論文

査読有り 筆頭著者 責任著者
2019年12月14日

A survey on adverse drug reaction studies: data, tasks and machine learning methods

Briefings in Bioinformatics
  • Duc Anh Nguyen
  • ,
  • Canh Hao Nguyen
  • ,
  • Hiroshi Mamitsuka

22
1
開始ページ
164
終了ページ
177
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.1093/bib/bbz140
出版者・発行元
Oxford University Press (OUP)

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.

リンク情報
DOI
https://doi.org/10.1093/bib/bbz140
URL
http://academic.oup.com/bib/article-pdf/22/1/164/35934668/bbz140.pdf
ID情報
  • DOI : 10.1093/bib/bbz140
  • ISSN : 1467-5463
  • eISSN : 1477-4054

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