Varmole: a biologically drop-connect deep neural network model for prioritizing disease risk variants and genes.
Varmole: a biologically drop-connect deep neural network model for prioritizing disease risk variants and genes.
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DOI：
10.1093/bioinformatics/btaa866
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发表时间：
2021-07-19
期刊：
影响因子：
--
通讯作者：
Wang D
中科院分区：
文献类型：
--
作者：
Nguyen ND;Jin T;Wang D
群体研究如全基因组关联研究已经鉴定出与人类疾病相关的多种基因组变异。为了进一步了解疾病变异的潜在机制，最近的统计方法将功能组学数据（例如基因表达）与基因型和表型相关联，并将变异与单个基因联系起来。然而，如何从这些关联中解释分子机制，特别是跨组学，仍然具有挑战性。为了解决这个问题，我们开发了一种可解释的深度学习方法Varmole，可以同时揭示基因组功能和机制，同时从基因型预测表型。特别是，Varmole将多组学网络嵌入到深度神经网络架构中，并通过生物drop-connect对变体、基因和调控连接进行优先级排序，而无需事先进行特征选择。Varmole是GitHub上的Python工具，网址是https://github.com/daifengwanglab/Varmole。 补充数据可在Bioinformatics在线获得。
Population studies such as genome-wide association study have identified a variety of genomic variants associated with human diseases. To further understand potential mechanisms of disease variants, recent statistical methods associate functional omic data (e.g. gene expression) with genotype and phenotype and link variants to individual genes. However, how to interpret molecular mechanisms from such associations, especially across omics, is still challenging. To address this problem, we developed an interpretable deep learning method, Varmole, to simultaneously reveal genomic functions and mechanisms while predicting phenotype from genotype. In particular, Varmole embeds multi-omic networks into a deep neural network architecture and prioritizes variants, genes and regulatory linkages via biological drop-connect without needing prior feature selections. Varmole is available as a Python tool on GitHub at https://github.com/daifengwanglab/Varmole. Supplementary data are available at Bioinformatics online.
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