Prediction of Side Effects Using Comprehensive Similarity Measures
Joint Authors
Seo, Sukyung
Lee, Taekeon
Kim, Mi-hyun
Yoon, Youngmi
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-02-28
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
Identifying the potential side effects of drugs is crucial in clinical trials in the pharmaceutical industry.
The existing side effect prediction methods mainly focus on the chemical and biological properties of drugs.
This study proposes a method that uses diverse information such as drug-drug interactions from DrugBank, drug-drug interactions from network, single nucleotide polymorphisms, and side effect anatomical hierarchy as well as chemical structures, indications, and targets.
The proposed method is based on the assumption that properties used in drug repositioning studies could be utilized to predict side effects because the phenotypic expression of a side effect is similar to that of the disease.
The prediction results using the proposed method showed a 3.5% improvement in the area under the curve (AUC) over that obtained when only chemical, indication, and target features were used.
The random forest model delivered outstanding results for all combinations of feature types.
Finally, after identifying candidate side effects of drugs using the proposed method, the following four popular drugs were discussed: (1) dasatinib, (2) sitagliptin, (3) vorinostat, and (4) clonidine.
American Psychological Association (APA)
Seo, Sukyung& Lee, Taekeon& Kim, Mi-hyun& Yoon, Youngmi. 2020. Prediction of Side Effects Using Comprehensive Similarity Measures. BioMed Research International،Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1131587
Modern Language Association (MLA)
Seo, Sukyung…[et al.]. Prediction of Side Effects Using Comprehensive Similarity Measures. BioMed Research International No. 2020 (2020), pp.1-10.
https://search.emarefa.net/detail/BIM-1131587
American Medical Association (AMA)
Seo, Sukyung& Lee, Taekeon& Kim, Mi-hyun& Yoon, Youngmi. Prediction of Side Effects Using Comprehensive Similarity Measures. BioMed Research International. 2020. Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1131587
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1131587