2021年1月18日
A survey on adverse drug reaction studies: data, tasks and machine learning methods
Briefings in Bioinformatics
- ,
- ,
- 巻
- 22
- 号
- 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)
<title>Abstract</title>
<sec>
<title>Motivation</title>
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.
</sec>
<sec>
<title>Results</title>
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.
</sec>
<sec>
<title>Availability</title>
Data and code are available at https://github.com/anhnda/ADRPModels.
</sec>
<sec>
<title>Motivation</title>
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.
</sec>
<sec>
<title>Results</title>
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.
</sec>
<sec>
<title>Availability</title>
Data and code are available at https://github.com/anhnda/ADRPModels.
</sec>
- リンク情報
- ID情報
-
- DOI : 10.1093/bib/bbz140
- ISSN : 1467-5463
- eISSN : 1477-4054
- PubMed ID : 31838499