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A survey on adverse drug reaction studies: Data, tasks and machine learning methods
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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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en
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14
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Briefings in Bioinformatics, Volume 22, issue 1, pp. 164-177
Abstract
Motivation: Adverse drug reaction (ADR) or drug side effect studies play a crucial role in drug discovery. Recently, with the rapid increase of both clinical and non-clinical data, machine learning methods have emerged as prominent tools to support analyzing and predicting ADRs. Nonetheless, there are still remaining challenges in ADR studies. Results: In this paper, we summarized ADR data sources and review ADR studies in three tasks: Drug-ADR benchmark data creation, drug-ADR prediction and ADR mechanism analysis. We focused on machine learning methods used in each task and then compare performances of the methods on the drug-ADR prediction task. Finally, we discussed open problems for further ADR studies. Availability: Data and code are available at https://github.com/anhnda/ADRPModels.
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Publisher Copyright: © 2019 The Author(s). Copyright: Copyright 2021 Elsevier B.V., All rights reserved.
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Nguyen, D A, Nguyen, C H & Mamitsuka, H 2021, 'A survey on adverse drug reaction studies : Data, tasks and machine learning methods', Briefings in Bioinformatics, vol. 22, no. 1, pp. 164-177. https://doi.org/10.1093/bib/bbz140