A biphasic Deep Semi-supervised framework for Suptype Classification and biomarker discovery

Abstract

To take full advantage of the unprecedented development of -omics technologies and generate further biological insights into human disease, it is a pressing need to develop novel computational methods for integrative analysis of multi-omics data. Here we proposed a biphasic Deep Semi-supervised multi-omics integration framework for Subtype Classification and biomarker discovery, DeepSSC. In phase 1, each denoising autoencoder was used to extract a compact representation for each -omics data, and then they were concatenated and put into a feed-forward neural network for subtype classification. In phase 2, our Biomarker Gene Identification procedure leveraged that neural network classifier to render subtype-specific important biomarkers. We also validated our given results on independent dataset. We demonstrated that DeepSSC exhibited better performance over other state-of-the-art techniques concerning classification tasks. As a result, DeepSSC successfully detected well-known biomarkers and hinted at novel candidates from different -omics data types related to the investigated biomedical problems.