Computational Methods for Preprocessing and Classifying Gene Expression Data- Survey

Abstract

Microarray experiments generate data sets containing valuable information on the gene expression levels of millions of genes in the form of a set of biological samples. Inference of gene regulatory networks from microarray data has become an important research area in bioinformatics. Several computational methods have been proposed to infer important relationships between transcription factors with target genes from gene expression and transcription factor data sets. The inferences from these methods are consistent with the biological literature and can help researchers design reasonable and efficient drugs to resist different diseases. This paper is a survey of different methods used for preprocessing and inferring the gene regulatory networks from a data set of gene expression.