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

TL;DR

DeePathology introduces a deep multi-task learning neural network that effectively infers molecular and tissue properties from cancer transcriptome data, outperforming previous methods and demonstrating robustness to noise.

Contribution

The paper presents a novel deep neural network architecture capable of multi-task inference from transcriptome data, with a low-dimensional latent space that enhances classification accuracy.

Findings

Achieves 99.4% accuracy in cancer subtype identification

Outperforms prior methods and classical machine learning approaches

Robust against noise and missing data

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 10787 clinical samples from 34 classes (one healthy and 33 different types of cancer) from 27 tissues. Our method significantly outperforms prior works and classical machine learning approaches in predicting tissue-of-origin, normal or disease state and cancer type of each sample. For tissues with more than one type of cancer, it reaches 99.4\% accuracy in identifying the correct cancer subtype. We also show this system is very robust against noise and missing values. Collectively, our results highlight applications of artificial intelligence in molecular cancer pathology and oncological research. DeePathology is freely available at \url{https://github.com/SharifBioinf/DeePathology}.