An embedded gene selection method using knockoffs optimizing neural network.

ABSTRACT:

Background

Gene selection refers to find a small subset of discriminant genes from the gene expression profiles. How to select genes that affect specific phenotypic traits effectively is an important research work in the field of biology. The neural network has better fitting ability when dealing with nonlinear data, and it can capture features automatically and flexibly. In this work, we propose an embedded gene selection method using neural network. The important genes can be obtained by calculating the weight coefficient after the training is completed. In order to solve the problem of black box of neural network and further make the training results interpretable in neural network, we use the idea of knockoffs to construct the knockoff feature genes of the original feature gene

SUBMITTER: Guo J 

PROVIDER: S-EPMC7510330 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature