Classification of Autism Gene Expression Data Using Deep Learning

Abstract

Gene expression data is used in the prediction of many diseases. Autism spectrum disorder (ASD) is among those diseases, where information on gene expression for selecting and classifying genes are evaluated. The difficulty of selection and identification of the ASD genes remains a major setback in the gene expression analysis of ASD. The objective of this paper is to develop a classification model for ASD subjects. The paper employs: Deep Belief Network (DBN) based on the Gaussian Restricted Boltzmann machine (GRBM). Restricted Boltzmann machine (RBM) is considered a popular graphical model that constructs a latent representation of raw data fed at its input nodes. The model is based on its learning algorithm, namely, contrastive divergence, and information gain (IG) is used as the criterion for gene selection. Our proposed model proves that it can deal with gene expression values efficiently and achieved improvements over classical classification methods. The results show that that the most discriminative genes can be selected and identified with its gene expression values. We report an increase of 8% over the highest achieving algorithm on a standard dataset in terms of accuracy.