A longitudinal feature selection method identifies relevant genes to distinguish complicated injury and uncomplicated injury over time

• Type of Material: Article
• Authors: Suyan Tian, Jilin University; Chi Wang, University of Kentucky; Howard Chang, Emory University
• Language: English
• Date: 2018-12-07
• Publisher: BMC (part of Springer Nature)
• Publication Version: Final Published Version
• Copyright Statement: © 2018 The Author(s).
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1186/s12911-018-0685-8
• Title of Journal or Parent Work: BMC Medical Informatics and Decision Making
• ISSN: 1472-6947
• Volume: 18
• Issue: Suppl 5
• Start Page: 115
• End Page: 115
• Grant/Funding Information: Publication of this article was sponsored by a grant (No. 31401123) from the National Natural Science Foundation of China.
• Keywords: Life Sciences & Biomedicine; MICROARRAY; Medical Informatics; Gene set analysis; Significance analysis of microarray (SAM); Feature selection; Longitudinal microarray data; Science & Technology; Core subset
• Research Categories: Biology, Biostatistics

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.