Machine learning approaches to predict lupus disease activity from gene expression data.

ABSTRACT: The integration of gene expression data to predict systemic lupus erythematosus (SLE) disease activity is a significant challenge because of the high degree of heterogeneity among patients and study cohorts, especially those collected on different microarray platforms. Here we deployed machine learning approaches to integrate gene expression data from three SLE data sets and used it to classify patients as having active or inactive disease as characterized by standard clinical composite outcome measures. Both raw whole blood gene expression data and informative gene modules generated by Weighted Gene Co-expression Network Analysis from purified leukocyte populations were employed with various classification algorithms. Classifiers were evaluated by 10-fold cross-validation across three com

SUBMITTER: Kegerreis B 

PROVIDER: S-EPMC6610624 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature