Predicting lithium treatment response in bipolar patients using gender-specific gene expression biomarkers and machine learning.

ABSTRACT: Background: We sought to test the hypothesis that transcriptome-level gene signatures are differentially expressed between male and female bipolar patients, prior to lithium treatment, in a patient cohort who later were clinically classified as lithium treatment responders. Methods: Gene expression study data was obtained from the Lithium Treatment-Moderate dose Use Study data accessed from the National Center for Biotechnology Information's Gene Expression Omnibus via accession number GSE4548. Differential gene expression analysis was conducted using the Linear Models for Microarray and RNA-Seq (limma) package and the Decision Tree and Random Forest machine learning algorithms in R. Results: Using quantitative gene expression values reported from patient blood samples

SUBMITTER: Eugene AR 

PROVIDER: S-EPMC6381805 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature