Citation

Tian S, Wang C, Chang HH. BMC Med. Inform. Decis. Mak. 2018; 18(Suppl 5): e115.

Affiliation

Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, 1518 Clifton Road NE, Atlanta, GA, 30322, USA.

Copyright

(Copyright © 2018, Holtzbrinck Springer Nature Publishing Group - BMC)

DOI

10.1186/s12911-018-0685-8

PMID

30526581

Abstract

BACKGROUND: Feature selection and gene set analysis are of increasing interest in the field of bioinformatics. While these two approaches have been developed for different purposes, we describe how some gene set analysis methods can be utilized to conduct feature selection.

METHODS: We adopted a gene set analysis method, the significance analysis of microarray gene set reduction (SAMGSR) algorithm, to carry out feature selection for longitudinal gene expression data.

RESULTS: Using a real-world application and simulated data, it is demonstrated that the proposed SAMGSR extension outperforms other relevant methods. In this study, we illustrate that a gene's expression profiles over time can be regarded as a gene set and then a suitable gene set analysis method can be utilized directly to select relevant genes associated with the phenotype of interest over time.

CONCLUSIONS: We believe this work will motivate more research to bridge feature selection and gene set analysis, with the development of novel algorithms capable of carrying out feature selection for longitudinal gene expression data.

Language: en

Keywords

Core subset; Feature selection; Gene set analysis; Longitudinal microarray data; Significance analysis of microarray (SAM)