Optimized between-group classification: a new jackknife-based gene selection procedure for genome-wide expression data.

ABSTRACT:

Background

A recent publication described a supervised classification method for microarray data: Between Group Analysis (BGA). This method which is based on performing multivariate ordination of groups proved to be very efficient for both classification of samples into pre-defined groups and disease class prediction of new unknown samples. Classification and prediction with BGA are classically performed using the whole set of genes and no variable selection is required. We hypothesize that an optimized selection of highly discriminating genes might improve the prediction power of BGA.

Results

We propose an optimized between-group classification (OBC) which uses a jackknife-based gene selection procedure. OBC emphasizes classification accuracy rather than feature selection.

SUBMITTER: Baty F 

PROVIDER: S-EPMC1261161 | biostudies-literature | 2005 Sep

REPOSITORIES: biostudies-literature