MapReduce based parallel gene selection method

Abstract

Microarray data analysis has been widely used for extracting relevant biological information from thousands of genes simultaneously expressed in a specific cell. Although many genes are expressed in a sample tissue, most of these are irrelevant or insignificant for clinical diagnosis or disease classification because of missing values and noises. Thus, finding a small, closely related gene set to accurately classify disease cells is an important research problem. At the same time, scalable gene selection methods are required for microarray data analysis due to rapidly increasing volume of microarray data. In this paper, we propose a scalable parallel gene selection method using the M a p R e u d c e programming model. The proposed method utilizes the kNN classifier algorithm for evaluating classification accuracy and uses four real and three synthetic datasets for experiments. Experimental results show that the proposed method can offer good scalability on large data with increasing number of nodes and it can also provide higher classification accuracy rather than using whole gene set for classification.