Zhu Z, Fan Y, Kong Y, Lv J, Sun F. DeepLINK: Deep learning inference using knockoffs with applications to genomics.
Proc Natl Acad Sci U S A 2021;
118:e2104683118. [PMID:
34480002 PMCID:
PMC8433583 DOI:
10.1073/pnas.2104683118]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 07/16/2021] [Indexed: 11/18/2022] Open
Abstract
We propose a deep learning-based knockoffs inference framework, DeepLINK, that guarantees the false discovery rate (FDR) control in high-dimensional settings. DeepLINK is applicable to a broad class of covariate distributions described by the possibly nonlinear latent factor models. It consists of two major parts: an autoencoder network for the knockoff variable construction and a multilayer perceptron network for feature selection with the FDR control. The empirical performance of DeepLINK is investigated through extensive simulation studies, where it is shown to achieve FDR control in feature selection with both high selection power and high prediction accuracy. We also apply DeepLINK to three real data applications to demonstrate its practical utility.
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