Article

Convolutional neural network model to predict causal risk factors that share complex regulatory features

2019-08-05

Abstract excerpt

<h4>ABSTRACT</h4> Major progress in disease genetics has been made through genome-wide association studies (GWASs). One of the key tasks for post-GWAS analyses is to identify causal noncoding variants with regulatory function. Here, on the basis of > 2,000 functional features, we developed a convolutional neural network framework for combinatorial, nonlinear modeling of complex patterns shared by risk variants sc...

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Literature Corpus work: 5cedce10-28c3-5d88-92ae-3d0ce9e1a1df
DOI: 10.1101/725309

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