Generalized relevance LVQ ( GRLVQ) with correlation measures for gene expression analysis

Abstract

A correlation-based similarity measure is derived for generalized relevance learning vector quantization ( GRLVQ). The resulting GRLVQ-C classifier makes Pearson correlation available in a classification cost framework where data prototypes and global attribute weighting terms are adapted into directions of minimum cost function values. In contrast to the Euclidean metric, the Pearson correlation measure makes input vector processing invariant to shifting and scaling transforms, which is a valuable feature for dealing with functional data and with intensity observations like gene expression patterns. Two types of data measures are derived from Pearson correlation in order to make its benefits for data processing available in compact prototype classification models. Fast convergence and high accuracies are demonstrated for cDNA-array gene expression data. Furthermore, the automatic attribute weighting of GRLVQ-C is successfully used to rate the functional relevance of analyzed genes.