Multimodal representation learning for predicting molecule-disease relations.

ABSTRACT:

Motivation

Predicting molecule-disease indications and side effects is important for drug development and pharmacovigilance. Comprehensively mining molecule-molecule, molecule-disease and disease-disease semantic dependencies can potentially improve prediction performance.

Methods

We introduce a Multi-Modal REpresentation Mapping Approach to Predicting molecular-disease relations (M2REMAP) by incorporating clinical semantics learned from electronic health records (EHR) of 12.6 million patients. Specifically, M2REMAP first learns a multimodal molecule representation that synthesizes chemical property and clinical semantic information by mapping molecule chemicals via a deep neural network onto the clinical semantic embedding space shared by drugs, diseases and other common cl

SUBMITTER: Wen J 

PROVIDER: S-EPMC9940625 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature