DeePathology: Deep Multi-Task Learning for Inferring Molecular Pathology from Cancer Transcriptome.

ABSTRACT: Despite great advances, molecular cancer pathology is often limited to the use of a small number of biomarkers rather than the whole transcriptome, partly due to computational challenges. Here, we introduce a novel architecture of Deep Neural Networks (DNNs) that is capable of simultaneous inference of various properties of biological samples, through multi-task and transfer learning. It encodes the whole transcription profile into a strikingly low-dimensional latent vector of size 8, and then recovers mRNA and miRNA expression profiles, tissue and disease type from this vector. This latent space is significantly better than the original gene expression profiles for discriminating samples based on their tissue and disease. We employed this architecture on mRNA transcription profiles of 107

SUBMITTER: Azarkhalili B 

PROVIDER: S-EPMC6848155 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature