title: A multi-index ROC-based methodology for high throughput experiments in gene discovery.
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.
authors.affiliation: Electrical and Computer Engineering Department, Southern Illinois University, 1230 Lincoln Drive, Carbondale, IL 62901, USA. kagaris@engr.siu.edu
authors.forename: Dimitri
authors.initials: D
authors.lastname: Kagaris
authors.forename: Constantin T
authors.initials: CT
authors.lastname: Yiannoutsos
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D020869:Gene Expression Profiling / Q000379:methods*; D056726:Genetic Association Studies; D012372:ROC Curve