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Abstract: The genetic function of most non-coding regions is unclear, and many genetic variants have been found in these regions. How to identify associated disease variants is still a challenge. A Support Vector Machine based algorithm CADD has been proposed, which can annotate coding and non-coding region variants. However, CADD fails to capture non-linear relationship among features. To solve this problem, this paper designed a hybrid convolutional neural network and fully connected neural network model. This model can capture non-linear relationship well among features. Our method achieves the highest accuracy of 66.44% on the testing set.
Key words: deep learning, genetic variants, pathogenicity, annotation
CLC Number:
TP391
YANG Shu-xin, TANG Da-rong. A hybrid deep neural network for annotating the pathogenicity of genetic variants[J]. , doi: 10.3969/j.issn.2095-1736.2019.03.094.
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URL: http://www.swxzz.com/EN/10.3969/j.issn.2095-1736.2019.03.094
http://www.swxzz.com/EN/Y2019/V36/I4/94