Mining gene expression data of multiple sclerosis.

ABSTRACT:

Objectives

Microarray produces a large amount of gene expression data, containing various biological implications. The challenge is to detect a panel of discriminative genes associated with disease. This study proposed a robust classification model for gene selection using gene expression data, and performed an analysis to identify disease-related genes using multiple sclerosis as an example.

Materials and methods

Gene expression profiles based on the transcriptome of peripheral blood mononuclear cells from a total of 44 samples from 26 multiple sclerosis patients and 18 individuals with other neurological diseases (control) were analyzed. Feature selection algorithms including Support Vector Machine based on Recursive Feature Elimination, Receiver Operating Characteristic C

SUBMITTER: Guo P 

PROVIDER: S-EPMC4059716 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature