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Genetic Application of Deep Learning

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.


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.

Materials and Methods

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]

Genome-wide Analysis

Spinal Muscular Atropy




[1] Hui Y. Xiong1 et al, The human splicing code reveals new insights into the genetic determinants of disease, Science 347, 2015.