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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 the intronic and exonic variants of mutations. This procedure should enable us to prognosis, diagnosis, and/or control many diseases.



Materials and Methods


Genome-wide Analysis

Spinal Muscular Atropy



<ref> Hui Y. Xiong1 et al, The human splicing code reveals new insights into the genetic determinants of disease, Science 347, 2015. </ref>