Combining gene expression, demographic and clinical data in modeling disease: a case study of bipolar disorder and schizophrenia.

ABSTRACT:

Background

This paper presents a retrospective statistical study on the newly-released data set by the Stanley Neuropathology Consortium on gene expression in bipolar disorder and schizophrenia. This data set contains gene expression data as well as limited demographic and clinical data for each subject. Previous studies using statistical classification or machine learning algorithms have focused on gene expression data only. The present paper investigates if such techniques can benefit from including demographic and clinical data.

Results

We compare six classification algorithms: support vector machines (SVMs), nearest shrunken centroids, decision trees, ensemble of voters, naïve Bayes, and nearest neighbor. SVMs outperform the other algorithms. Using expression data only,

SUBMITTER: Struyf J 

PROVIDER: S-EPMC2628394 | biostudies-literature | 2008 Nov

REPOSITORIES: biostudies-literature