Identification of Differentially Expressed Genes Using Deep Learning in Bioinformatics

TLDR: In this paper, SegNet and U-Net were applied to the microarray dataset of colon cancer (typically containing tumour and normal tissue samples) to extract the culprit/responsible gene.

Abstract: Bioinformatics data can be used for the ultimate prediction of diseases in different organisms. The microarray technology is a special form of 2D representation of genomic data characterized by an enormous number of genes across a handful of samples. The actual analysis of this data involves extraction or selection of the relevant genes from this vast amount of irrelevant and redundant data. These genes can be further used to predict classes of unknown samples. In this work, we have implemented two popular deep learning segmentation architectures, namely, SegNet and U-Net. These techniques have been applied to the microarray dataset of colon cancer (typically containing tumour and normal tissue samples) to extract the culprit/responsible gene. The performance of the reduced set formed from these genes has been compared across different classifiers using different existing methods of feature selection. It is found that both deep learning based approaches outperform the other methods. Lastly, the biological significance of the genes has also been verified using ontological tools, and the results are significant.