A gene expression analysis system for medical diagnosis

In this paper we present a novel system that utilizes molecular-level information for medical diagnosis. It accepts high dimensional vectors of gene expressions, quantified by means of microarray image analysis, as input. The proposed system incorporates various data pre-processing methods, such as missing values estimation and data normalization. A novel approach to the classification of gene expression vectors in multiple classes that embodies vari-ous gene selection methods has been adopted for diagnostic purposes. The pro-posed system has been extensively tested on various, publicly available data-sets. We demonstrate its performance for prostate cancer diagnosis and com-pare its performance with a well established multiclass classification scheme. The results show that the proposed system could be proved a valuable diagnostic aid in medicine.