MOLI: multi-omics late integration with deep neural networks for drug response prediction.

ABSTRACT:

Motivation

Historically, gene expression has been shown to be the most informative data for drug response prediction. Recent evidence suggests that integrating additional omics can improve the prediction accuracy which raises the question of how to integrate the additional omics. Regardless of the integration strategy, clinical utility and translatability are crucial. Thus, we reasoned a multi-omics approach combined with clinical datasets would improve drug response prediction and clinical relevance.

Results

We propose MOLI, a multi-omics late integration method based on deep neural networks. MOLI takes somatic mutation, copy number aberration and gene expression data as input, and integrates them for drug response prediction. MOLI uses type-specific encoding sub-networks t

SUBMITTER: Sharifi-Noghabi H 

PROVIDER: S-EPMC6612815 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature