Title: A multi-index ROC-based methodology for high throughput experiments in gene discovery

Authors: Dimitri Kagaris; Constantin T. Yiannoutsos

Addresses: Electrical and Computer Engineering Department, Southern Illinois University, 1230 Lincoln Drive, Carbondale, IL 62901, USA ' Department of Biostatistics, Indiana University School of Medicine, 410 West 10th Street, Suite 3000, Indianapolis, IN 46202, USA

Abstract: We address the problem of ranking differentially expressed genes in high throughput experiments using Receiver Operating Characteristic (ROC) curves. As it is generally unknown whether large expression values constitute 'positive' or 'negative' results or which group is 'healthy' or 'diseased', we generate four ROC curves per gene. We then consider classification indices based on all or part of the four ROC curves and identify genes ranked low by the area under the curve (AUC) but high by at least one alternative index, invariably resulting to the discovery of genes that would otherwise be missed by the AUC index.

Keywords: microarray data analysis; gene expression; gene selection; receiver operating characteristic; ROC curves; breast cancer; ovarian cancer; gene discovery; bioinformatics.

DOI: 10.1504/IJDMB.2013.054693

International Journal of Data Mining and Bioinformatics, 2013 Vol.8 No.1, pp.42 - 65

Received: 30 Jul 2010
Accepted: 23 Jun 2011
Published online: 20 Oct 2014 *

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