Classifying the multi-omics data of gastric cancer using a deep feature selection method

作者：

Highlights：

• This manuscript main purpose is to integrate the omics data of gastric cancer.

• Multi-omics integration analysis can use the cross-complementarity among omics to explore the correlation between different omics, which can make up for the information shortage of single omics.

• In the research of feature selection algorithms for multi-grouping data, most of them use it has low classification performance. RDFS has a neural network in it, the accuracy of classification is greatly improved by neural network.

摘要

•This manuscript main purpose is to integrate the omics data of gastric cancer.•Multi-omics integration analysis can use the cross-complementarity among omics to explore the correlation between different omics, which can make up for the information shortage of single omics.•In the research of feature selection algorithms for multi-grouping data, most of them use it has low classification performance. RDFS has a neural network in it, the accuracy of classification is greatly improved by neural network.

论文关键词：Gastric cancer,Multi-omics data,Feature selection,Neural network

论文评审过程：Received 8 September 2021, Revised 4 February 2022, Accepted 1 March 2022, Available online 12 March 2022, Version of Record 29 March 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.116813