Partial Mutual Information for Classification of Gene Expression Data by Learning Vector Quantization

Abstract

Gene expression data analysis is frequently performed using correlation measures whereas unsupervised and supervised vector quantization methods are usually designed for Euclidean distances. In this paper we summarize recent approaches to apply correlation measures to those vector quantization algorithms for analysis of microarray gene expression data. Additionally, we consider fc-th order partial correlations as a natural extension if pseudo-correlations should be avoided. Further, we draw the focus to mutual information as powerful alternatives to correlation measures. Related to this we provide the concept of fc-th order partial mutual information as counterpart to partial correlations. We apply these methods to an exemplary but real classification problem in gene expression analysis for detection of diabetic patients. © Springer International Publishing Switzerland 2014.