Neuro-logistic Models Based on Evolutionary Generalized Radial Basis Function for the Microarray Gene Expression Classification Problem

Abstract

Gene expression detection is a key bioinformatic problem which has been tackled as a classification problem of microarray gene expression, obtained by the light reflection analysis of genomic material. A typical microarray dataset may contain thousands of genes but only a small number of patterns (often less than two hundred). When the dataset presents these kinds of characteristics, state-of-the-art classification models show a high lack of performance. A two-stage algorithm has been proposed to successfully address the problem of microarray classification. In the first stage, two filter algorithms identify salient expression genes from thousands of genes. In the second stage, the proposed methodology is performed using selected gene subsets as new input variables. The methodology proposed is composed of a combination of Logistic Regression (LR) and Evolutionary Generalized Radial Basis Function (EGRBF) neural networks which have shown to be highly accurate in previous research in the modeling of high-dimensional patterns. Finally, the results obtained are contrasted with nonparametric statistical tests and confirm good synergy between EGRBF and LR models.