Work

DeepLINK: Deep learning inference using knockoffs with applications to genomics

Year: 2021

Type: article

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.

Source: Proceedings of the National Academy of Sciences

DOI: 10.1073/pnas.2104683118

Authors: Zifan Zhu, Yingying Fan, Yinfei Kong, Jinchi Lv, Fengzhu Sun

Institutions: University of Southern California, California State University, Fullerton

Language: English

Topic: Statistical Methods and Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funders: National Institute of General Medical Sciences, Division of Mathematical Sciences

Awards: Adaptive Reproducible High-Dimensional Nonlinear Inference for Big Biological Data, G5814994580, Computational Studies of Virus-host Interactions Using Metagenomics Data and Applications