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摘要： 绝大部分非编码区的基因功能尚不清楚，而许多的遗传变体就存在这些区域，如何识别与疾病相关的变体仍是一个挑战。已有基于支持向量机的算法CADD被提出，它可以注释编码和非编码区的变体，但是该方法未能捕获特征间的非线性关系。为了解决此问题，设计了一个混合卷积网络和全连接网路的模型，能很好地捕获特征之间的非线性关系。在测试集上，方法达到了最高的66.44%准确率。
关键词: 深度学习, 遗传变体, 致病性, 注释
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
中图分类号:
TP391
杨书新， 汤达荣. 基于混合深度神经网络的基因遗传变体致病性注释[J]. 生物学杂志, doi: 10.3969/j.issn.2095-1736.2019.03.094.
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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http://www.swxzz.com/CN/Y2019/V36/I4/94
