TRIQ: A Comprehensive Evaluation Measure for Triclustering Algorithms

Abstract

Triclustering has shown to be a valuable tool for the analysis of microarray data since its appearance as an improvement of classical clustering and biclustering techniques. Triclustering relaxes the constraints for grouping and allows genes to be evaluated under a subset of experimental conditions and a subset of time points simultaneously. The authors previously presented a genetic algorithm, TriGen, that finds triclusters of gene expression dasta. They also defined three different fitness functions for TriGen: MSR3D, LSL and MSL. In order to asses the results obtained by application of TriGen, a validity measure needs to be defined. Therefore, we present TRIQ, a validity measure which combines information from three different sources: (1) correlation among genes, conditions and times, (2) graphic validation of the patterns extracted and (3) functional annotations for the genes extracted.