Machine learning approaches for drug combination therapies.
bioinformatics
data integration
drug combination therapy
machine learning
personalized medicine
Journal
Briefings in bioinformatics
ISSN: 1477-4054
Titre abrégé: Brief Bioinform
Pays: England
ID NLM: 100912837
Informations de publication
Date de publication:
05 11 2021
05 11 2021
Historique:
received:
13
04
2021
revised:
08
07
2021
accepted:
14
07
2021
pubmed:
10
8
2021
medline:
11
3
2022
entrez:
9
8
2021
Statut:
ppublish
Résumé
Drug combination therapy is a promising strategy to treat complex diseases such as cancer and infectious diseases. However, current knowledge of drug combination therapies, especially in cancer patients, is limited because of adverse drug effects, toxicity and cell line heterogeneity. Screening new drug combinations requires substantial efforts since considering all possible combinations between drugs is infeasible and expensive. Therefore, building computational approaches, particularly machine learning methods, could provide an effective strategy to overcome drug resistance and improve therapeutic efficacy. In this review, we group the state-of-the-art machine learning approaches to analyze personalized drug combination therapies into three categories and discuss each method in each category. We also present a short description of relevant databases used as a benchmark in drug combination therapies and provide a list of well-known, publicly available interactive data analysis portals. We highlight the importance of data integration on the identification of drug combinations. Finally, we address the advantages of combining multiple data sources on drug combination analysis by showing an experimental comparison.
Identifiants
pubmed: 34368832
pii: 6343528
doi: 10.1093/bib/bbab293
pmc: PMC8574999
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Review
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© The Author(s) 2021. Published by Oxford University Press.
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