A Novel Deep Learning Method for Identification of Cancer Genes From Gene Expression Dataset

Abstract

Computational biology is the research area that contributes to the analysis of biological information. The selection of the subset of cancer-related genes is one amongst the foremost promising clinical research of gene expression data. Since a gene can take the role of various biological pathways that in turn can be active only under specific experimental conditions, the stacked denoising auto-encoder(SDAE) and the genetic algorithm were combined to perform biclustering of cancer genes from huge dimensional microarray gene expression data. The Genetic-SDAE proved superior to recently proposed biclustering methods and better to determine the maximum similarity of a set of biclusters of gene expression data with lower MSR and higher gene variance. This work also assesses the results with respect to the discovered genes and spot that the extracted set of biclusters are supported by biological evidence, such as enrichment of gene functions and biological processes.