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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.


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>