Gene Expression Classification Based on Deep Learning

Abstract

One of the most significant research topics in bioinformatics is the classification of gene expression. Gene expression data commonly have a large number of features and a small number of samples. The gene expression data are very different from one to another, this differentiation among data and the feature’s large number make the classification for gene expression data challenging. In this study, for classification we assessed the accuracy for most powerful deep learning’s algorithms such as Deep Neural Network, Recurrent Neural Network, Convolutional Neural Network and improved Deep Neural Network with the preprocessing technique. The DNN was improved by adding Dropout to it by which the overfitting problem was overcame. Our results showed that the proposed improved-DNN outperforms the other algorithms among all used datasets.