Inferring Molecular Pathology and micro-RNA Transcriptome from mRNA Profiles of Cancer Biopsies through Deep Multi-Task Learning

08/07/2018

∙

Despite great advances, molecular cancer pathology is often limited to use a small number of biomarkers rather than the whole transcriptome, partly due to the computational challenges. Here, we introduce a novel architecture of DNNs that is capable of simultaneous inference of various properties of biological samples, through multi-task and transfer learning. 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 system significantly outperforms prior works and classical machine learning approaches in predicting tissue-of-origin, normal or disease state and cancer type of each sample. Furthermore, it can predict miRNA transcription profile of each sample, which enables performing miRNA expression research when only mRNA transcriptome data are available. 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.

READ FULL TEXT

Inferring Molecular Pathology and micro-RNA Transcriptome from mRNA Profiles of Cancer Biopsies through Deep Multi-Task Learning

Towards computational fluorescence microscopy: Machine learning-based integrated prediction of morphological and molecular tumor profiles

Bayesian multi-domain learning for cancer subtype discovery from next-generation sequencing count data

Machine Learning Against Cancer: Accurate Diagnosis of Cancer by Machine Learning Classification of the Whole Genome Sequencing Data

Multi-task Learning of Histology and Molecular Markers for Classifying Diffuse Glioma

Network-based methods for outcome prediction in the "sample space"

Integrated Multi-omics Analysis Using Variational Autoencoders: Application to Pan-cancer Classification

SubOmiEmbed: Self-supervised Representation Learning of Multi-omics Data for Cancer Type Classification

Inferring Molecular Pathology and micro-RNA Transcriptome from mRNA Profiles of Cancer Biopsies through Deep Multi-Task Learning

Related Research

Towards computational fluorescence microscopy: Machine learning-based integrated prediction of morphological and molecular tumor profiles

Bayesian multi-domain learning for cancer subtype discovery from next-generation sequencing count data

Machine Learning Against Cancer: Accurate Diagnosis of Cancer by Machine Learning Classification of the Whole Genome Sequencing Data

Multi-task Learning of Histology and Molecular Markers for Classifying Diffuse Glioma

Network-based methods for outcome prediction in the "sample space"

Integrated Multi-omics Analysis Using Variational Autoencoders: Application to Pan-cancer Classification

SubOmiEmbed: Self-supervised Representation Learning of Multi-omics Data for Cancer Type Classification