Multimodal representation learning for predicting molecule-disease relations.
Bioinformatics
; 39(2)2023 02 03.
Article
de En
| MEDLINE
| ID: mdl-36805623
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 clinical concepts. To infer molecule-disease relations, M2REMAP combines multimodal molecule representation and disease semantic embedding to jointly infer indications and side effects.RESULTS:
We extensively evaluate M2REMAP on molecule indications, side effects and interactions. Results show that incorporating EHR embeddings improves performance significantly, for example, attaining an improvement over the baseline models by 23.6% in PRC-AUC on indications and 23.9% on side effects. Further, M2REMAP overcomes the limitation of existing methods and effectively predicts drugs for novel diseases and emerging pathogens. AVAILABILITY AND IMPLEMENTATION The code is available at https//github.com/celehs/M2REMAP, and prediction results are provided at https//shiny.parse-health.org/drugs-diseases-dev/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Texte intégral:
1
Collection:
01-internacional
Base de données:
MEDLINE
Sujet principal:
Effets secondaires indésirables des médicaments
Type d'étude:
Prognostic_studies
/
Risk_factors_studies
Limites:
Humans
Langue:
En
Journal:
Bioinformatics
Sujet du journal:
INFORMATICA MEDICA
Année:
2023
Type de document:
Article
Pays d'affiliation:
États-Unis d'Amérique