Classification of a large microarray data set: algorithm comparison and analysis of drug signatures.

ABSTRACT: A large gene expression database has been produced that characterizes the gene expression and physiological effects of hundreds of approved and withdrawn drugs, toxicants, and biochemical standards in various organs of live rats. In order to derive useful biological knowledge from this large database, a variety of supervised classification algorithms were compared using a 597-microarray subset of the data. Our studies show that several types of linear classifiers based on Support Vector Machines (SVMs) and Logistic Regression can be used to derive readily interpretable drug signatures with high classification performance. Both methods can be tuned to produce classifiers of drug treatments in the form of short, weighted gene lists which upon analysis reveal that some of the signature genes

SUBMITTER: Natsoulis G 

PROVIDER: S-EPMC1088301 | biostudies-literature | 2005 May

REPOSITORIES: biostudies-literature