br />
[2] Xiong H.Y. ''et al'', Baysian Prediction of tissue-regulated splicing using RNA sequence and cellular context, Bioiformation '''27''', pp. 2554-2562, 2011.</div>Ali.MSHhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=genetics&diff=26353genetics2015-11-17T02:35:30Z<p>Ali.MSH: </p>
<hr />
<div>'''<br />
== Genetic Application of Deep Learning ==<br />
'''<br />
This paper presentation is based on the paper [Hui Y. Xiong1 ''et al'', Science '''347''', 2015] which reveals the importance of deep learning methods in genetic study of disease while using different types of machine-learning approaches would enable us to precise annotation mechanism. These techniques have been done for a wide variety of disease including different cancers which has led to important achievements in mutation-driven splicing. t reach to this goal, various intronic and exonic disease mutations have taken into account to detect variants of mutations. This procedure should enable us to prognosis, diagnosis, and/or control a wide variety of diseases. <br />
<br />
<br />
<br />
<br />
'''<br />
== Introduction ==<br />
'''<br />
It has been a while since whole-genome sequencing been used to detect the source of disease or unwanted malignancies genetically. The idea is to find a hierarchy of mutations tending to such diseases by looking at alterations via genetic variations in the genome and particularly when they occur outside of those domains in which protein-coding happens. In the present paper, a computational method is given to detect those genetic variants which influence RNA splicing. RNA splicing is a modification of pre-messenger RNA (pre-mRNA) when introns are removed and makes the exons joined. Any type of interruptions on this important step of gene expression would lead to various kind of disease such as cancers and neurological disorders.<br />
<br />
[[File:Stat1.jpg]]<br />
<br />
'''<br />
<br />
== Rationale ==<br />
'''<br />
<br />
Deep learning algorithm is used to construct a computational model in which DNA sequences are inputs to predict splicing in human textures. In this model, test variants up to 300 nucleotides into an intron, can then be used to derive a score for variant alterations for splicing.<br />
<br />
[[File:Stat3.jpg]]<br />
<br />
<br />
<br />
'''<br />
== Materials and Methods ==<br />
'''<br />
<br />
The human splicing regulatory model is analyzed by Baysian machine learning method. 10,698 cassette exons has considered in this study as a training case. The goal is to maximize an information-theoretic code quality measure <math>CQ=\sum_e \sum_t D_{KL} (q_{t,e} | r_t ) - D_{KL} (q_{t,e} | p_{t,e} ) </math> where <math>q_{t,e}</math> is the target splicing pattern for exon in tissue t, <math> r_t </math> is the optimized guesser's prediction ignoring possible RNA features, <math>p_{t,e}</math> is the non-trained regulatory prediction on exons, and <math>D_{KL}</math> is the Kullback-Leibler between two distributions. CQ is, in fact, a likelihood function of <math>p_{t,e} </math>. <br />
<br />
The structure of each model is a two-layer neural network of units which are sigmoidal hidden within a considered tissue. In our special case study, nonlinear and texture-dependent correlation between the RNA features and the splicing has considered. In such a model, RNA features provide the inputs to 30 hidden variables at most. Each hidden variable is a sigmoidal non-linearity of its corresponding input. Then by applying a softmax function, the non-linear hidden variable are used to prepare the prediction. Moreover, tissues are also trained jointly as disjoint output units.<br />
<br />
Regarding the complexity of this approach, considering maximum likelihood learning method an overfitting is done for each model. The main learning algorithm applied in this paper are from <ref><br />
Xiong H.Y. ''et al'', Baysian Prediction of tissue-regulated splicing using RNA sequence and cellular context, Bioiformation 27, pp. 2554-2562, 2011.<br />
</ref>. As a generalization of logistic regression, the multinomial regression model has considered linear in log odds ratio domain and without hidden variables. Then the model is trained by taking into account the same objective function, RNA features, splicing patterns, and partitioning the dataset as the Baysian neutral network described in above.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
'''<br />
== Experimental Validation ==<br />
'''<br />
[[File:Stat2.jpg]]<br />
<br />
To check the accuracy of the suggested splicing regulatory model, in this research, experimental results of several data bases are used including RNA-seq data, ET-PCR data, RNA binding protein affinity data, splicing factor knockdown data, and phenotypic/genotypic data. <br />
<br />
[[File:Stat6.jpg]]<br />
<br />
<br />
<br />
'''<br />
== Genome-wide Analysis ==<br />
'''<br />
<br />
As an important implications of genetic variation of splicing regulation, 658420 SNVs mapped to exonic and intronic sequences. Then the effect of each SNV on splicing regulation scored by applying the regulatory model of finding the largest value of the difference in predicted splicing level <math>\nabla \psi</math> across tissues.<br />
<br />
[[File:Stat5.jpg]]<br />
<br />
[[File:Stat8.jpg]]<br />
<br />
<br />
'''<br />
<br />
== Conclusion ==<br />
'''<br />
<br />
The method introduced in this paper represents a technique for disease-causing variants classification and for aberrant splicing malignancies. This computational model was trained to predict DNA sequence splicing in the absence of disease annotations or other existing population data and thus can be compared as a naive approach to the experimental data. Thus this model provides a method to understand the genetic basis of various diseases.<br />
<br />
[[File:Stat7.jpg]]<br />
<br />
'''<br />
== References ==<br />
'''<br />
<br />
[1] Hui Y. Xiong1 ''et al'', The human splicing code reveals new insights into the genetic determinants of disease, Science '''347''', 2015.<br />
<br />
[2] Xiong H.Y. ''et al'', Baysian Prediction of tissue-regulated splicing using RNA sequence and cellular context, Bioiformation '''27''', pp. 2554-2562, 2011.</div>Ali.MSHhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=genetics&diff=26352genetics2015-11-17T02:21:17Z<p>Ali.MSH: /* Genome-widw Analysis */</p>
<hr />
<div>'''<br />
== Genetic Application of Deep Learning ==<br />
'''<br />
This paper presentation is based on the paper [Hui Y. Xiong1 ''et al'', Science '''347''', 2015] which reveals the importance of deep learning methods in genetic study of disease while using different types of machine-learning approaches would enable us to precise annotation mechanism. These techniques have been done for a wide variety of disease including different cancers which has led to important achievements in mutation-driven splicing. t reach to this goal, various intronic and exonic disease mutations have taken into account to detect variants of mutations. This procedure should enable us to prognosis, diagnosis, and/or control a wide variety of diseases. <br />
<br />
<br />
<br />
<br />
'''<br />
== Introduction ==<br />
'''<br />
It has been a while since whole-genome sequencing been used to detect the source of disease or unwanted malignancies genetically. The idea is to find a hierarchy of mutations tending to such diseases by looking at alterations via genetic variations in the genome and particularly when they occur outside of those domains in which protein-coding happens. In the present paper, a computational method is given to detect those genetic variants which influence RNA splicing. RNA splicing is a modification of pre-messenger RNA (pre-mRNA) when introns are removed and makes the exons joined. Any type of interruptions on this important step of gene expression would lead to various kind of disease such as cancers and neurological disorders.<br />
<br />
[[File:Stat1.jpg]]<br />
<br />
'''<br />
<br />
== Rationale ==<br />
'''<br />
<br />
Deep learning algorithm is used to construct a computational model in which DNA sequences are inputs to predict splicing in human textures. In this model, test variants up to 300 nucleotides into an intron, can then be used to derive a score for variant alterations for splicing.<br />
<br />
<br />
<br />
<br />
'''<br />
== Materials and Methods ==<br />
'''<br />
<br />
The human splicing regulatory model is analyzed by Baysian machine learning method. 10,698 cassette exons has considered in this study as a training case. The goal is to maximize an information-theoretic code quality measure <math>CQ=\sum_e \sum_t D_{KL} (q_{t,e} | r_t ) - D_{KL} (q_{t,e} | p_{t,e} ) </math> where <math>q_{t,e}</math> is the target splicing pattern for exon in tissue t, <math> r_t </math> is the optimized guesser's prediction ignoring possible RNA features, <math>p_{t,e}</math> is the non-trained regulatory prediction on exons, and <math>D_{KL}</math> is the Kullback-Leibler between two distributions. CQ is, in fact, a likelihood function of <math>p_{t,e} </math>. <br />
<br />
The structure of each model is a two-layer neural network of units which are sigmoidal hidden within a considered tissue. In our special case study, nonlinear and texture-dependent correlation between the RNA features and the splicing has considered. In such a model, RNA features provide the inputs to 30 hidden variables at most. Each hidden variable is a sigmoidal non-linearity of its corresponding input. Then by applying a softmax function, the non-linear hidden variable are used to prepare the prediction. Moreover, tissues are also trained jointly as disjoint output units.<br />
<br />
Regarding the complexity of this approach, considering maximum likelihood learning method an overfitting is done for each model. The main learning algorithm applied in this paper are from <ref><br />
Xiong H.Y. ''et al'', Baysian Prediction of tissue-regulated splicing using RNA sequence and cellular context, Bioiformation 27, pp. 2554-2562, 2011.<br />
</ref>. As a generalization of logistic regression, the multinomial regression model has considered linear in log odds ratio domain and without hidden variables. Then the model is trained by taking into account the same objective function, RNA features, splicing patterns, and partitioning the dataset as the Baysian neutral network described in above.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
'''<br />
== Experimental Validation ==<br />
'''<br />
<br />
To check the accuracy of the suggested splicing regulatory model, in this research, experimental results of several data bases are used including RNA-seq data, ET-PCR data, RNA binding protein affinity data, splicing factor knockdown data, and phenotypic/genotypic data. <br />
<br />
<br />
<br />
'''<br />
== Genome-wide Analysis ==<br />
'''<br />
<br />
As an important implications of genetic variation of splicing regulation, 658420 SNVs mapped to exonic and intronic sequences. Then the effect of each SNV on splicing regulation scored by applying the regulatory model of finding the largest value of the difference in predicted splicing level <math>\nabla \psi</math> across tissues.<br />
<br />
[[File:Stat5.jpg]]<br />
<br />
[[File:Stat6.jpg]]<br />
<br />
[[File:Stat7.jpg]]<br />
<br />
'''<br />
<br />
== Conclusion ==<br />
'''<br />
<br />
The method introduced in this paper represents a technique for disease-causing variants classification and for aberrant splicing malignancies. This computational model was trained to predict DNA sequence splicing in the absence of disease annotations or other existing population data and thus can be compared as a naive approach to the experimental data. Thus this model provides a method to understand the genetic basis of various diseases.<br />
<br />
<br />
'''<br />
== References ==<br />
'''<br />
<br />
[1] Hui Y. Xiong1 ''et al'', The human splicing code reveals new insights into the genetic determinants of disease, Science '''347''', 2015.<br />
<br />
[2] Xiong H.Y. ''et al'', Baysian Prediction of tissue-regulated splicing using RNA sequence and cellular context, Bioiformation '''27''', pp. 2554-2562, 2011.</div>Ali.MSHhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=genetics&diff=26346genetics2015-11-17T02:12:31Z<p>Ali.MSH: </p>
<hr />
<div>'''<br />
== Genetic Application of Deep Learning ==<br />
'''<br />
This paper presentation is based on the paper [Hui Y. Xiong1 ''et al'', Science '''347''', 2015] which reveals the importance of deep learning methods in genetic study of disease while using different types of machine-learning approaches would enable us to precise annotation mechanism. These techniques have been done for a wide variety of disease including different cancers which has led to important achievements in mutation-driven splicing. t reach to this goal, various intronic and exonic disease mutations have taken into account to detect variants of mutations. This procedure should enable us to prognosis, diagnosis, and/or control a wide variety of diseases. <br />
<br />
<br />
<br />
<br />
'''<br />
== Introduction ==<br />
'''<br />
It has been a while since whole-genome sequencing been used to detect the source of disease or unwanted malignancies genetically. The idea is to find a hierarchy of mutations tending to such diseases by looking at alterations via genetic variations in the genome and particularly when they occur outside of those domains in which protein-coding happens. In the present paper, a computational method is given to detect those genetic variants which influence RNA splicing. RNA splicing is a modification of pre-messenger RNA (pre-mRNA) when introns are removed and makes the exons joined. Any type of interruptions on this important step of gene expression would lead to various kind of disease such as cancers and neurological disorders.<br />
<br />
[[File:Stat1.jpg]]<br />
<br />
'''<br />
<br />
== Rationale ==<br />
'''<br />
<br />
Deep learning algorithm is used to construct a computational model in which DNA sequences are inputs to predict splicing in human textures. In this model, test variants up to 300 nucleotides into an intron, can then be used to derive a score for variant alterations for splicing.<br />
<br />
<br />
<br />
<br />
'''<br />
== Materials and Methods ==<br />
'''<br />
<br />
The human splicing regulatory model is analyzed by Baysian machine learning method. 10,698 cassette exons has considered in this study as a training case. The goal is to maximize an information-theoretic code quality measure <math>CQ=\sum_e \sum_t D_{KL} (q_{t,e} | r_t ) - D_{KL} (q_{t,e} | p_{t,e} ) </math> where <math>q_{t,e}</math> is the target splicing pattern for exon in tissue t, <math> r_t </math> is the optimized guesser's prediction ignoring possible RNA features, <math>p_{t,e}</math> is the non-trained regulatory prediction on exons, and <math>D_{KL}</math> is the Kullback-Leibler between two distributions. CQ is, in fact, a likelihood function of <math>p_{t,e} </math>. <br />
<br />
The structure of each model is a two-layer neural network of units which are sigmoidal hidden within a considered tissue. In our special case study, nonlinear and texture-dependent correlation between the RNA features and the splicing has considered. In such a model, RNA features provide the inputs to 30 hidden variables at most. Each hidden variable is a sigmoidal non-linearity of its corresponding input. Then by applying a softmax function, the non-linear hidden variable are used to prepare the prediction. Moreover, tissues are also trained jointly as disjoint output units.<br />
<br />
Regarding the complexity of this approach, considering maximum likelihood learning method an overfitting is done for each model. The main learning algorithm applied in this paper are from <ref><br />
Xiong H.Y. ''et al'', Baysian Prediction of tissue-regulated splicing using RNA sequence and cellular context, Bioiformation 27, pp. 2554-2562, 2011.<br />
</ref>. As a generalization of logistic regression, the multinomial regression model has considered linear in log odds ratio domain and without hidden variables. Then the model is trained by taking into account the same objective function, RNA features, splicing patterns, and partitioning the dataset as the Baysian neutral network described in above.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
'''<br />
== Experimental Validation ==<br />
'''<br />
<br />
To check the accuracy of the suggested splicing regulatory model, in this research, experimental results of several data bases are used including RNA-seq data, ET-PCR data, RNA binding protein affinity data, splicing factor knockdown data, and phenotypic/genotypic data. <br />
<br />
<br />
<br />
'''<br />
== Genome-widw Analysis ==<br />
'''<br />
<br />
As an important implications of genetic variation of splicing regulation, 658420 SNVs mapped to exonic and intronic sequences. Then the effect of each SNV on splicing regulation scored by applying the regulatory model of finding the largest value of the difference in predicted splicing level <math>\nabla \psi</math> across tissues.<br />
<br />
<br />
<br />
<br />
<br />
<br />
'''<br />
== Conclusion ==<br />
'''<br />
<br />
The method introduced in this paper represents a technique for disease-causing variants classification and for aberrant splicing malignancies. This computational model was trained to predict DNA sequence splicing in the absence of disease annotations or other existing population data and thus can be compared as a naive approach to the experimental data. Thus this model provides a method to understand the genetic basis of various diseases.<br />
<br />
<br />
'''<br />
== References ==<br />
'''<br />
<br />
[1] Hui Y. Xiong1 ''et al'', The human splicing code reveals new insights into the genetic determinants of disease, Science '''347''', 2015.<br />
<br />
[2] Xiong H.Y. ''et al'', Baysian Prediction of tissue-regulated splicing using RNA sequence and cellular context, Bioiformation '''27''', pp. 2554-2562, 2011.</div>Ali.MSHhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=File:Stat8.jpg&diff=26345File:Stat8.jpg2015-11-17T02:11:34Z<p>Ali.MSH: </p>
<hr />
<div></div>Ali.MSHhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=File:Stat7.jpg&diff=26341File:Stat7.jpg2015-11-17T02:10:17Z<p>Ali.MSH: </p>
<hr />
<div></div>Ali.MSHhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=File:Stat6.jpg&diff=26340File:Stat6.jpg2015-11-17T02:09:56Z<p>Ali.MSH: </p>
<hr />
<div></div>Ali.MSHhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=File:Stat5.jpg&diff=26337File:Stat5.jpg2015-11-17T02:09:38Z<p>Ali.MSH: uploaded a new version of &quot;File:Stat5.jpg&quot;</p>
<hr />
<div></div>Ali.MSHhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=File:Stat5.jpg&diff=26336File:Stat5.jpg2015-11-17T02:09:07Z<p>Ali.MSH: </p>
<hr />
<div></div>Ali.MSHhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=File:Stat4.jpg&diff=26334File:Stat4.jpg2015-11-17T02:08:48Z<p>Ali.MSH: </p>
<hr />
<div></div>Ali.MSHhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=File:Stat3.jpg&diff=26333File:Stat3.jpg2015-11-17T02:08:20Z<p>Ali.MSH: </p>
<hr />
<div></div>Ali.MSHhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=File:Stat2.jpg&diff=26332File:Stat2.jpg2015-11-17T02:07:57Z<p>Ali.MSH: </p>
<hr />
<div></div>Ali.MSHhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=File:Stat1.jpg&diff=26331File:Stat1.jpg2015-11-17T02:05:59Z<p>Ali.MSH: </p>
<hr />
<div></div>Ali.MSHhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=genetics&diff=26329genetics2015-11-17T02:01:14Z<p>Ali.MSH: /* Introduction */</p>
<hr />
<div>'''<br />
== Genetic Application of Deep Learning ==<br />
'''<br />
This paper presentation is based on the paper [Hui Y. Xiong1 ''et al'', Science '''347''', 2015] which reveals the importance of deep learning methods in genetic study of disease while using different types of machine-learning approaches would enable us to precise annotation mechanism. These techniques have been done for a wide variety of disease including different cancers which has led to important achievements in mutation-driven splicing. t reach to this goal, various intronic and exonic disease mutations have taken into account to detect variants of mutations. This procedure should enable us to prognosis, diagnosis, and/or control a wide variety of diseases. <br />
<br />
<br />
<br />
<br />
'''<br />
== Introduction ==<br />
'''<br />
It has been a while since whole-genome sequencing been used to detect the source of disease or unwanted malignancies genetically. The idea is to find a hierarchy of mutations tending to such diseases by looking at alterations via genetic variations in the genome and particularly when they occur outside of those domains in which protein-coding happens. In the present paper, a computational method is given to detect those genetic variants which influence RNA splicing. RNA splicing is a modification of pre-messenger RNA (pre-mRNA) when introns are removed and makes the exons joined. Any type of interruptions on this important step of gene expression would lead to various kind of disease such as cancers and neurological disorders.<br />
<br />
<br />
<gallery><br />
Image:Stat1.jpg|Caption1<br />
Image:Stat2.jpg|Caption2<br />
</gallery><br />
<br />
'''<br />
<br />
== Rationale ==<br />
'''<br />
<br />
Deep learning algorithm is used to construct a computational model in which DNA sequences are inputs to predict splicing in human textures. In this model, test variants up to 300 nucleotides into an intron, can then be used to derive a score for variant alterations for splicing.<br />
<br />
<br />
<br />
<br />
'''<br />
== Materials and Methods ==<br />
'''<br />
<br />
The human splicing regulatory model is analyzed by Baysian machine learning method. 10,698 cassette exons has considered in this study as a training case. The goal is to maximize an information-theoretic code quality measure <math>CQ=\sum_e \sum_t D_{KL} (q_{t,e} | r_t ) - D_{KL} (q_{t,e} | p_{t,e} ) </math> where <math>q_{t,e}</math> is the target splicing pattern for exon in tissue t, <math> r_t </math> is the optimized guesser's prediction ignoring possible RNA features, <math>p_{t,e}</math> is the non-trained regulatory prediction on exons, and <math>D_{KL}</math> is the Kullback-Leibler between two distributions. CQ is, in fact, a likelihood function of <math>p_{t,e} </math>. <br />
<br />
The structure of each model is a two-layer neural network of units which are sigmoidal hidden within a considered tissue. In our special case study, nonlinear and texture-dependent correlation between the RNA features and the splicing has considered. In such a model, RNA features provide the inputs to 30 hidden variables at most. Each hidden variable is a sigmoidal non-linearity of its corresponding input. Then by applying a softmax function, the non-linear hidden variable are used to prepare the prediction. Moreover, tissues are also trained jointly as disjoint output units.<br />
<br />
Regarding the complexity of this approach, considering maximum likelihood learning method an overfitting is done for each model. The main learning algorithm applied in this paper are from <ref><br />
Xiong H.Y. ''et al'', Baysian Prediction of tissue-regulated splicing using RNA sequence and cellular context, Bioiformation 27, pp. 2554-2562, 2011.<br />
</ref>. As a generalization of logistic regression, the multinomial regression model has considered linear in log odds ratio domain and without hidden variables. Then the model is trained by taking into account the same objective function, RNA features, splicing patterns, and partitioning the dataset as the Baysian neutral network described in above.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
'''<br />
== Experimental Validation ==<br />
'''<br />
<br />
To check the accuracy of the suggested splicing regulatory model, in this research, experimental results of several data bases are used including RNA-seq data, ET-PCR data, RNA binding protein affinity data, splicing factor knockdown data, and phenotypic/genotypic data. <br />
<br />
<br />
<br />
'''<br />
== Genome-widw Analysis ==<br />
'''<br />
<br />
As an important implications of genetic variation of splicing regulation, 658420 SNVs mapped to exonic and intronic sequences. Then the effect of each SNV on splicing regulation scored by applying the regulatory model of finding the largest value of the difference in predicted splicing level <math>\nabla \psi</math> across tissues.<br />
<br />
<br />
<br />
<br />
<br />
<br />
'''<br />
== Conclusion ==<br />
'''<br />
<br />
The method introduced in this paper represents a technique for disease-causing variants classification and for aberrant splicing malignancies. This computational model was trained to predict DNA sequence splicing in the absence of disease annotations or other existing population data and thus can be compared as a naive approach to the experimental data. Thus this model provides a method to understand the genetic basis of various diseases.<br />
<br />
<br />
'''<br />
== References ==<br />
'''<br />
<br />
[1] Hui Y. Xiong1 ''et al'', The human splicing code reveals new insights into the genetic determinants of disease, Science '''347''', 2015.<br />
<br />
[2] Xiong H.Y. ''et al'', Baysian Prediction of tissue-regulated splicing using RNA sequence and cellular context, Bioiformation '''27''', pp. 2554-2562, 2011.</div>Ali.MSHhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=genetics&diff=26327genetics2015-11-17T01:57:10Z<p>Ali.MSH: </p>
<hr />
<div>'''<br />
== Genetic Application of Deep Learning ==<br />
'''<br />
This paper presentation is based on the paper [Hui Y. Xiong1 ''et al'', Science '''347''', 2015] which reveals the importance of deep learning methods in genetic study of disease while using different types of machine-learning approaches would enable us to precise annotation mechanism. These techniques have been done for a wide variety of disease including different cancers which has led to important achievements in mutation-driven splicing. t reach to this goal, various intronic and exonic disease mutations have taken into account to detect variants of mutations. This procedure should enable us to prognosis, diagnosis, and/or control a wide variety of diseases. <br />
<br />
<br />
<br />
<br />
'''<br />
== Introduction ==<br />
'''<br />
It has been a while since whole-genome sequencing been used to detect the source of disease or unwanted malignancies genetically. The idea is to find a hierarchy of mutations tending to such diseases by looking at alterations via genetic variations in the genome and particularly when they occur outside of those domains in which protein-coding happens. In the present paper, a computational method is given to detect those genetic variants which influence RNA splicing. RNA splicing is a modification of pre-messenger RNA (pre-mRNA) when introns are removed and makes the exons joined. Any type of interruptions on this important step of gene expression would lead to various kind of disease such as cancers and neurological disorders.<br />
<br />
<br />
<br />
'''<br />
== Rationale ==<br />
'''<br />
<br />
Deep learning algorithm is used to construct a computational model in which DNA sequences are inputs to predict splicing in human textures. In this model, test variants up to 300 nucleotides into an intron, can then be used to derive a score for variant alterations for splicing.<br />
<br />
<br />
<br />
<br />
'''<br />
== Materials and Methods ==<br />
'''<br />
<br />
The human splicing regulatory model is analyzed by Baysian machine learning method. 10,698 cassette exons has considered in this study as a training case. The goal is to maximize an information-theoretic code quality measure <math>CQ=\sum_e \sum_t D_{KL} (q_{t,e} | r_t ) - D_{KL} (q_{t,e} | p_{t,e} ) </math> where <math>q_{t,e}</math> is the target splicing pattern for exon in tissue t, <math> r_t </math> is the optimized guesser's prediction ignoring possible RNA features, <math>p_{t,e}</math> is the non-trained regulatory prediction on exons, and <math>D_{KL}</math> is the Kullback-Leibler between two distributions. CQ is, in fact, a likelihood function of <math>p_{t,e} </math>. <br />
<br />
The structure of each model is a two-layer neural network of units which are sigmoidal hidden within a considered tissue. In our special case study, nonlinear and texture-dependent correlation between the RNA features and the splicing has considered. In such a model, RNA features provide the inputs to 30 hidden variables at most. Each hidden variable is a sigmoidal non-linearity of its corresponding input. Then by applying a softmax function, the non-linear hidden variable are used to prepare the prediction. Moreover, tissues are also trained jointly as disjoint output units.<br />
<br />
Regarding the complexity of this approach, considering maximum likelihood learning method an overfitting is done for each model. The main learning algorithm applied in this paper are from <ref><br />
Xiong H.Y. ''et al'', Baysian Prediction of tissue-regulated splicing using RNA sequence and cellular context, Bioiformation 27, pp. 2554-2562, 2011.<br />
</ref>. As a generalization of logistic regression, the multinomial regression model has considered linear in log odds ratio domain and without hidden variables. Then the model is trained by taking into account the same objective function, RNA features, splicing patterns, and partitioning the dataset as the Baysian neutral network described in above.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
'''<br />
== Experimental Validation ==<br />
'''<br />
<br />
To check the accuracy of the suggested splicing regulatory model, in this research, experimental results of several data bases are used including RNA-seq data, ET-PCR data, RNA binding protein affinity data, splicing factor knockdown data, and phenotypic/genotypic data. <br />
<br />
<br />
<br />
'''<br />
== Genome-widw Analysis ==<br />
'''<br />
<br />
As an important implications of genetic variation of splicing regulation, 658420 SNVs mapped to exonic and intronic sequences. Then the effect of each SNV on splicing regulation scored by applying the regulatory model of finding the largest value of the difference in predicted splicing level <math>\nabla \psi</math> across tissues.<br />
<br />
<br />
<br />
<br />
<br />
<br />
'''<br />
== Conclusion ==<br />
'''<br />
<br />
The method introduced in this paper represents a technique for disease-causing variants classification and for aberrant splicing malignancies. This computational model was trained to predict DNA sequence splicing in the absence of disease annotations or other existing population data and thus can be compared as a naive approach to the experimental data. Thus this model provides a method to understand the genetic basis of various diseases.<br />
<br />
<br />
'''<br />
== References ==<br />
'''<br />
<br />
[1] Hui Y. Xiong1 ''et al'', The human splicing code reveals new insights into the genetic determinants of disease, Science '''347''', 2015.<br />
<br />
[2] Xiong H.Y. ''et al'', Baysian Prediction of tissue-regulated splicing using RNA sequence and cellular context, Bioiformation '''27''', pp. 2554-2562, 2011.</div>Ali.MSHhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=genetics&diff=26326genetics2015-11-17T01:44:24Z<p>Ali.MSH: </p>
<hr />
<div>'''<br />
== Genetic Application of Deep Learning ==<br />
'''<br />
This paper presentation is based on the paper [Hui Y. Xiong1 ''et al'', Science '''347''', 2015] which reveals the importance of deep learning methods in genetic study of disease while using different types of machine-learning approaches would enable us to precise annotation mechanism. These techniques have been done for a wide variety of disease including different cancers which has led to important achievements in mutation-driven splicing. t reach to this goal, various intronic and exonic disease mutations have taken into account to detect variants of mutations. This procedure should enable us to prognosis, diagnosis, and/or control a wide variety of diseases. <br />
<br />
<br />
<br />
<br />
'''<br />
== Introduction ==<br />
'''<br />
It has been a while since whole-genome sequencing been used to detect the source of disease or unwanted malignancies genetically. The idea is to find a hierarchy of mutations tending to such diseases by looking at alterations via genetic variations in the genome and particularly when they occur outside of those domains in which protein-coding happens. In the present paper, a computational method is given to detect those genetic variants which influence RNA splicing. RNA splicing is a modification of pre-messenger RNA (pre-mRNA) when introns are removed and makes the exons joined. Any type of interruptions on this important step of gene expression would lead to various kind of disease such as cancers and neurological disorders.<br />
<br />
<br />
<br />
'''<br />
== Rationale ==<br />
'''<br />
<br />
Deep learning algorithm is used to construct a computational model in which DNA sequences are inputs to predict splicing in human textures. In this model, test variants up to 300 nucleotides into an intron, can then be used to derive a score for variant alterations for splicing.<br />
<br />
<br />
<br />
<br />
'''<br />
== Materials and Methods ==<br />
'''<br />
<br />
The human splicing regulatory model is analyzed by Baysian machine learning method. 10,698 cassette exons has considered in this study as a training case. The goal is to maximize an information-theoretic code quality measure <math>CQ=\sum_e \sum_t D_{KL} (q_{t,e} | r_t ) - D_{KL} (q_{t,e} | p_{t,e} ) </math> where <math>q_{t,e}</math> is the target splicing pattern for exon in tissue t, <math> r_t </math> is the optimized guesser's prediction ignoring possible RNA features, <math>p_{t,e}</math> is the non-trained regulatory prediction on exons, and <math>D_{KL}</math> is the Kullback-Leibler between two distributions. CQ is, in fact, a likelihood function of <math>p_{t,e} </math>. <br />
<br />
The structure of each model is a two-layer neural network of units which are sigmoidal hidden within a considered tissue. In our special case study, nonlinear and texture-dependent correlation between the RNA features and the splicing has considered. In such a model, RNA features provide the inputs to 30 hidden variables at most. Each hidden variable is a sigmoidal non-linearity of its corresponding input. Then by applying a softmax function, the non-linear hidden variable are used to prepare the prediction. Moreover, tissues are also trained jointly as disjoint output units.<br />
<br />
Regarding the complexity of this approach, considering maximum likelihood learning method an overfitting is done for each model. The main learning algorithm applied in this paper are from <ref><br />
Xiong H.Y. ''et al'', Baysian Prediction of tissue-regulated splicing using RNA sequence and cellular context, Bioiformation 27, pp. 2554-2562, 2011.<br />
</ref>. As a generalization of logistic regression, the multinomial regression model has considered linear in log odds ratio domain and without hidden variables. Then the model is trained by taking into account the same objective function, RNA features, splicing patterns, and partitioning the dataset as the Baysian neutral network described in above.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
'''<br />
== Experimental Validation ==<br />
'''<br />
<br />
To check the accuracy of the suggested splicing regulatory model, in this research, experimental results of several data bases are used including RNA-seq data, ET-PCR data, RNA binding protein affinity data, splicing factor knockdown data, and phenotypic/genotypic data. <br />
<br />
<br />
<br />
'''<br />
== Genome-widw Analysis ==<br />
'''<br />
<br />
As an important implications of genetic variation of splicing regulation, 658420 SNVs mapped to exonic and intronic sequences. Then the effect of each SNV on splicing regulation scored by applying the regulatory model of finding the largest value of the difference in predicted splicing level <math>\nabla \psi</math> across tissues.<br />
<br />
<br />
<br />
<br />
<br />
<br />
'''<br />
== Conclusion ==<br />
'''<br />
<br />
<br />
<br />
<br />
'''<br />
== References ==<br />
'''<br />
<br />
[1] Hui Y. Xiong1 ''et al'', The human splicing code reveals new insights into the genetic determinants of disease, Science '''347''', 2015.<br />
<br />
[2] Xiong H.Y. ''et al'', Baysian Prediction of tissue-regulated splicing using RNA sequence and cellular context, Bioiformation '''27''', pp. 2554-2562, 2011.</div>Ali.MSHhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=genetics&diff=26323genetics2015-11-17T01:28:21Z<p>Ali.MSH: </p>
<hr />
<div>'''<br />
== Genetic Application of Deep Learning ==<br />
'''<br />
This paper presentation is based on the paper [Hui Y. Xiong1 ''et al'', Science '''347''', 2015] which reveals the importance of deep learning methods in genetic study of disease while using different types of machine-learning approaches would enable us to precise annotation mechanism. These techniques have been done for a wide variety of disease including different cancers which has led to important achievements in mutation-driven splicing. t reach to this goal, various intronic and exonic disease mutations have taken into account to detect variants of mutations. This procedure should enable us to prognosis, diagnosis, and/or control a wide variety of diseases. <br />
<br />
<br />
<br />
<br />
'''<br />
== Introduction ==<br />
'''<br />
It has been a while since whole-genome sequencing been used to detect the source of disease or unwanted malignancies genetically. The idea is to find a hierarchy of mutations tending to such diseases by looking at alterations via genetic variations in the genome and particularly when they occur outside of those domains in which protein-coding happens. In the present paper, a computational method is given to detect those genetic variants which influence RNA splicing. RNA splicing is a modification of pre-messenger RNA (pre-mRNA) when introns are removed and makes the exons joined. Any type of interruptions on this important step of gene expression would lead to various kind of disease such as cancers and neurological disorders.<br />
<br />
<br />
'''<br />
== Materials and Methods ==<br />
'''<br />
<br />
The human splicing regulatory model is analyzed by Baysian machine learning method. 10,698 cassette exons has considered in this study as a training case. The goal is to maximize an information-theoretic code quality measure <math>CQ=\sum_e \sum_t D_{KL} (q_{t,e} | r_t ) - D_{KL} (q_{t,e} | p_{t,e} ) </math> where <math>q_{t,e}</math> is the target splicing pattern for exon in tissue t, <math> r_t </math> is the optimized guesser's prediction ignoring possible RNA features, <math>p_{t,e}</math> is the non-trained regulatory prediction on exons, and <math>D_{KL}</math> is the Kullback-Leibler between two distributions. CQ is, in fact, a likelihood function of <math>p_{t,e} </math>. <br />
<br />
The structure of each model is a two-layer neural network of units which are sigmoidal hidden within a considered tissue. In our special case study, nonlinear and texture-dependent correlation between the RNA features and the splicing has considered. In such a model, RNA features provide the inputs to 30 hidden variables at most. Each hidden variable is a sigmoidal non-linearity of its corresponding input. Then by applying a softmax function, the non-linear hidden variable are used to prepare the prediction. Moreover, tissues are also trained jointly as disjoint output units.<br />
<br />
Regarding the complexity of this approach, considering maximum likelihood learning method an overfitting is done for each model. The main learning algorithm applied in this paper are from <ref><br />
Xiong H.Y. ''et al'', Baysian Prediction of tissue-regulated splicing using RNA sequence and cellular context, Bioiformation 27, pp. 2554-2562, 2011.<br />
</ref>. As a generalization of logistic regression, the multinomial regression model has considered linear in log odds ratio domain and without hidden variables. Then the model is trained by taking into account the same objective function, RNA features, splicing patterns, and partitioning the dataset as the Baysian neutral network described in above.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
'''<br />
== Experimental Validation ==<br />
'''<br />
<br />
To check the accuracy of the suggested splicing regulatory model, in this research, experimental results of several data bases are used including RNA-seq data, ET-PCR data, RNA binding protein affinity data, splicing factor knockdown data, and phenotypic/genotypic data. <br />
<br />
<br />
<br />
'''<br />
== Genome-widw Analysis ==<br />
'''<br />
<br />
As an important implications of genetic variation of splicing regulation, 658420 SNVs mapped to exonic and intronic sequences. Then the effect of each SNV on splicing regulation scored by applying the regulatory model of finding the largest value of the difference in predicted splicing level <math>\nabla \psi</math> across tissues.<br />
<br />
<br />
'''<br />
== Rationale ==<br />
'''<br />
<br />
<br />
<br />
'''<br />
== Conclusion ==<br />
'''<br />
<br />
<br />
<br />
<br />
'''<br />
== References ==<br />
'''<br />
<br />
[1] Hui Y. Xiong1 ''et al'', The human splicing code reveals new insights into the genetic determinants of disease, Science '''347''', 2015.<br />
<br />
[2] Xiong H.Y. ''et al'', Baysian Prediction of tissue-regulated splicing using RNA sequence and cellular context, Bioiformation '''27''', pp. 2554-2562, 2011.</div>Ali.MSHhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=genetics&diff=26319genetics2015-11-17T00:51:45Z<p>Ali.MSH: </p>
<hr />
<div>'''<br />
== Genetic Application of Deep Learning ==<br />
'''<br />
This paper presentation is based on the paper [Hui Y. Xiong1 ''et al'', Science '''347''', 2015] which reveals the importance of deep learning methods in genetic study of disease while using different types of machine-learning approaches would enable us to precise annotation mechanism. These techniques have been done for a wide variety of disease including different cancers which has led to important achievements in mutation-driven splicing. t reach to this goal, various intronic and exonic disease mutations have taken into account to detect variants of mutations. This procedure should enable us to prognosis, diagnosis, and/or control a wide variety of diseases. <br />
<br />
<br />
<br />
<br />
'''<br />
== Introduction ==<br />
'''<br />
It has been a while since whole-genome sequencing been used to detect the source of disease or unwanted malignancies genetically. The idea is to find a hierarchy of mutations tending to such diseases by looking at alterations via genetic variations in the genome and particularly when they occur outside of those domains in which protein-coding happens. In the present paper, a computational method is given to detect those genetic variants which influence RNA splicing. RNA splicing is a modification of pre-messenger RNA (pre-mRNA) when introns are removed and makes the exons joined. Any type of interruptions on this important step of gene expression would lead to various kind of disease such as cancers and neurological disorders.<br />
<br />
<br />
'''<br />
== Materials and Methods ==<br />
'''<br />
<br />
The human splicing regulatory model is analyzed by Baysian machine learning method. 10,698 cassette exons has considered in this study as a training case. The goal is to maximize an information-theoretic code quality measure <math>CQ=\sum_e \sum_t D_{KL} (q_{t,e} | r_t ) - D_{KL} (q_{t,e} | p_{t,e} ) </math> where <math>q_{t,e}</math> is the target splicing pattern for exon in tissue t, <math> r_t </math> is the optimized guesser's prediction ignoring possible RNA features, <math>p_{t,e}</math> is the non-trained regulatory prediction on exons, and <math>D_{KL}</math> is the Kullback-Leibler between two distributions. CQ is, in fact, a likelihood function of <math>p_{t,e} </math>. <br />
<br />
The structure of each model is a two-layer neural network of units which are sigmoidal hidden within a considered tissue. In our special case study, nonlinear and texture-dependent correlation between the RNA features and the splicing has considered. In such a model, RNA features provide the inputs to 30 hidden variables at most. Each hidden variable is a sigmoidal non-linearity of its corresponding input. Then by applying a softmax function, the non-linear hidden variable are used to prepare the prediction. Moreover, tissues are also trained jointly as disjoint output units.<br />
<br />
Regarding the complexity of this approach, considering maximum likelihood learning method an overfitting is done for each model. The main learning algorithm applied in this paper are from <ref><br />
Xiong H.Y. ''et al'', Baysian Prediction of tissue-regulated splicing using RNA sequence and cellular context, Bioiformation 27, pp. 2554-2562, 2011.<br />
</ref>. As a generalization of logistic regression, the multinomial regression model has considered linear in log odds ratio domain and without hidden variables. Then the model is trained by taking into account the same objective function, RNA features, splicing patterns, and partitioning the dataset as the Baysian neutral network described in above.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
'''<br />
== Experimental Validation ==<br />
'''<br />
<br />
To check the accuracy of the suggested splicing regulatory model, in this research, experimental results of several data bases are used including RNA-seq data, ET-PCR data, RNA binding protein affinity data, splicing factor knockdown data, and phenotypic/genotypic data. <br />
<br />
<br />
<br />
'''<br />
== Spinal Muscular Atropy ==<br />
'''<br />
<br />
<br />
<br />
'''<br />
== Rationale ==<br />
'''<br />
<br />
<br />
<br />
'''<br />
== Conclusion ==<br />
'''<br />
<br />
<br />
<br />
<br />
'''<br />
== References ==<br />
'''<br />
<br />
[1] Hui Y. Xiong1 ''et al'', The human splicing code reveals new insights into the genetic determinants of disease, Science '''347''', 2015.<br />
<br />
[2] Xiong H.Y. ''et al'', Baysian Prediction of tissue-regulated splicing using RNA sequence and cellular context, Bioiformation '''27''', pp. 2554-2562, 2011.</div>Ali.MSHhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=genetics&diff=26315genetics2015-11-17T00:18:06Z<p>Ali.MSH: </p>
<hr />
<div>'''<br />
== Genetic Application of Deep Learning ==<br />
'''<br />
This paper presentation is based on the paper [Hui Y. Xiong1 ''et al'', Science '''347''', 2015] which reveals the importance of deep learning methods in genetic study of disease while using different types of machine-learning approaches would enable us to precise annotation mechanism. These techniques have been done for a wide variety of disease including different cancers which has led to important achievements in mutation-driven splicing. t reach to this goal, various intronic and exonic disease mutations have taken into account to detect variants of mutations. This procedure should enable us to prognosis, diagnosis, and/or control a wide variety of diseases. <br />
<br />
<br />
<br />
<br />
'''<br />
== Introduction ==<br />
'''<br />
It has been a while since whole-genome sequencing been used to detect the source of disease or unwanted malignancies genetically. The idea is to find a hierarchy of mutations tending to such diseases by looking at alterations via genetic variations in the genome and particularly when they occur outside of those domains in which protein-coding happens. In the present paper, a computational method is given to detect those genetic variants which influence RNA splicing. RNA splicing is a modification of pre-messenger RNA (pre-mRNA) when introns are removed and makes the exons joined. Any type of interruptions on this important step of gene expression would lead to various kind of disease such as cancers and neurological disorders.<br />
<br />
<br />
'''<br />
== Materials and Methods ==<br />
'''<br />
<br />
The human splicing regulatory model is analyzed by Baysian machine learning method. 10,698 cassette exons has considered in this study as a training case. The goal is to maximize an information-theoretic code quality measure <math>CQ=\sum_e \sum_t D_{KL} (q_{t,e} | r_t ) - D_{KL} (q_{t,e} | p_{t,e} ) </math> where <math>q_{t,e}</math> is the target splicing pattern for exon in tissue t, <math> r_t </math> is the optimized guesser's prediction ignoring possible RNA features, <math>p_{t,e}</math> is the non-trained regulatory prediction on exons, and <math>D_{KL}</math> is the Kullback-Leibler between two distributions. CQ is, in fact, a likelihood function of <math>p_{t,e} </math>. <br />
<br />
The structure of each model is a two-layer neural network of units which are sigmoidal hidden within a considered tissue. In our special case study, nonlinear and texture-dependent correlation between the RNA features and the splicing has considered. In such a model, RNA features provide the inputs to 30 hidden variables at most. Each hidden variable is a sigmoidal non-linearity of its corresponding input. Then by applying a softmax function, the non-linear hidden variable are used to prepare the prediction. Moreover, tissues are also trained jointly as disjoint output units.<br />
<br />
Regarding the complexity of this approach, considering maximum likelihood learning method an overfitting is done for each model. The main learning algorithm applied in this paper are from <ref><br />
Xiong H.Y. ''et al'', Baysian Prediction of tissue-regulated splicing using RNA sequence and cellular context, Bioiformation 27, pp. 2554-2562, 2011.<br />
</ref><br />
<br />
<br />
<br />
<br />
<br />
'''<br />
== Genome-wide Analysis ==<br />
'''<br />
<br />
<br />
<br />
'''<br />
== Spinal Muscular Atropy ==<br />
'''<br />
<br />
<br />
<br />
'''<br />
== Rationale ==<br />
'''<br />
<br />
<br />
<br />
'''<br />
== Conclusion ==<br />
'''<br />
<br />
<br />
<br />
<br />
'''<br />
== References ==<br />
'''<br />
<br />
[1] Hui Y. Xiong1 ''et al'', The human splicing code reveals new insights into the genetic determinants of disease, Science '''347''', 2015.<br />
<br />
[2] Xiong H.Y. ''et al'', Baysian Prediction of tissue-regulated splicing using RNA sequence and cellular context, Bioiformation '''27''', pp. 2554-2562, 2011.</div>Ali.MSHhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=genetics&diff=26312genetics2015-11-17T00:06:13Z<p>Ali.MSH: </p>
<hr />
<div>'''<br />
== Genetic Application of Deep Learning ==<br />
'''<br />
This paper presentation is based on the paper [Hui Y. Xiong1 ''et al'', Science '''347''', 2015] which reveals the importance of deep learning methods in genetic study of disease while using different types of machine-learning approaches would enable us to precise annotation mechanism. These techniques have been done for a wide variety of disease including different cancers which has led to important achievements in mutation-driven splicing. t reach to this goal, various intronic and exonic disease mutations have taken into account to detect variants of mutations. This procedure should enable us to prognosis, diagnosis, and/or control a wide variety of diseases. <br />
<br />
<br />
<br />
<br />
'''<br />
== Introduction ==<br />
'''<br />
It has been a while since whole-genome sequencing been used to detect the source of disease or unwanted malignancies genetically. The idea is to find a hierarchy of mutations tending to such diseases by looking at alterations via genetic variations in the genome and particularly when they occur outside of those domains in which protein-coding happens. In the present paper, a computational method is given to detect those genetic variants which influence RNA splicing. RNA splicing is a modification of pre-messenger RNA (pre-mRNA) when introns are removed and makes the exons joined. Any type of interruptions on this important step of gene expression would lead to various kind of disease such as cancers and neurological disorders.<br />
<br />
<br />
'''<br />
== Materials and Methods ==<br />
'''<br />
<br />
The human splicing regulatory model is analyzed by Baysian machine learning method. 10,698 cassette exons has considered in this study as a training case. The goal is to maximize an information-theoretic code quality measure <math>CQ=\sum_e \sum_t D_{KL} (q_{t,e} | r_t ) - D_{KL} (q_{t,e} | p_{t,e} ) </math> where <math>q_{t,e}</math> is the target splicing pattern for exon in tissue t, <math> r_t </math> is the optimized guesser's prediction ignoring possible RNA features, <math>p_{t,e}</math> is the nontrained regulatory prediction on exons, and <math>D_{KL}</math> is the Kullback-Leibler between two distributions. CQ is, in fact, a likelihood function of <math>p_{t,e} </math>. <br />
<br />
The structure of each model is a two-layer neural network of units which are sigmoidal hidden within a considered tissue. In our special case study, nonlinear and texture-dependent correlation between the RNA features and the splicing has considered. In such a model, RNA features provide the inputs to 30 hidden variables at most. Each hidden variable is a sigmoidal non-linearity of its corresponding input. Then by applying a softmax function, the non-linear hidden variable are used to prepare the prediction. Moreover, tissues are also trained jointly as disjoint output units.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
'''<br />
== Genome-wide Analysis ==<br />
'''<br />
<br />
<br />
<br />
'''<br />
== Spinal Muscular Atropy ==<br />
'''<br />
<br />
<br />
<br />
'''<br />
== Rationale ==<br />
'''<br />
<br />
<br />
<br />
'''<br />
== Conclusion ==<br />
'''<br />
<br />
<br />
<br />
<br />
'''<br />
== References ==<br />
'''<br />
<br />
[1] Hui Y. Xiong1 ''et al'', The human splicing code reveals new insights into the genetic determinants of disease, Science '''347''', 2015.<br />
<br />
[2]</div>Ali.MSHhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=genetics&diff=26307genetics2015-11-16T23:46:13Z<p>Ali.MSH: </p>
<hr />
<div>'''<br />
== Genetic Application of Deep Learning ==<br />
'''<br />
This paper presentation is based on the paper [Hui Y. Xiong1 ''et al'', Science '''347''', 2015] which reveals the importance of deep learning methods in genetic study of disease while using different types of machine-learning approaches would enable us to precise annotation mechanism. These techniques have been done for a wide variety of disease including different cancers which has led to important achievements in mutation-driven splicing. t reach to this goal, various intronic and exonic disease mutations have taken into account to detect variants of mutations. This procedure should enable us to prognosis, diagnosis, and/or control a wide variety of diseases. <br />
<br />
<br />
<br />
<br />
'''<br />
== Introduction ==<br />
'''<br />
It has been a while since whole-genome sequencing been used to detect the source of disease or unwanted malignancies genetically. The idea is to find a hierarchy of mutations tending to such diseases by looking at alterations via genetic variations in the genome and particularly when they occur outside of those domains in which protein-coding happens. In the present paper, a computational method is given to detect those genetic variants which influence RNA splicing. RNA splicing is a modification of pre-messenger RNA (pre-mRNA) when introns are removed and makes the exons joined. Any type of interruptions on this important step of gene expression would lead to various kind of disease such as cancers and neurological disorders.<br />
<br />
<br />
'''<br />
== Materials and Methods ==<br />
'''<br />
<br />
The human splicing regulatory model is analyzed by Baysian machine learning method. 10,698 cassette exons has considered in this study as a training case. The goal is to maximize an information-theoretic code quality measure <math>CQ=\sum_e \sum_t D_{KL} (q_{t,e} | r_t ) - D_{KL} (q_{t,e} | p_{t,e} ) </math><br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
'''<br />
== Genome-wide Analysis ==<br />
'''<br />
<br />
<br />
<br />
'''<br />
== Spinal Muscular Atropy ==<br />
'''<br />
<br />
<br />
<br />
'''<br />
== Rationale ==<br />
'''<br />
<br />
<br />
<br />
'''<br />
== Conclusion ==<br />
'''<br />
<br />
<br />
<br />
<br />
'''<br />
== References ==<br />
'''<br />
<br />
[1] Hui Y. Xiong1 ''et al'', The human splicing code reveals new insights into the genetic determinants of disease, Science '''347''', 2015.<br />
<br />
[2]</div>Ali.MSHhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=genetics&diff=26306genetics2015-11-16T22:36:22Z<p>Ali.MSH: </p>
<hr />
<div>'''<br />
== Genetic Application of Deep Learning ==<br />
'''<br />
This paper presentation is based on the paper [Hui Y. Xiong1 ''et al'', Science '''347''', 2015] which reveals the importance of deep learning methods in genetic study of disease while using different types of machine-learning approaches would enable us to precise annotation mechanism. These techniques have been done for a wide variety of disease including different cancers which has led to important achievements in mutation-driven splicing. t reach to this goal, various intronic and exonic disease mutations have taken into account to detect variants of mutations. This procedure should enable us to prognosis, diagnosis, and/or control a wide variety of diseases. <br />
<br />
<br />
<br />
<br />
'''<br />
== Introduction ==<br />
'''<br />
It has been a while since whole-genome sequencing been used to detect the source of disease or unwanted malignancies genetically. The idea is to find a hierarchy of mutations tending to such diseases by looking at alterations via genetic variations in the genome and particularly when they occur outside of those domains in which protein-coding happens. In the present paper, a computational method is given to detect those genetic variants which influence RNA splicing. RNA splicing is a modification of pre-messenger RNA (pre-mRNA) when introns are removed and makes the exons joined. Any type of interruptions on this important step of gene expression would lead to various kind of disease such as cancers and neurological disorders.<br />
<br />
<br />
'''<br />
== Materials and Methods ==<br />
'''<br />
<br />
[[File:Example.jpg]]<br />
<br />
'''<br />
== Genome-wide Analysis ==<br />
'''<br />
<br />
<br />
<br />
'''<br />
== Spinal Muscular Atropy ==<br />
'''<br />
<br />
<br />
<br />
'''<br />
== Rationale ==<br />
'''<br />
<br />
<br />
<br />
'''<br />
== Conclusion ==<br />
'''<br />
<br />
<br />
<br />
<br />
'''<br />
== References ==<br />
'''<br />
<br />
[1] Hui Y. Xiong1 ''et al'', The human splicing code reveals new insights into the genetic determinants of disease, Science '''347''', 2015.<br />
<br />
[2]</div>Ali.MSHhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=genetics&diff=26305genetics2015-11-16T22:35:32Z<p>Ali.MSH: </p>
<hr />
<div>'''<br />
== Genetic Application of Deep Learning ==<br />
'''<br />
This paper presentation is based on the paper [Hui Y. Xiong1 ''et al'', Science '''347''', 2015] which reveals the importance of deep learning methods in genetic study of disease while using different types of machine-learning approaches would enable us to precise annotation mechanism. These techniques have been done for a wide variety of disease including different cancers which has led to important achievements in mutation-driven splicing. t reach to this goal, various intronic and exonic disease mutations have taken into account to detect variants of mutations. This procedure should enable us to prognosis, diagnosis, and/or control a wide variety of diseases. <br />
<br />
<br />
<br />
<br />
'''<br />
== Introduction ==<br />
'''<br />
It has been a while since whole-genome sequencing been used to detect the source of disease or unwanted malignancies genetically. The idea is to find a hierarchy of mutations tending to such diseases by looking at alterations via genetic variations in the genome and particularly when they occur outside of those domains in which protein-coding happens. In the present paper, a computational method is given to detect those genetic variants which influence RNA splicing. RNA splicing is a modification of pre-messenger RNA (pre-mRNA) when introns are removed and makes the exons joined. Any type of intruptions on this important step of gene expression would lead to various kind of disease such as cancers and neurological disorders.<br />
<br />
<br />
'''<br />
== Materials and Methods ==<br />
'''<br />
<br />
[[File:Example.jpg]]<br />
<br />
'''<br />
== Genome-wide Analysis ==<br />
'''<br />
<br />
<br />
<br />
'''<br />
== Spinal Muscular Atropy ==<br />
'''<br />
<br />
<br />
<br />
'''<br />
== Rationale ==<br />
'''<br />
<br />
<br />
<br />
'''<br />
== Conclusion ==<br />
'''<br />
<br />
<br />
<br />
<br />
'''<br />
== References ==<br />
'''<br />
<br />
[1] Hui Y. Xiong1 ''et al'', The human splicing code reveals new insights into the genetic determinants of disease, Science '''347''', 2015.<br />
<br />
[2]</div>Ali.MSHhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=genetics&diff=26284genetics2015-11-16T18:05:36Z<p>Ali.MSH: </p>
<hr />
<div>'''<br />
== Genetic Application of Deep Learning ==<br />
'''<br />
This paper presentation is based on the paper [Hui Y. Xiong1 ''et al'', Science '''347''', 2015] which reveals the importance of deep learning methods in genetic study of disease while using different types of machine-learning approaches would enable us to precise annotation mechanism. These techniques have been done for a wide variety of disease including different cancers which has led to important achievements in mutation-driven splicing. t reach to this goal, various intronic and exonic disease mutations have taken into account to detect variants of mutations. This procedure should enable us to prognosis, diagnosis, and/or control a wide variety of diseases. <br />
<br />
<br />
<br />
<br />
'''<br />
== Introduction ==<br />
'''<br />
<br />
<br />
<br />
<br />
'''<br />
== Materials and Methods ==<br />
'''<br />
<br />
[[File:Example.jpg]]<br />
<br />
'''<br />
== Genome-wide Analysis ==<br />
'''<br />
<br />
<br />
<br />
'''<br />
== Spinal Muscular Atropy ==<br />
'''<br />
<br />
<br />
<br />
'''<br />
== Rationale ==<br />
'''<br />
<br />
<br />
<br />
'''<br />
== Conclusion ==<br />
'''<br />
<br />
<br />
<br />
<br />
<br />
<ref><br />
Hui Y. Xiong1 ''et al'', The human splicing code reveals new insights into the genetic determinants of disease, Science '''347''', 2015.<br />
</ref></div>Ali.MSHhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=genetics&diff=26283genetics2015-11-16T17:59:00Z<p>Ali.MSH: </p>
<hr />
<div>'''<br />
== Genetic Application of Deep Learning ==<br />
'''<br />
This paper presentation is based on the paper [Hui Y. Xiong1 ''et al'', Science '''347''', 2015] which reveals the importance of deep learning methods in genetic study of disease while using different types of machine-learning approaches would enable us to precise annotation mechanism. These techniques have been done for a wide variety of disease including different cancers which has led to important achievements in mutation-driven splicing. t reach to this goal, various intronic and exonic disease mutations have taken into account to detect the intronic and exonic variants of mutations. This procedure should enable us to prognosis, diagnosis, and/or control many diseases. <br />
<br />
<br />
<br />
<br />
'''<br />
== Introduction ==<br />
'''<br />
<br />
[[File:[[Media:file:///C:/Users/Mahdipour/Desktop/Stat1.jpg]]]]<br />
<br />
'''<br />
== Materials and Methods ==<br />
'''<br />
<br />
[[File:Example.jpg]]<br />
<br />
'''<br />
== Genome-wide Analysis ==<br />
'''<br />
<br />
<br />
<br />
'''<br />
== Spinal Muscular Atropy ==<br />
'''<br />
<br />
<br />
<br />
'''<br />
== Rationale ==<br />
'''<br />
<br />
<br />
<br />
'''<br />
== Conclusion ==<br />
'''<br />
<br />
<br />
<br />
<br />
<br />
<ref><br />
Hui Y. Xiong1 ''et al'', The human splicing code reveals new insights into the genetic determinants of disease, Science '''347''', 2015.<br />
</ref></div>Ali.MSHhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=genetics&diff=26282genetics2015-11-16T17:47:19Z<p>Ali.MSH: </p>
<hr />
<div>'''<br />
== Genetic Application of Deep Learning ==<br />
'''<br />
Hui Y. Xiong1 ''et al'', The human splicing code reveals new insights into the genetic determinants of disease, Science '''347''', 2015.<br />
<br />
<br />
<br />
<br />
'''<br />
== Introduction ==<br />
'''<br />
<br />
[[File:[[Media:file:///C:/Users/Mahdipour/Desktop/Stat1.jpg]]]]<br />
<br />
'''<br />
== Materials and Methods ==<br />
'''<br />
<br />
[[File:Example.jpg]]<br />
<br />
'''<br />
== Genome-wide Analysis ==<br />
'''<br />
<br />
<br />
<br />
'''<br />
== Spinal Muscular Atropy ==<br />
'''<br />
<br />
<br />
<br />
'''<br />
== Rationale ==<br />
'''<br />
<br />
<br />
<br />
'''<br />
== Conclusion ==<br />
'''<br />
<br />
<br />
<br />
<br />
<br />
<ref><br />
Reference<br />
</ref><br />
<br />
[1] Hui Y. Xiong1 ''et al'', The human splicing code reveals new insights into the genetic determinants of disease, Science '''347''', 2015.</div>Ali.MSHhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=genetics&diff=26280genetics2015-11-16T17:39:20Z<p>Ali.MSH: </p>
<hr />
<div>'''<br />
== Genetic Application of Deep Learning ==<br />
'''<br />
Hui Y. Xiong1 ''et al'', The human splicing code reveals new insights into the genetic determinants of disease, Science '''347''', 2015.<br />
<br />
<br />
<br />
<br />
'''<br />
== Introduction ==<br />
'''<br />
<br />
[[File:file:///C:/Users/Mahdipour/Desktop/Stat1.jpg]]<br />
<br />
'''<br />
== Materials and Methods ==<br />
'''<br />
<br />
<br />
<br />
'''<br />
== Genome-wide Analysis ==<br />
'''<br />
<br />
<br />
<br />
'''<br />
== Spinal Muscular Atropy ==<br />
'''<br />
<br />
<br />
<br />
'''<br />
== Rationale ==<br />
'''<br />
<br />
<br />
<br />
'''<br />
== Conclusion ==<br />
'''<br />
[[File:Example.jpg]]</div>Ali.MSHhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=genetics&diff=26279genetics2015-11-16T17:34:36Z<p>Ali.MSH: </p>
<hr />
<div>'''<br />
== Genetic Application of Deep Learning ==<br />
'''<br />
Hui Y. Xiong1 ''et al'', The human splicing code reveals new insights into the genetic determinants of disease, Science '''347''', 2015.<br />
<br />
<br />
<br />
<br />
'''<br />
== Introduction ==<br />
'''<br />
<br />
[[File:Stat1.jpg<br />
<br />
'''<br />
== Materials and Methods ==<br />
'''<br />
<br />
<br />
<br />
'''<br />
== Genome-wide Analysis ==<br />
'''<br />
<br />
<br />
<br />
'''<br />
== Spinal Muscular Atropy ==<br />
'''<br />
<br />
<br />
<br />
'''<br />
== Rationale ==<br />
'''<br />
<br />
<br />
<br />
'''<br />
== Conclusion ==<br />
'''</div>Ali.MSHhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=genetics&diff=26278genetics2015-11-16T17:32:27Z<p>Ali.MSH: </p>
<hr />
<div>'''<br />
== Genetic Application of Deep Learning ==<br />
'''<br />
Hui Y. Xiong1 ''et al'', The human splicing code reveals new insights into the genetic determinants of disease, Science '''347''', 2015.<br />
<br />
<br />
<br />
<br />
'''<br />
== Introduction ==<br />
'''<br />
<br />
[[File:Example.jpg]]<br />
<br />
<br />
'''<br />
== Materials and Methods ==<br />
'''<br />
<br />
<br />
<br />
'''<br />
== Genome-wide Analysis ==<br />
'''<br />
<br />
<br />
<br />
'''<br />
== Spinal Muscular Atropy ==<br />
'''<br />
<br />
<br />
<br />
'''<br />
== Rationale ==<br />
'''<br />
<br />
<br />
<br />
'''<br />
== Conclusion ==<br />
'''<br />
<br />
<gallery><br />
Image:Example.jpg|Caption1<br />
Image:Example.jpg|Caption2<br />
</gallery></div>Ali.MSHhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=f15Stat946PaperSignUp&diff=26277f15Stat946PaperSignUp2015-11-16T17:03:35Z<p>Ali.MSH: </p>
<hr />
<div> <br />
=[https://uwaterloo.ca/data-science/sites/ca.data-science/files/uploads/files/listofpapers1.pdf List of Papers]=<br />
<br />
= Record your contributions [https://docs.google.com/spreadsheets/d/1A_0ej3S6ns3bBMwWLS4pwA6zDLz_0Ivwujj-d1Gr9eo/edit?usp=sharing here:]=<br />
<br />
Use the following notations:<br />
<br />
S: You have written a summary on the paper<br />
<br />
T: You had technical contribution on a paper (excluding the paper that you present from set A or critique from set B)<br />
<br />
E: You had editorial contribution on a paper (excluding the paper that you present from set A or critique from set B)<br />
<br />
[http://goo.gl/forms/RASFRZXoxJ Your feedback on presentations]<br />
<br />
<br />
=Set A=<br />
{| class="wikitable"<br />
<br />
{| border="1" cellpadding="3"<br />
|-<br />
|width="60pt"|Date<br />
|width="100pt"|Name <br />
|width="30pt"|Paper number <br />
|width="400pt"|Title<br />
|width="30pt"|Link to the paper<br />
|width="30pt"|Link to the summary<br />
|-<br />
|Oct 16 || pascal poupart || || Guest Lecturer||||<br />
|-<br />
|Oct 16 ||pascal poupart || ||Guest Lecturer ||||<br />
|-<br />
|Oct 23 || Ali Ghodsi || || Lecturer||||<br />
|-<br />
|Oct 23 || Ali Ghodsi || || Lecturer||||<br />
|-<br />
|Oct 23 ||Ri Wang || ||Sequence to sequence learning with neural networks.||[http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf Paper] || [http://wikicoursenote.com/wiki/Stat946f15/Sequence_to_sequence_learning_with_neural_networks#Long_Short-Term_Memory_Recurrent_Neural_Network Summary]<br />
|-<br />
|Oct 23 || Deepak Rishi || || Parsing natural scenes and natural language with recursive neural networks || [http://www-nlp.stanford.edu/pubs/SocherLinNgManning_ICML2011.pdf Paper] || [[Parsing natural scenes and natural language with recursive neural networks | Summary]]<br />
|-<br />
|Oct 30 || Ali Ghodsi || || Lecturer||||<br />
|-<br />
|Oct 30 || Ali Ghodsi || || Lecturer||||<br />
|-<br />
|Oct 30 ||Rui Qiao || ||Going deeper with convolutions || [http://arxiv.org/pdf/1409.4842v1.pdf Paper]|| [[GoingDeeperWithConvolutions|Summary]]<br />
|-<br />
|Oct 30 ||Amirreza Lashkari|| 21 ||Overfeat: integrated recognition, localization and detection using convolutional networks. || [http://arxiv.org/pdf/1312.6229v4.pdf Paper]|| [[Overfeat: integrated recognition, localization and detection using convolutional networks|Summary]]<br />
|-<br />
|Mkeup Class (TBA) || Peter Blouw|| ||Memory Networks.|| [http://arxiv.org/abs/1410.3916]|| [[Memory Networks|Summary]]<br />
|-<br />
|Nov 6 || Ali Ghodsi || || Lecturer||||<br />
|-<br />
|Nov 6 || Ali Ghodsi || || Lecturer||||<br />
|-<br />
|Nov 6 || Anthony Caterini ||56 || Human-level control through deep reinforcement learning ||[http://www.nature.com/nature/journal/v518/n7540/pdf/nature14236.pdf Paper]|| [[Human-level control through deep reinforcement learning|Summary]]<br />
|-<br />
|Nov 6 || Sean Aubin || ||Learning Hierarchical Features for Scene Labeling ||[http://yann.lecun.com/exdb/publis/pdf/farabet-pami-13.pdf Paper]||[[Learning Hierarchical Features for Scene Labeling|Summary]]<br />
|-<br />
|Nov 13|| Mike Hynes || 12 ||Speech recognition with deep recurrent neural networks || [http://www.cs.toronto.edu/~fritz/absps/RNN13.pdf Paper] || [[Graves et al., Speech recognition with deep recurrent neural networks|Summary]]<br />
|-<br />
|Nov 13 || Tim Tse || || Question Answering with Subgraph Embeddings || [http://arxiv.org/pdf/1406.3676v3.pdf Paper] || [[Question Answering with Subgraph Embeddings | Summary ]]<br />
|-<br />
|Nov 13 || Maysum Panju || ||Neural machine translation by jointly learning to align and translate ||[http://arxiv.org/pdf/1409.0473v6.pdf Paper] || [[Neural Machine Translation: Jointly Learning to Align and Translate|Summary]]<br />
|-<br />
|Nov 13 || Abdullah Rashwan || || Deep neural networks for acoustic modeling in speech recognition. ||[http://research.microsoft.com/pubs/171498/HintonDengYuEtAl-SPM2012.pdf paper]|| [[Deep neural networks for acoustic modeling in speech recognition| Summary]]<br />
|-<br />
|Nov 20 || Valerie Platsko || ||Natural language processing (almost) from scratch. ||[http://arxiv.org/pdf/1103.0398.pdf Paper]|| [[Natural language processing (almost) from scratch. | Summary]]<br />
|-<br />
|Nov 20 || Brent Komer || ||Show, Attend and Tell: Neural Image Caption Generation with Visual Attention || [http://arxiv.org/pdf/1502.03044v2.pdf Paper]||[[Show, Attend and Tell: Neural Image Caption Generation with Visual Attention|Summary]]<br />
|-<br />
|Nov 20 || Luyao Ruan || || Dropout: A Simple Way to Prevent Neural Networks from Overfitting || [https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf Paper]|| [[dropout | Summary]]<br />
|-<br />
|Nov 20 || Ali Mahdipour || || The human splicing code reveals new insights into the genetic determinants of disease ||[https://www.sciencemag.org/content/347/6218/1254806.full.pdf Paper] || [[Genetics | Summary]]<br />
|-<br />
|Nov 27 ||Mahmood Gohari || ||Deep Neural Nets as a Method for Quantitative Structure–Activity Relationships ||[http://pubs.acs.org/doi/abs/10.1021/ci500747n.pdf Paper]||<br />
|-<br />
|Nov 27 || Derek Latremouille || ||The Wake-Sleep Algorithm for Unsupervised Neural Networks || [http://www.gatsby.ucl.ac.uk/~dayan/papers/hdfn95.pdf Paper] ||<br />
|-<br />
|Nov 27 ||Xinran Liu || ||ImageNet Classification with Deep Convolutional Neural Networks ||[http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf Paper]||[[ImageNet Classification with Deep Convolutional Neural Networks|Summary]]<br />
|-<br />
|Nov 27 ||Ali Sarhadi|| ||Strategies for Training Large Scale Neural Network Language Models||||<br />
|-<br />
|Dec 4 || Chris Choi || || On the difficulty of training recurrent neural networks || [http://www.jmlr.org/proceedings/papers/v28/pascanu13.pdf Paper] || [[On the difficulty of training recurrent neural networks | Summary]]<br />
|-<br />
|Dec 4 || Fatemeh Karimi || ||MULTIPLE OBJECT RECOGNITION WITH VISUAL ATTENTION||[http://arxiv.org/pdf/1412.7755v2.pdf Paper]||<br />
|-<br />
|Dec 4 || Jan Gosmann || || On the Number of Linear Regions of Deep Neural Networks || [http://arxiv.org/abs/1402.1869 Paper] || [[On the Number of Linear Regions of Deep Neural Networks | Summary]]<br />
|-<br />
|Dec 4 || Dylan Drover || || Towards AI-complete question answering: a set of prerequisite toy tasks || [http://arxiv.org/pdf/1502.05698.pdf Paper] ||<br />
|-<br />
|}<br />
|}<br />
<br />
=Set B=<br />
<br />
{| class="wikitable"<br />
<br />
{| border="1" cellpadding="3"<br />
|-<br />
|width="100pt"|Name <br />
|width="30pt"|Paper number <br />
|width="400pt"|Title<br />
|width="30pt"|Link to the paper<br />
|width="30pt"|Link to the summary<br />
|-<br />
|Anthony Caterini ||15 ||The Manifold Tangent Classifier ||[http://papers.nips.cc/paper/4409-the-manifold-tangent-classifier.pdf Paper]||<br />
|-<br />
|Jan Gosmann || || Neural Turing machines || [http://arxiv.org/abs/1410.5401 Paper] || [[Neural Turing Machines|Summary]]<br />
|-<br />
|Brent Komer || || Scene Parsing with Multiscale Feature Learning, Purity Trees, and Optimal Covers || [http://arxiv.org/pdf/1202.2160v2.pdf Paper] || [[Scene Parsing with Multiscale Feature Learning, Purity Trees, and Optimal Covers Machines|Summary]]<br />
|-<br />
|Sean Aubin || || Deep Sparse Rectifier Neural Networks || [http://jmlr.csail.mit.edu/proceedings/papers/v15/glorot11a/glorot11a.pdf Paper] || [[Deep Sparse Rectifier Neural Networks|Summary]]<br />
|-<br />
|Peter Blouw|| || Generating text with recurrent neural networks || [http://www.cs.utoronto.ca/~ilya/pubs/2011/LANG-RNN.pdf Paper] ||<br />
|-<br />
|Tim Tse|| || From Machine Learning to Machine Reasoning || [http://research.microsoft.com/pubs/206768/mlj-2013.pdf Paper] || [[From Machine Learning to Machine Reasoning | Summary ]]<br />
|-<br />
|Rui Qiao|| || Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation || [http://arxiv.org/pdf/1406.1078v3.pdf Paper] || [[Learning Phrase Representations|Summary]]<br />
|-<br />
|Ftemeh Karimi|| 23 || Very Deep Convoloutional Networks for Large-Scale Image Recognition || [http://arxiv.org/pdf/1409.1556.pdf Paper] || [[Very Deep Convoloutional Networks for Large-Scale Image Recognition|Summary]]<br />
|-<br />
|Amirreza Lashkari|| 43 || Distributed Representations of Words and Phrases and their Compositionality || [http://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf Paper] || [[Distributed Representations of Words and Phrases and their Compositionality|Summary]]<br />
|-<br />
|Xinran Liu|| 19 || Joint training of a convolutional network and a graphical model for human pose estimation || [http://papers.nips.cc/paper/5573-joint-training-of-a-convolutional-network-and-a-graphical-model-for-human-pose-estimation.pdf Paper] || [[Joint training of a convolutional network and a graphical model for human pose estimation|Summary]]<br />
|-<br />
|Chris Choi|| || Learning Long-Range Vision for Autonomous Off-Road Driving || [http://yann.lecun.com/exdb/publis/pdf/hadsell-jfr-09.pdf Paper] || [[Learning Long-Range Vision for Autonomous Off-Road Driving|Summary]]<br />
|-<br />
|Luyao Ruan|| || Deep Learning of the tissue-regulated splicing code || [http://bioinformatics.oxfordjournals.org/content/30/12/i121.full.pdf+html Paper] || [[Deep Learning of the tissue-regulated splicing code| Summary]]<br />
|-<br />
|Abdullah Rashwan|| || Deep Convolutional Neural Networks For LVCSR || [http://www.cs.toronto.edu/~asamir/papers/icassp13_cnn.pdf paper] || [[Deep Convolutional Neural Networks For LVCSR| Summary]]<br />
|-<br />
|Mahmood Gohari||37 || On using very large target vocabulary for neural machine translation || [http://arxiv.org/pdf/1412.2007v2.pdf paper] || [[On using very large target vocabulary for neural machine translation| Summary]]<br />
|-<br />
|Valerie Platsko|| || Learning Convolutional Feature Hierarchies for Visual Recognition || [http://papers.nips.cc/paper/4133-learning-convolutional-feature-hierarchies-for-visual-recognition Paper] || [[Learning Convolutional Feature Hierarchies for Visual Recognition | Summary]]<br />
|-<br />
|Derek Latremouille|| || Learning fast approximations of sparse coding || [http://yann.lecun.com/exdb/publis/pdf/gregor-icml-10.pdf Paper] || [[Learning fast approximations of sparse coding | Summary]]<br />
|-<br />
|Ri Wang|| || Continuous space language models || [https://wiki.inf.ed.ac.uk/twiki/pub/CSTR/ListenSemester2_2009_10/sdarticle.pdf Paper] || [[Continuous space language models | Summary]]</div>Ali.MSHhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=f15Stat946PaperSignUp&diff=26276f15Stat946PaperSignUp2015-11-16T17:02:19Z<p>Ali.MSH: </p>
<hr />
<div> <br />
=[https://uwaterloo.ca/data-science/sites/ca.data-science/files/uploads/files/listofpapers1.pdf List of Papers]=<br />
<br />
= Record your contributions [https://docs.google.com/spreadsheets/d/1A_0ej3S6ns3bBMwWLS4pwA6zDLz_0Ivwujj-d1Gr9eo/edit?usp=sharing here:]=<br />
<br />
Use the following notations:<br />
<br />
S: You have written a summary on the paper<br />
<br />
T: You had technical contribution on a paper (excluding the paper that you present from set A or critique from set B)<br />
<br />
E: You had editorial contribution on a paper (excluding the paper that you present from set A or critique from set B)<br />
<br />
[http://goo.gl/forms/RASFRZXoxJ Your feedback on presentations]<br />
<br />
<br />
=Set A=<br />
{| class="wikitable"<br />
<br />
{| border="1" cellpadding="3"<br />
|-<br />
|width="60pt"|Date<br />
|width="100pt"|Name <br />
|width="30pt"|Paper number <br />
|width="400pt"|Title<br />
|width="30pt"|Link to the paper<br />
|width="30pt"|Link to the summary<br />
|-<br />
|Oct 16 || pascal poupart || || Guest Lecturer||||<br />
|-<br />
|Oct 16 ||pascal poupart || ||Guest Lecturer ||||<br />
|-<br />
|Oct 23 || Ali Ghodsi || || Lecturer||||<br />
|-<br />
|Oct 23 || Ali Ghodsi || || Lecturer||||<br />
|-<br />
|Oct 23 ||Ri Wang || ||Sequence to sequence learning with neural networks.||[http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf Paper] || [http://wikicoursenote.com/wiki/Stat946f15/Sequence_to_sequence_learning_with_neural_networks#Long_Short-Term_Memory_Recurrent_Neural_Network Summary]<br />
|-<br />
|Oct 23 || Deepak Rishi || || Parsing natural scenes and natural language with recursive neural networks || [http://www-nlp.stanford.edu/pubs/SocherLinNgManning_ICML2011.pdf Paper] || [[Parsing natural scenes and natural language with recursive neural networks | Summary]]<br />
|-<br />
|Oct 30 || Ali Ghodsi || || Lecturer||||<br />
|-<br />
|Oct 30 || Ali Ghodsi || || Lecturer||||<br />
|-<br />
|Oct 30 ||Rui Qiao || ||Going deeper with convolutions || [http://arxiv.org/pdf/1409.4842v1.pdf Paper]|| [[GoingDeeperWithConvolutions|Summary]]<br />
|-<br />
|Oct 30 ||Amirreza Lashkari|| 21 ||Overfeat: integrated recognition, localization and detection using convolutional networks. || [http://arxiv.org/pdf/1312.6229v4.pdf Paper]|| [[Overfeat: integrated recognition, localization and detection using convolutional networks|Summary]]<br />
|-<br />
|Mkeup Class (TBA) || Peter Blouw|| ||Memory Networks.|| [http://arxiv.org/abs/1410.3916]|| [[Memory Networks|Summary]]<br />
|-<br />
|Nov 6 || Ali Ghodsi || || Lecturer||||<br />
|-<br />
|Nov 6 || Ali Ghodsi || || Lecturer||||<br />
|-<br />
|Nov 6 || Anthony Caterini ||56 || Human-level control through deep reinforcement learning ||[http://www.nature.com/nature/journal/v518/n7540/pdf/nature14236.pdf Paper]|| [[Human-level control through deep reinforcement learning|Summary]]<br />
|-<br />
|Nov 6 || Sean Aubin || ||Learning Hierarchical Features for Scene Labeling ||[http://yann.lecun.com/exdb/publis/pdf/farabet-pami-13.pdf Paper]||[[Learning Hierarchical Features for Scene Labeling|Summary]]<br />
|-<br />
|Nov 13|| Mike Hynes || 12 ||Speech recognition with deep recurrent neural networks || [http://www.cs.toronto.edu/~fritz/absps/RNN13.pdf Paper] || [[Graves et al., Speech recognition with deep recurrent neural networks|Summary]]<br />
|-<br />
|Nov 13 || Tim Tse || || Question Answering with Subgraph Embeddings || [http://arxiv.org/pdf/1406.3676v3.pdf Paper] || [[Question Answering with Subgraph Embeddings | Summary ]]<br />
|-<br />
|Nov 13 || Maysum Panju || ||Neural machine translation by jointly learning to align and translate ||[http://arxiv.org/pdf/1409.0473v6.pdf Paper] || [[Neural Machine Translation: Jointly Learning to Align and Translate|Summary]]<br />
|-<br />
|Nov 13 || Abdullah Rashwan || || Deep neural networks for acoustic modeling in speech recognition. ||[http://research.microsoft.com/pubs/171498/HintonDengYuEtAl-SPM2012.pdf paper]|| [[Deep neural networks for acoustic modeling in speech recognition| Summary]]<br />
|-<br />
|Nov 20 || Valerie Platsko || ||Natural language processing (almost) from scratch. ||[http://arxiv.org/pdf/1103.0398.pdf Paper]|| [[Natural language processing (almost) from scratch. | Summary]]<br />
|-<br />
|Nov 20 || Brent Komer || ||Show, Attend and Tell: Neural Image Caption Generation with Visual Attention || [http://arxiv.org/pdf/1502.03044v2.pdf Paper]||[[Show, Attend and Tell: Neural Image Caption Generation with Visual Attention|Summary]]<br />
|-<br />
|Nov 20 || Luyao Ruan || || Dropout: A Simple Way to Prevent Neural Networks from Overfitting || [https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf Paper]|| [[dropout | Summary]]<br />
|-<br />
|Nov 20 || Ali Mahdipour || || The human splicing code reveals new insights into the genetic determinants of disease ||[https://www.sciencemag.org/content/347/6218/1254806.full.pdf Paper] || [[Genetic Application of Deep Learning | Summary]]<br />
|-<br />
|Nov 27 ||Mahmood Gohari || ||Deep Neural Nets as a Method for Quantitative Structure–Activity Relationships ||[http://pubs.acs.org/doi/abs/10.1021/ci500747n.pdf Paper]||<br />
|-<br />
|Nov 27 || Derek Latremouille || ||The Wake-Sleep Algorithm for Unsupervised Neural Networks || [http://www.gatsby.ucl.ac.uk/~dayan/papers/hdfn95.pdf Paper] ||<br />
|-<br />
|Nov 27 ||Xinran Liu || ||ImageNet Classification with Deep Convolutional Neural Networks ||[http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf Paper]||[[ImageNet Classification with Deep Convolutional Neural Networks|Summary]]<br />
|-<br />
|Nov 27 ||Ali Sarhadi|| ||Strategies for Training Large Scale Neural Network Language Models||||<br />
|-<br />
|Dec 4 || Chris Choi || || On the difficulty of training recurrent neural networks || [http://www.jmlr.org/proceedings/papers/v28/pascanu13.pdf Paper] || [[On the difficulty of training recurrent neural networks | Summary]]<br />
|-<br />
|Dec 4 || Fatemeh Karimi || ||MULTIPLE OBJECT RECOGNITION WITH VISUAL ATTENTION||[http://arxiv.org/pdf/1412.7755v2.pdf Paper]||<br />
|-<br />
|Dec 4 || Jan Gosmann || || On the Number of Linear Regions of Deep Neural Networks || [http://arxiv.org/abs/1402.1869 Paper] || [[On the Number of Linear Regions of Deep Neural Networks | Summary]]<br />
|-<br />
|Dec 4 || Dylan Drover || || Towards AI-complete question answering: a set of prerequisite toy tasks || [http://arxiv.org/pdf/1502.05698.pdf Paper] ||<br />
|-<br />
|}<br />
|}<br />
<br />
=Set B=<br />
<br />
{| class="wikitable"<br />
<br />
{| border="1" cellpadding="3"<br />
|-<br />
|width="100pt"|Name <br />
|width="30pt"|Paper number <br />
|width="400pt"|Title<br />
|width="30pt"|Link to the paper<br />
|width="30pt"|Link to the summary<br />
|-<br />
|Anthony Caterini ||15 ||The Manifold Tangent Classifier ||[http://papers.nips.cc/paper/4409-the-manifold-tangent-classifier.pdf Paper]||<br />
|-<br />
|Jan Gosmann || || Neural Turing machines || [http://arxiv.org/abs/1410.5401 Paper] || [[Neural Turing Machines|Summary]]<br />
|-<br />
|Brent Komer || || Scene Parsing with Multiscale Feature Learning, Purity Trees, and Optimal Covers || [http://arxiv.org/pdf/1202.2160v2.pdf Paper] || [[Scene Parsing with Multiscale Feature Learning, Purity Trees, and Optimal Covers Machines|Summary]]<br />
|-<br />
|Sean Aubin || || Deep Sparse Rectifier Neural Networks || [http://jmlr.csail.mit.edu/proceedings/papers/v15/glorot11a/glorot11a.pdf Paper] || [[Deep Sparse Rectifier Neural Networks|Summary]]<br />
|-<br />
|Peter Blouw|| || Generating text with recurrent neural networks || [http://www.cs.utoronto.ca/~ilya/pubs/2011/LANG-RNN.pdf Paper] ||<br />
|-<br />
|Tim Tse|| || From Machine Learning to Machine Reasoning || [http://research.microsoft.com/pubs/206768/mlj-2013.pdf Paper] || [[From Machine Learning to Machine Reasoning | Summary ]]<br />
|-<br />
|Rui Qiao|| || Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation || [http://arxiv.org/pdf/1406.1078v3.pdf Paper] || [[Learning Phrase Representations|Summary]]<br />
|-<br />
|Ftemeh Karimi|| 23 || Very Deep Convoloutional Networks for Large-Scale Image Recognition || [http://arxiv.org/pdf/1409.1556.pdf Paper] || [[Very Deep Convoloutional Networks for Large-Scale Image Recognition|Summary]]<br />
|-<br />
|Amirreza Lashkari|| 43 || Distributed Representations of Words and Phrases and their Compositionality || [http://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf Paper] || [[Distributed Representations of Words and Phrases and their Compositionality|Summary]]<br />
|-<br />
|Xinran Liu|| 19 || Joint training of a convolutional network and a graphical model for human pose estimation || [http://papers.nips.cc/paper/5573-joint-training-of-a-convolutional-network-and-a-graphical-model-for-human-pose-estimation.pdf Paper] || [[Joint training of a convolutional network and a graphical model for human pose estimation|Summary]]<br />
|-<br />
|Chris Choi|| || Learning Long-Range Vision for Autonomous Off-Road Driving || [http://yann.lecun.com/exdb/publis/pdf/hadsell-jfr-09.pdf Paper] || [[Learning Long-Range Vision for Autonomous Off-Road Driving|Summary]]<br />
|-<br />
|Luyao Ruan|| || Deep Learning of the tissue-regulated splicing code || [http://bioinformatics.oxfordjournals.org/content/30/12/i121.full.pdf+html Paper] || [[Deep Learning of the tissue-regulated splicing code| Summary]]<br />
|-<br />
|Abdullah Rashwan|| || Deep Convolutional Neural Networks For LVCSR || [http://www.cs.toronto.edu/~asamir/papers/icassp13_cnn.pdf paper] || [[Deep Convolutional Neural Networks For LVCSR| Summary]]<br />
|-<br />
|Mahmood Gohari||37 || On using very large target vocabulary for neural machine translation || [http://arxiv.org/pdf/1412.2007v2.pdf paper] || [[On using very large target vocabulary for neural machine translation| Summary]]<br />
|-<br />
|Valerie Platsko|| || Learning Convolutional Feature Hierarchies for Visual Recognition || [http://papers.nips.cc/paper/4133-learning-convolutional-feature-hierarchies-for-visual-recognition Paper] || [[Learning Convolutional Feature Hierarchies for Visual Recognition | Summary]]<br />
|-<br />
|Derek Latremouille|| || Learning fast approximations of sparse coding || [http://yann.lecun.com/exdb/publis/pdf/gregor-icml-10.pdf Paper] || [[Learning fast approximations of sparse coding | Summary]]<br />
|-<br />
|Ri Wang|| || Continuous space language models || [https://wiki.inf.ed.ac.uk/twiki/pub/CSTR/ListenSemester2_2009_10/sdarticle.pdf Paper] || [[Continuous space language models | Summary]]</div>Ali.MSHhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=genetics&diff=26245genetics2015-11-14T21:03:34Z<p>Ali.MSH: </p>
<hr />
<div><br />
'''<br />
== Overview ==<br />
'''<br />
Hui Y. Xiong1 ''et al'', The human splicing code reveals new insights into the genetic determinants of disease, Science '''347''', 2015.<br />
<br />
<br />
<br />
<br />
'''<br />
== Introduction ==<br />
'''<br />
<br />
<br />
<br />
<br />
'''<br />
== Materials and Methods ==<br />
'''<br />
<br />
<br />
<br />
'''<br />
== Genome-wide Analysis ==<br />
'''<br />
<br />
<br />
<br />
'''<br />
== Spinal Muscular Atropy ==<br />
'''<br />
<br />
<br />
<br />
'''<br />
== Rationale ==<br />
'''<br />
<br />
<br />
<br />
'''<br />
== Conclusion ==<br />
'''</div>Ali.MSHhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=genetics&diff=26244genetics2015-11-14T21:02:46Z<p>Ali.MSH: Genetic Application of Deep Learining</p>
<hr />
<div>'''<br />
== Hui Y. Xiong1 ''et al'', The human splicing code reveals new insights into the genetic determinants of disease, Scienece '''347''', 2015. ==<br />
'''<br />
<br />
<br />
<br />
<br />
<br />
'''<br />
== Overview ==<br />
'''<br />
<br />
<br />
<br />
<br />
<br />
'''<br />
== Introduction ==<br />
'''<br />
<br />
<br />
<br />
<br />
'''<br />
== Materials and Methods ==<br />
'''<br />
<br />
<br />
<br />
'''<br />
== Genome-wide Analysis ==<br />
'''<br />
<br />
<br />
<br />
'''<br />
== Spinal Muscular Atropy ==<br />
'''<br />
<br />
<br />
<br />
'''<br />
== Rationale ==<br />
'''<br />
<br />
<br />
<br />
'''<br />
== Conclusion ==<br />
'''</div>Ali.MSHhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=genetics&diff=26243genetics2015-11-14T21:02:18Z<p>Ali.MSH: Genetic Application of Deep Learining</p>
<hr />
<div><br />
'''== Hui Y. Xiong1 ''et al'', The human splicing code reveals new insights into the genetic determinants of disease, Scienece '''347''', 2015. =='''<br />
<br />
<br />
<br />
<br />
<br />
'''<br />
== Overview ==<br />
'''<br />
<br />
<br />
<br />
<br />
<br />
'''<br />
== Introduction ==<br />
'''<br />
<br />
<br />
<br />
<br />
'''<br />
== Materials and Methods ==<br />
'''<br />
<br />
<br />
<br />
'''<br />
== Genome-wide Analysis ==<br />
'''<br />
<br />
<br />
<br />
'''<br />
== Spinal Muscular Atropy ==<br />
'''<br />
<br />
<br />
<br />
'''<br />
== Rationale ==<br />
'''<br />
<br />
<br />
<br />
'''<br />
== Conclusion ==<br />
'''</div>Ali.MSHhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=genetics&diff=26242genetics2015-11-14T20:43:53Z<p>Ali.MSH: Genetic Application of Deep Learining</p>
<hr />
<div>'''<br />
== Overview ==<br />
'''<br />
<br />
<br />
<br />
<br />
<br />
'''<br />
== Introduction ==<br />
'''<br />
<br />
<br />
<br />
<br />
'''<br />
== Materials and Methods ==<br />
'''<br />
<br />
<br />
<br />
'''<br />
== Genome-wide Analysis ==<br />
'''<br />
<br />
<br />
<br />
'''<br />
== Spinal Muscular Atropy ==<br />
'''<br />
<br />
<br />
<br />
'''<br />
== Rationale ==<br />
'''<br />
<br />
<br />
<br />
'''<br />
== Conclusion ==<br />
'''</div>Ali.MSHhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=genetics&diff=26241genetics2015-11-14T20:43:01Z<p>Ali.MSH: Genetic Application of Deep Learining</p>
<hr />
<div>'''<br />
== Intorduction ==<br />
'''<br />
<br />
<br />
<br />
<br />
'''<br />
== Materials and Methods ==<br />
'''<br />
<br />
<br />
<br />
'''<br />
== Genome-wide Analysis ==<br />
'''<br />
<br />
<br />
<br />
'''<br />
== Spinal Muscular Atropy ==<br />
'''<br />
<br />
<br />
<br />
'''<br />
== Rationale ==<br />
'''<br />
<br />
<br />
<br />
'''<br />
== Conclusion ==<br />
'''</div>Ali.MSHhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=f15Stat946PaperSignUp&diff=26240f15Stat946PaperSignUp2015-11-14T20:36:43Z<p>Ali.MSH: </p>
<hr />
<div> <br />
=[https://uwaterloo.ca/data-science/sites/ca.data-science/files/uploads/files/listofpapers1.pdf List of Papers]=<br />
<br />
= Record your contributions [https://docs.google.com/spreadsheets/d/1A_0ej3S6ns3bBMwWLS4pwA6zDLz_0Ivwujj-d1Gr9eo/edit?usp=sharing here:]=<br />
<br />
Use the following notations:<br />
<br />
S: You have written a summary on the paper<br />
<br />
T: You had technical contribution on a paper (excluding the paper that you present from set A or critique from set B)<br />
<br />
E: You had editorial contribution on a paper (excluding the paper that you present from set A or critique from set B)<br />
<br />
[http://goo.gl/forms/RASFRZXoxJ Your feedback on presentations]<br />
<br />
<br />
=Set A=<br />
{| class="wikitable"<br />
<br />
{| border="1" cellpadding="3"<br />
|-<br />
|width="60pt"|Date<br />
|width="100pt"|Name <br />
|width="30pt"|Paper number <br />
|width="400pt"|Title<br />
|width="30pt"|Link to the paper<br />
|width="30pt"|Link to the summary<br />
|-<br />
|Oct 16 || pascal poupart || || Guest Lecturer||||<br />
|-<br />
|Oct 16 ||pascal poupart || ||Guest Lecturer ||||<br />
|-<br />
|Oct 23 || Ali Ghodsi || || Lecturer||||<br />
|-<br />
|Oct 23 || Ali Ghodsi || || Lecturer||||<br />
|-<br />
|Oct 23 ||Ri Wang || ||Sequence to sequence learning with neural networks.||[http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf Paper] || [http://wikicoursenote.com/wiki/Stat946f15/Sequence_to_sequence_learning_with_neural_networks#Long_Short-Term_Memory_Recurrent_Neural_Network Summary]<br />
|-<br />
|Oct 23 || Deepak Rishi || || Parsing natural scenes and natural language with recursive neural networks || [http://www-nlp.stanford.edu/pubs/SocherLinNgManning_ICML2011.pdf Paper] || [[Parsing natural scenes and natural language with recursive neural networks | Summary]]<br />
|-<br />
|Oct 30 || Ali Ghodsi || || Lecturer||||<br />
|-<br />
|Oct 30 || Ali Ghodsi || || Lecturer||||<br />
|-<br />
|Oct 30 ||Rui Qiao || ||Going deeper with convolutions || [http://arxiv.org/pdf/1409.4842v1.pdf Paper]|| [[GoingDeeperWithConvolutions|Summary]]<br />
|-<br />
|Oct 30 ||Amirreza Lashkari|| 21 ||Overfeat: integrated recognition, localization and detection using convolutional networks. || [http://arxiv.org/pdf/1312.6229v4.pdf Paper]|| [[Overfeat: integrated recognition, localization and detection using convolutional networks|Summary]]<br />
|-<br />
|Mkeup Class (TBA) || Peter Blouw|| ||Memory Networks.|| [http://arxiv.org/abs/1410.3916]|| [[Memory Networks|Summary]]<br />
|-<br />
|Nov 6 || Ali Ghodsi || || Lecturer||||<br />
|-<br />
|Nov 6 || Ali Ghodsi || || Lecturer||||<br />
|-<br />
|Nov 6 || Anthony Caterini ||56 || Human-level control through deep reinforcement learning ||[http://www.nature.com/nature/journal/v518/n7540/pdf/nature14236.pdf Paper]|| [[Human-level control through deep reinforcement learning|Summary]]<br />
|-<br />
|Nov 6 || Sean Aubin || ||Learning Hierarchical Features for Scene Labeling ||[http://yann.lecun.com/exdb/publis/pdf/farabet-pami-13.pdf Paper]||[[Learning Hierarchical Features for Scene Labeling|Summary]]<br />
|-<br />
|Nov 13|| Mike Hynes || 12 ||Speech recognition with deep recurrent neural networks || [http://www.cs.toronto.edu/~fritz/absps/RNN13.pdf Paper] || [[Graves et al., Speech recognition with deep recurrent neural networks|Summary]]<br />
|-<br />
|Nov 13 || Tim Tse || || Question Answering with Subgraph Embeddings || [http://arxiv.org/pdf/1406.3676v3.pdf Paper] || [[Question Answering with Subgraph Embeddings | Summary ]]<br />
|-<br />
|Nov 13 || Maysum Panju || ||Neural machine translation by jointly learning to align and translate ||[http://arxiv.org/pdf/1409.0473v6.pdf Paper] || [[Neural Machine Translation: Jointly Learning to Align and Translate|Summary]]<br />
|-<br />
|Nov 13 || Abdullah Rashwan || || Deep neural networks for acoustic modeling in speech recognition. ||[http://research.microsoft.com/pubs/171498/HintonDengYuEtAl-SPM2012.pdf paper]|| [[Deep neural networks for acoustic modeling in speech recognition| Summary]]<br />
|-<br />
|Nov 20 || Valerie Platsko || ||Natural language processing (almost) from scratch. ||[http://arxiv.org/pdf/1103.0398.pdf Paper]|| [[Natural language processing (almost) from scratch. | Summary]]<br />
|-<br />
|Nov 20 || Brent Komer || ||Show, Attend and Tell: Neural Image Caption Generation with Visual Attention || [http://arxiv.org/pdf/1502.03044v2.pdf Paper]||[[Show, Attend and Tell: Neural Image Caption Generation with Visual Attention|Summary]]<br />
|-<br />
|Nov 20 || Luyao Ruan || || Dropout: A Simple Way to Prevent Neural Networks from Overfitting || [https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf Paper]|| [[dropout | Summary]]<br />
|-<br />
|Nov 20 || Ali Mahdipour || || The human splicing code reveals new insights into the genetic determinants of disease ||[https://www.sciencemag.org/content/347/6218/1254806.full.pdf Paper] || [[Genetics | Summary]]<br />
|-<br />
|Nov 27 ||Mahmood Gohari || ||Deep Neural Nets as a Method for Quantitative Structure–Activity Relationships ||[http://pubs.acs.org/doi/abs/10.1021/ci500747n.pdf Paper]||<br />
|-<br />
|Nov 27 || Derek Latremouille || ||The Wake-Sleep Algorithm for Unsupervised Neural Networks || [http://www.gatsby.ucl.ac.uk/~dayan/papers/hdfn95.pdf Paper] ||<br />
|-<br />
|Nov 27 ||Xinran Liu || ||ImageNet Classification with Deep Convolutional Neural Networks ||[http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf Paper]||[[ImageNet Classification with Deep Convolutional Neural Networks|Summary]]<br />
|-<br />
|Nov 27 ||Ali Sarhadi|| ||Strategies for Training Large Scale Neural Network Language Models||||<br />
|-<br />
|Dec 4 || Chris Choi || || On the difficulty of training recurrent neural networks || [http://www.jmlr.org/proceedings/papers/v28/pascanu13.pdf Paper] || [[On the difficulty of training recurrent neural networks | Summary]]<br />
|-<br />
|Dec 4 || Fatemeh Karimi || ||MULTIPLE OBJECT RECOGNITION WITH VISUAL ATTENTION||[http://arxiv.org/pdf/1412.7755v2.pdf Paper]||<br />
|-<br />
|Dec 4 || Jan Gosmann || || On the Number of Linear Regions of Deep Neural Networks || [http://arxiv.org/abs/1402.1869 Paper] || [[On the Number of Linear Regions of Deep Neural Networks | Summary]]<br />
|-<br />
|Dec 4 || Dylan Drover || || Towards AI-complete question answering: a set of prerequisite toy tasks || [http://arxiv.org/pdf/1502.05698.pdf Paper] ||<br />
|-<br />
|}<br />
|}<br />
<br />
=Set B=<br />
<br />
{| class="wikitable"<br />
<br />
{| border="1" cellpadding="3"<br />
|-<br />
|width="100pt"|Name <br />
|width="30pt"|Paper number <br />
|width="400pt"|Title<br />
|width="30pt"|Link to the paper<br />
|width="30pt"|Link to the summary<br />
|-<br />
|Anthony Caterini ||15 ||The Manifold Tangent Classifier ||[http://papers.nips.cc/paper/4409-the-manifold-tangent-classifier.pdf Paper]||<br />
|-<br />
|Jan Gosmann || || Neural Turing machines || [http://arxiv.org/abs/1410.5401 Paper] || [[Neural Turing Machines|Summary]]<br />
|-<br />
|Brent Komer || || Scene Parsing with Multiscale Feature Learning, Purity Trees, and Optimal Covers || [http://arxiv.org/pdf/1202.2160v2.pdf Paper] ||<br />
|-<br />
|Sean Aubin || || Deep Sparse Rectifier Neural Networks || [http://jmlr.csail.mit.edu/proceedings/papers/v15/glorot11a/glorot11a.pdf Paper] || [[Deep Sparse Rectifier Neural Networks|Summary]]<br />
|-<br />
|Peter Blouw|| || Generating text with recurrent neural networks || [http://www.cs.utoronto.ca/~ilya/pubs/2011/LANG-RNN.pdf Paper] ||<br />
|-<br />
|Tim Tse|| || From Machine Learning to Machine Reasoning || [http://research.microsoft.com/pubs/206768/mlj-2013.pdf Paper] || [[From Machine Learning to Machine Reasoning | Summary ]]<br />
|-<br />
|Rui Qiao|| || Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation || [http://arxiv.org/pdf/1406.1078v3.pdf Paper] || [[Learning Phrase Representations|Summary]]<br />
|-<br />
|Ftemeh Karimi|| 23 || Very Deep Convoloutional Networks for Large-Scale Image Recognition || [http://arxiv.org/pdf/1409.1556.pdf Paper] || [[Very Deep Convoloutional Networks for Large-Scale Image Recognition|Summary]]<br />
|-<br />
|Amirreza Lashkari|| 43 || Distributed Representations of Words and Phrases and their Compositionality || [http://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf Paper] || [[Distributed Representations of Words and Phrases and their Compositionality|Summary]]<br />
|-<br />
|Xinran Liu|| 19 || Joint training of a convolutional network and a graphical model for human pose estimation || [http://papers.nips.cc/paper/5573-joint-training-of-a-convolutional-network-and-a-graphical-model-for-human-pose-estimation.pdf Paper] || [[Joint training of a convolutional network and a graphical model for human pose estimation|Summary]]<br />
|-<br />
|Chris Choi|| || Learning Long-Range Vision for Autonomous Off-Road Driving || [http://yann.lecun.com/exdb/publis/pdf/hadsell-jfr-09.pdf Paper] || [[Learning Long-Range Vision for Autonomous Off-Road Driving|Summary]]<br />
|-<br />
|Luyao Ruan|| || Deep Learning of the tissue-regulated splicing code || [http://bioinformatics.oxfordjournals.org/content/30/12/i121.full.pdf+html Paper] || [[Deep Learning of the tissue-regulated splicing code| Summary]]<br />
|-<br />
|Abdullah Rashwan|| || Deep Convolutional Neural Networks For LVCSR || [http://www.cs.toronto.edu/~asamir/papers/icassp13_cnn.pdf paper] || [[Deep Convolutional Neural Networks For LVCSR| Summary]]<br />
|-<br />
|Mahmood Gohari||37 || On using very large target vocabulary for neural machine translation || [http://arxiv.org/pdf/1412.2007v2.pdf paper] || [[On using very large target vocabulary for neural machine translation| Summary]]<br />
|-<br />
|Valerie Platsko|| || Learning Convolutional Feature Hierarchies for Visual Recognition || [http://papers.nips.cc/paper/4133-learning-convolutional-feature-hierarchies-for-visual-recognition Paper] || [[Learning Convolutional Feature Hierarchies for Visual Recognition | Summary]]<br />
|-<br />
|Derek Latremouille|| || Learning fast approximations of sparse coding || [http://yann.lecun.com/exdb/publis/pdf/gregor-icml-10.pdf Paper] || [[Learning fast approximations of sparse coding | Summary]]</div>Ali.MSHhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=f15Stat946PaperSignUp&diff=26239f15Stat946PaperSignUp2015-11-14T20:34:42Z<p>Ali.MSH: Genetic Application of Deep Learning</p>
<hr />
<div> <br />
=[https://uwaterloo.ca/data-science/sites/ca.data-science/files/uploads/files/listofpapers1.pdf List of Papers]=<br />
<br />
= Record your contributions [https://docs.google.com/spreadsheets/d/1A_0ej3S6ns3bBMwWLS4pwA6zDLz_0Ivwujj-d1Gr9eo/edit?usp=sharing here:]=<br />
<br />
Use the following notations:<br />
<br />
S: You have written a summary on the paper<br />
<br />
T: You had technical contribution on a paper (excluding the paper that you present from set A or critique from set B)<br />
<br />
E: You had editorial contribution on a paper (excluding the paper that you present from set A or critique from set B)<br />
<br />
[http://goo.gl/forms/RASFRZXoxJ Your feedback on presentations]<br />
<br />
<br />
=Set A=<br />
{| class="wikitable"<br />
<br />
{| border="1" cellpadding="3"<br />
|-<br />
|width="60pt"|Date<br />
|width="100pt"|Name <br />
|width="30pt"|Paper number <br />
|width="400pt"|Title<br />
|width="30pt"|Link to the paper<br />
|width="30pt"|Link to the summary<br />
|-<br />
|Oct 16 || pascal poupart || || Guest Lecturer||||<br />
|-<br />
|Oct 16 ||pascal poupart || ||Guest Lecturer ||||<br />
|-<br />
|Oct 23 || Ali Ghodsi || || Lecturer||||<br />
|-<br />
|Oct 23 || Ali Ghodsi || || Lecturer||||<br />
|-<br />
|Oct 23 ||Ri Wang || ||Sequence to sequence learning with neural networks.||[http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf Paper] || [http://wikicoursenote.com/wiki/Stat946f15/Sequence_to_sequence_learning_with_neural_networks#Long_Short-Term_Memory_Recurrent_Neural_Network Summary]<br />
|-<br />
|Oct 23 || Deepak Rishi || || Parsing natural scenes and natural language with recursive neural networks || [http://www-nlp.stanford.edu/pubs/SocherLinNgManning_ICML2011.pdf Paper] || [[Parsing natural scenes and natural language with recursive neural networks | Summary]]<br />
|-<br />
|Oct 30 || Ali Ghodsi || || Lecturer||||<br />
|-<br />
|Oct 30 || Ali Ghodsi || || Lecturer||||<br />
|-<br />
|Oct 30 ||Rui Qiao || ||Going deeper with convolutions || [http://arxiv.org/pdf/1409.4842v1.pdf Paper]|| [[GoingDeeperWithConvolutions|Summary]]<br />
|-<br />
|Oct 30 ||Amirreza Lashkari|| 21 ||Overfeat: integrated recognition, localization and detection using convolutional networks. || [http://arxiv.org/pdf/1312.6229v4.pdf Paper]|| [[Overfeat: integrated recognition, localization and detection using convolutional networks|Summary]]<br />
|-<br />
|Mkeup Class (TBA) || Peter Blouw|| ||Memory Networks.|| [http://arxiv.org/abs/1410.3916]|| [[Memory Networks|Summary]]<br />
|-<br />
|Nov 6 || Ali Ghodsi || || Lecturer||||<br />
|-<br />
|Nov 6 || Ali Ghodsi || || Lecturer||||<br />
|-<br />
|Nov 6 || Anthony Caterini ||56 || Human-level control through deep reinforcement learning ||[http://www.nature.com/nature/journal/v518/n7540/pdf/nature14236.pdf Paper]|| [[Human-level control through deep reinforcement learning|Summary]]<br />
|-<br />
|Nov 6 || Sean Aubin || ||Learning Hierarchical Features for Scene Labeling ||[http://yann.lecun.com/exdb/publis/pdf/farabet-pami-13.pdf Paper]||[[Learning Hierarchical Features for Scene Labeling|Summary]]<br />
|-<br />
|Nov 13|| Mike Hynes || 12 ||Speech recognition with deep recurrent neural networks || [http://www.cs.toronto.edu/~fritz/absps/RNN13.pdf Paper] || [[Graves et al., Speech recognition with deep recurrent neural networks|Summary]]<br />
|-<br />
|Nov 13 || Tim Tse || || Question Answering with Subgraph Embeddings || [http://arxiv.org/pdf/1406.3676v3.pdf Paper] || [[Question Answering with Subgraph Embeddings | Summary ]]<br />
|-<br />
|Nov 13 || Maysum Panju || ||Neural machine translation by jointly learning to align and translate ||[http://arxiv.org/pdf/1409.0473v6.pdf Paper] || [[Neural Machine Translation: Jointly Learning to Align and Translate|Summary]]<br />
|-<br />
|Nov 13 || Abdullah Rashwan || || Deep neural networks for acoustic modeling in speech recognition. ||[http://research.microsoft.com/pubs/171498/HintonDengYuEtAl-SPM2012.pdf paper]|| [[Deep neural networks for acoustic modeling in speech recognition| Summary]]<br />
|-<br />
|Nov 20 || Valerie Platsko || ||Natural language processing (almost) from scratch. ||[http://arxiv.org/pdf/1103.0398.pdf Paper]|| [[Natural language processing (almost) from scratch. | Summary]]<br />
|-<br />
|Nov 20 || Brent Komer || ||Show, Attend and Tell: Neural Image Caption Generation with Visual Attention || [http://arxiv.org/pdf/1502.03044v2.pdf Paper]||[[Show, Attend and Tell: Neural Image Caption Generation with Visual Attention|Summary]]<br />
|-<br />
|Nov 20 || Luyao Ruan || || Dropout: A Simple Way to Prevent Neural Networks from Overfitting || [https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf Paper]|| [[dropout | Summary]]<br />
|-<br />
|Nov 20 || Ali Mahdipour || || The human splicing code reveals new insights into the genetic determinants of disease ||[https://www.sciencemag.org/content/347/6218/1254806.full.pdf Paper] ||<br />
| [[Genetics | Summary]]|-<br />
|Nov 27 ||Mahmood Gohari || ||Deep Neural Nets as a Method for Quantitative Structure–Activity Relationships ||[http://pubs.acs.org/doi/abs/10.1021/ci500747n.pdf Paper]||<br />
|-<br />
|Nov 27 || Derek Latremouille || ||The Wake-Sleep Algorithm for Unsupervised Neural Networks || [http://www.gatsby.ucl.ac.uk/~dayan/papers/hdfn95.pdf Paper] ||<br />
|-<br />
|Nov 27 ||Xinran Liu || ||ImageNet Classification with Deep Convolutional Neural Networks ||[http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf Paper]||[[ImageNet Classification with Deep Convolutional Neural Networks|Summary]]<br />
|-<br />
|Nov 27 ||Ali Sarhadi|| ||Strategies for Training Large Scale Neural Network Language Models||||<br />
|-<br />
|Dec 4 || Chris Choi || || On the difficulty of training recurrent neural networks || [http://www.jmlr.org/proceedings/papers/v28/pascanu13.pdf Paper] || [[On the difficulty of training recurrent neural networks | Summary]]<br />
|-<br />
|Dec 4 || Fatemeh Karimi || ||MULTIPLE OBJECT RECOGNITION WITH VISUAL ATTENTION||[http://arxiv.org/pdf/1412.7755v2.pdf Paper]||<br />
|-<br />
|Dec 4 || Jan Gosmann || || On the Number of Linear Regions of Deep Neural Networks || [http://arxiv.org/abs/1402.1869 Paper] || [[On the Number of Linear Regions of Deep Neural Networks | Summary]]<br />
|-<br />
|Dec 4 || Dylan Drover || || Towards AI-complete question answering: a set of prerequisite toy tasks || [http://arxiv.org/pdf/1502.05698.pdf Paper] ||<br />
|-<br />
|}<br />
|}<br />
<br />
=Set B=<br />
<br />
{| class="wikitable"<br />
<br />
{| border="1" cellpadding="3"<br />
|-<br />
|width="100pt"|Name <br />
|width="30pt"|Paper number <br />
|width="400pt"|Title<br />
|width="30pt"|Link to the paper<br />
|width="30pt"|Link to the summary<br />
|-<br />
|Anthony Caterini ||15 ||The Manifold Tangent Classifier ||[http://papers.nips.cc/paper/4409-the-manifold-tangent-classifier.pdf Paper]||<br />
|-<br />
|Jan Gosmann || || Neural Turing machines || [http://arxiv.org/abs/1410.5401 Paper] || [[Neural Turing Machines|Summary]]<br />
|-<br />
|Brent Komer || || Scene Parsing with Multiscale Feature Learning, Purity Trees, and Optimal Covers || [http://arxiv.org/pdf/1202.2160v2.pdf Paper] ||<br />
|-<br />
|Sean Aubin || || Deep Sparse Rectifier Neural Networks || [http://jmlr.csail.mit.edu/proceedings/papers/v15/glorot11a/glorot11a.pdf Paper] || [[Deep Sparse Rectifier Neural Networks|Summary]]<br />
|-<br />
|Peter Blouw|| || Generating text with recurrent neural networks || [http://www.cs.utoronto.ca/~ilya/pubs/2011/LANG-RNN.pdf Paper] ||<br />
|-<br />
|Tim Tse|| || From Machine Learning to Machine Reasoning || [http://research.microsoft.com/pubs/206768/mlj-2013.pdf Paper] || [[From Machine Learning to Machine Reasoning | Summary ]]<br />
|-<br />
|Rui Qiao|| || Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation || [http://arxiv.org/pdf/1406.1078v3.pdf Paper] || [[Learning Phrase Representations|Summary]]<br />
|-<br />
|Ftemeh Karimi|| 23 || Very Deep Convoloutional Networks for Large-Scale Image Recognition || [http://arxiv.org/pdf/1409.1556.pdf Paper] || [[Very Deep Convoloutional Networks for Large-Scale Image Recognition|Summary]]<br />
|-<br />
|Amirreza Lashkari|| 43 || Distributed Representations of Words and Phrases and their Compositionality || [http://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf Paper] || [[Distributed Representations of Words and Phrases and their Compositionality|Summary]]<br />
|-<br />
|Xinran Liu|| 19 || Joint training of a convolutional network and a graphical model for human pose estimation || [http://papers.nips.cc/paper/5573-joint-training-of-a-convolutional-network-and-a-graphical-model-for-human-pose-estimation.pdf Paper] || [[Joint training of a convolutional network and a graphical model for human pose estimation|Summary]]<br />
|-<br />
|Chris Choi|| || Learning Long-Range Vision for Autonomous Off-Road Driving || [http://yann.lecun.com/exdb/publis/pdf/hadsell-jfr-09.pdf Paper] || [[Learning Long-Range Vision for Autonomous Off-Road Driving|Summary]]<br />
|-<br />
|Luyao Ruan|| || Deep Learning of the tissue-regulated splicing code || [http://bioinformatics.oxfordjournals.org/content/30/12/i121.full.pdf+html Paper] || [[Deep Learning of the tissue-regulated splicing code| Summary]]<br />
|-<br />
|Abdullah Rashwan|| || Deep Convolutional Neural Networks For LVCSR || [http://www.cs.toronto.edu/~asamir/papers/icassp13_cnn.pdf paper] || [[Deep Convolutional Neural Networks For LVCSR| Summary]]<br />
|-<br />
|Mahmood Gohari||37 || On using very large target vocabulary for neural machine translation || [http://arxiv.org/pdf/1412.2007v2.pdf paper] || [[On using very large target vocabulary for neural machine translation| Summary]]<br />
|-<br />
|Valerie Platsko|| || Learning Convolutional Feature Hierarchies for Visual Recognition || [http://papers.nips.cc/paper/4133-learning-convolutional-feature-hierarchies-for-visual-recognition Paper] || [[Learning Convolutional Feature Hierarchies for Visual Recognition | Summary]]<br />
|-<br />
|Derek Latremouille|| || Learning fast approximations of sparse coding || [http://yann.lecun.com/exdb/publis/pdf/gregor-icml-10.pdf Paper] || [[Learning fast approximations of sparse coding | Summary]]</div>Ali.MSHhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=f15Stat946PaperSignUp&diff=25345f15Stat946PaperSignUp2015-10-15T20:19:12Z<p>Ali.MSH: </p>
<hr />
<div> <br />
[https://uwaterloo.ca/data-science/sites/ca.data-science/files/uploads/files/listofpapers1.pdf List of Papers]<br />
<br />
<br />
{| class="wikitable"<br />
<br />
{| border="1" cellpadding="3"<br />
|-<br />
|width="60pt"|Date<br />
|width="100pt"|Name <br />
|width="30pt"|Paper number <br />
|width="400pt"|Title<br />
|width="30pt"|Link to the paper<br />
|width="30pt"|Link to the summary<br />
|-<br />
|Oct 16 || pascal poupart || || Guest Lecturer||||<br />
|-<br />
|Oct 16 ||pascal poupart || ||Guest Lecturer ||||<br />
|-<br />
|Oct 23 ||Ri Wang || ||Sequence to sequence learning with neural networks.||[http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf Paper] ||<br />
|-<br />
|Oct 23 || Deepak Rishi || || Parsing natural scenes and natural language with recursive neural networks || [http://www-nlp.stanford.edu/pubs/SocherLinNgManning_ICML2011.pdf Paper] ||<br />
|-<br />
|Oct 30 ||Rui Qiao || ||Going deeper with convolutions || [http://arxiv.org/pdf/1409.4842v1.pdf Paper]|| [[Going deeper with convolutions|Summary]]<br />
|-<br />
|Oct 30 ||Amirreza Lashkari|| ||Overfeat: integrated recognition, localization and detection using convolutional networks. || [http://arxiv.org/pdf/1312.6229v4.pdf Paper]|| [[Overfeat: integrated recognition, localization and detection using convolutional networks|Summary]]<br />
|-<br />
|Oct 30 || Peter Blouw|| ||Distributed representations of words and phrases and their compositionality.|| [http://goo.gl/NCXliI]|| [[Distributed representations of words and phrases and their compositionally|Summary]]<br />
|-<br />
|Nov 6 || Anthony Caterini || || Human-level control through deep reinforcement learning ||[http://www.nature.com/nature/journal/v518/n7540/pdf/nature14236.pdf Paper]|| [[Human-level control through deep reinforcement learning|Summary]]<br />
|-<br />
|Nov 6 || Sean Aubin || ||Learning Hierarchical Features for Scene Labeling ||[http://yann.lecun.com/exdb/publis/pdf/farabet-pami-13.pdf Paper]||[[Learning Hierarchical Features for Scene Labeling|Summary]]<br />
|-<br />
|Nov 6 || Mike Hynes || 12 ||Speech recognition with deep recurrent neural networks || [http://www.cs.toronto.edu/~fritz/absps/RNN13.pdf Paper] || [[Graves et al., Speech recognition with deep recurrent neural networks|Summary]]<br />
<br />
<br />
|-<br />
|Nov 13 || Tim Tse || || . From machine learning to machine reasoning. Mach. Learn. ||[http://research.microsoft.com/pubs/206768/mlj-2013.pdf Paper]||<br />
|-<br />
|Nov 13 || Maysum Panju || ||Neural machine translation by jointly learning to align and translate ||[http://arxiv.org/pdf/1409.0473v6.pdf Paper] ||<br />
|-<br />
|Nov 13 || Abdullah Rashwan || || Deep neural networks for acoustic modeling in speech recognition. ||[http://research.microsoft.com/pubs/171498/HintonDengYuEtAl-SPM2012.pdf paper]||<br />
|-<br />
|Nov 20 || Valerie Platsko || ||Natural language processing (almost) from scratch. ||[http://arxiv.org/pdf/1103.0398.pdf Paper]||<br />
|-<br />
|Nov 20 || Brent Komer || ||Show, Attend and Tell: Neural Image Caption Generation with Visual Attention || [http://arxiv.org/pdf/1502.03044v2.pdf Paper]||[[Show, Attend and Tell: Neural Image Caption Generation with Visual Attention|Summary]]<br />
|-<br />
|Nov 20 || Luyao Ruan || || Dropout: A Simple Way to Prevent Neural Networks from Overfitting || [https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf Paper]||<br />
|-<br />
|Nov 20 || Ali Mahdipour || || The human splicing code reveals new insights into the genetic determinants of disease ||[https://www.sciencemag.org/content/347/6218/1254806.full.pdf Paper] ||<br />
|-<br />
|Nov 27 ||Mahmood Gohari || ||Deep Neural Nets as a Method for Quantitative Structure–Activity Relationships ||[http://pubs.acs.org/doi/abs/10.1021/ci500747n.pdf Paper]||<br />
|-<br />
|Nov 27 || Derek Latremouille || ||The Wake-Sleep Algorithm for Unsupervised Neural Networks || [http://www.gatsby.ucl.ac.uk/~dayan/papers/hdfn95.pdf Paper] ||<br />
|-<br />
|Nov 27 ||Xinran Liu || ||ImageNet Classification with Deep Convolutional Neural Networks ||[http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf Paper]||[[ImageNet Classification with Deep Convolutional Neural Networks|Summary]]<br />
|-<br />
|Nov 27 || || || || ||<br />
|-<br />
|Dec 4 || Chris Choi || || On the difficulty of training recurrent neural networks || [http://www.jmlr.org/proceedings/papers/v28/pascanu13.pdf Paper] || [[On the difficulty of training recurrent neural networks | Summary]]<br />
|-<br />
|Dec 4 || Fatemeh Karimi || ||Connectomic reconstruction of the inner plexiform layer in the mouse retina ||[http://www.nature.com/nature/journal/v500/n7461/pdf/nature12346.pdf Paper]||<br />
|-<br />
|Dec 4 || Jan Gosmann || || A fast learning algorithm for deep belief nets || [http://www.mitpressjournals.org/doi/pdf/10.1162/neco.2006.18.7.1527 Paper] || [[A fast learning algorithm for deep belief nets | Summary]]<br />
|-<br />
|Dec 4 || || || || ||</div>Ali.MSHhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=f15Stat946PaperSignUp&diff=25344f15Stat946PaperSignUp2015-10-15T20:17:14Z<p>Ali.MSH: </p>
<hr />
<div> <br />
[https://uwaterloo.ca/data-science/sites/ca.data-science/files/uploads/files/listofpapers1.pdf List of Papers]<br />
<br />
<br />
{| class="wikitable"<br />
<br />
{| border="1" cellpadding="3"<br />
|-<br />
|width="60pt"|Date<br />
|width="100pt"|Name <br />
|width="30pt"|Paper number <br />
|width="400pt"|Title<br />
|width="30pt"|Link to the paper<br />
|width="30pt"|Link to the summary<br />
|-<br />
|Oct 16 || pascal poupart || || Guest Lecturer||||<br />
|-<br />
|Oct 16 ||pascal poupart || ||Guest Lecturer ||||<br />
|-<br />
|Oct 23 ||Ri Wang || ||Sequence to sequence learning with neural networks.||[http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf Paper] ||<br />
|-<br />
|Oct 23 || Deepak Rishi || || Parsing natural scenes and natural language with recursive neural networks || [http://www-nlp.stanford.edu/pubs/SocherLinNgManning_ICML2011.pdf Paper] ||<br />
|-<br />
|Oct 30 ||Rui Qiao || ||Going deeper with convolutions || [http://arxiv.org/pdf/1409.4842v1.pdf Paper]|| [[Going deeper with convolutions|Summary]]<br />
|-<br />
|Oct 30 ||Amirreza Lashkari|| ||Overfeat: integrated recognition, localization and detection using convolutional networks. || [http://arxiv.org/pdf/1312.6229v4.pdf Paper]|| [[Overfeat: integrated recognition, localization and detection using convolutional networks|Summary]]<br />
|-<br />
|Oct 30 || Peter Blouw|| ||Distributed representations of words and phrases and their compositionality.|| [http://goo.gl/NCXliI]|| [[Distributed representations of words and phrases and their compositionally|Summary]]<br />
|-<br />
|Nov 6 || Anthony Caterini || || Human-level control through deep reinforcement learning ||[http://www.nature.com/nature/journal/v518/n7540/pdf/nature14236.pdf Paper]|| [[Human-level control through deep reinforcement learning|Summary]]<br />
|-<br />
|Nov 6 || Sean Aubin || ||Learning Hierarchical Features for Scene Labeling ||[http://yann.lecun.com/exdb/publis/pdf/farabet-pami-13.pdf Paper]||[[Learning Hierarchical Features for Scene Labeling|Summary]]<br />
|-<br />
|Nov 6 || Mike Hynes || 12 ||Speech recognition with deep recurrent neural networks || [http://www.cs.toronto.edu/~fritz/absps/RNN13.pdf Paper] || [[Graves et al., Speech recognition with deep recurrent neural networks|Summary]]<br />
<br />
<br />
|-<br />
|Nov 13 || Tim Tse || || . From machine learning to machine reasoning. Mach. Learn. ||[http://research.microsoft.com/pubs/206768/mlj-2013.pdf Paper]||<br />
|-<br />
|Nov 13 || Maysum Panju || ||Neural machine translation by jointly learning to align and translate ||[http://arxiv.org/pdf/1409.0473v6.pdf Paper] ||<br />
|-<br />
|Nov 13 || Abdullah Rashwan || || Deep neural networks for acoustic modeling in speech recognition. ||[http://research.microsoft.com/pubs/171498/HintonDengYuEtAl-SPM2012.pdf paper]||<br />
|-<br />
|Nov 20 || Valerie Platsko || ||Natural language processing (almost) from scratch. ||[http://arxiv.org/pdf/1103.0398.pdf Paper]||<br />
|-<br />
|Nov 20 || Brent Komer || ||Show, Attend and Tell: Neural Image Caption Generation with Visual Attention || [http://arxiv.org/pdf/1502.03044v2.pdf Paper]||[[Show, Attend and Tell: Neural Image Caption Generation with Visual Attention|Summary]]<br />
|-<br />
|Nov 20 || Luyao Ruan || || Dropout: A Simple Way to Prevent Neural Networks from Overfitting || [https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf Paper]||<br />
|-<br />
|Nov 20 || Ali Mahdipour || || The human splicing code reveals new insights into the genetic determinants of disease ||[https://www.sciencemag.org/content/347/6218/1254806.full.pdf] ||<br />
|-<br />
|Nov 27 ||Mahmood Gohari || ||Deep Neural Nets as a Method for Quantitative Structure–Activity Relationships ||[http://pubs.acs.org/doi/abs/10.1021/ci500747n.pdf Paper]||<br />
|-<br />
|Nov 27 || Derek Latremouille || ||The Wake-Sleep Algorithm for Unsupervised Neural Networks || [http://www.gatsby.ucl.ac.uk/~dayan/papers/hdfn95.pdf Paper] ||<br />
|-<br />
|Nov 27 ||Xinran Liu || ||ImageNet Classification with Deep Convolutional Neural Networks ||[http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf Paper]||[[ImageNet Classification with Deep Convolutional Neural Networks|Summary]]<br />
|-<br />
|Nov 27 || || || || ||<br />
|-<br />
|Dec 4 || Chris Choi || || On the difficulty of training recurrent neural networks || [http://www.jmlr.org/proceedings/papers/v28/pascanu13.pdf Paper] || [[On the difficulty of training recurrent neural networks | Summary]]<br />
|-<br />
|Dec 4 || Fatemeh Karimi || ||Connectomic reconstruction of the inner plexiform layer in the mouse retina ||[http://www.nature.com/nature/journal/v500/n7461/pdf/nature12346.pdf Paper]||<br />
|-<br />
|Dec 4 || Jan Gosmann || || A fast learning algorithm for deep belief nets || [http://www.mitpressjournals.org/doi/pdf/10.1162/neco.2006.18.7.1527 Paper] || [[A fast learning algorithm for deep belief nets | Summary]]<br />
|-<br />
|Dec 4 || || || || ||</div>Ali.MSH