Machine learning approaches for drug combination therapies

Author:

Güvenç Paltun Betül12,Kaski Samuel123,Mamitsuka Hiroshi124

Affiliation:

1. Department of Computer Science, Aalto University, Espoo, Finland

2. Helsinki Institute for Information Technology (HIIT), Finland

3. University of Manchester, UK

4. Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji 6110011, Japan

Abstract

Abstract 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.

Funder

JST ACCEL

MEXT Kakenhi

Academy of Finland

Finnish Center for Artificial Intelligence FCAI

Publisher

Oxford University Press (OUP)

Subject

Molecular Biology,Information Systems

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