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Similarity-Based Method with Multiple-Feature Sampling for Predicting Drug Side Effects

Drugs can treat different diseases but also bring side effects. Undetected and unaccepted side effects for approved drugs can greatly harm the human body and bring huge risks for pharmaceutical companies. Traditional experimental methods used to determine the side effects have several drawbacks, suc...

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Detalles Bibliográficos
Autores principales: Wu, Zixin, Chen, Lei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8993545/
https://www.ncbi.nlm.nih.gov/pubmed/35401786
http://dx.doi.org/10.1155/2022/9547317
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author Wu, Zixin
Chen, Lei
author_facet Wu, Zixin
Chen, Lei
author_sort Wu, Zixin
collection PubMed
description Drugs can treat different diseases but also bring side effects. Undetected and unaccepted side effects for approved drugs can greatly harm the human body and bring huge risks for pharmaceutical companies. Traditional experimental methods used to determine the side effects have several drawbacks, such as low efficiency and high cost. One alternative to achieve this purpose is to design computational methods. Previous studies modeled a binary classification problem by pairing drugs and side effects; however, their classifiers can only extract one feature from each type of drug association. The present work proposed a novel multiple-feature sampling scheme that can extract several features from one type of drug association. Thirteen classification algorithms were employed to construct classifiers with features yielded by such scheme. Their performance was greatly improved compared with that of the classifiers that use the features yielded by the original scheme. Best performance was observed for the classifier based on random forest with MCC of 0.8661, AUROC of 0.969, and AUPR of 0.977. Finally, one key parameter in the multiple-feature sampling scheme was analyzed.
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spelling pubmed-89935452022-04-09 Similarity-Based Method with Multiple-Feature Sampling for Predicting Drug Side Effects Wu, Zixin Chen, Lei Comput Math Methods Med Research Article Drugs can treat different diseases but also bring side effects. Undetected and unaccepted side effects for approved drugs can greatly harm the human body and bring huge risks for pharmaceutical companies. Traditional experimental methods used to determine the side effects have several drawbacks, such as low efficiency and high cost. One alternative to achieve this purpose is to design computational methods. Previous studies modeled a binary classification problem by pairing drugs and side effects; however, their classifiers can only extract one feature from each type of drug association. The present work proposed a novel multiple-feature sampling scheme that can extract several features from one type of drug association. Thirteen classification algorithms were employed to construct classifiers with features yielded by such scheme. Their performance was greatly improved compared with that of the classifiers that use the features yielded by the original scheme. Best performance was observed for the classifier based on random forest with MCC of 0.8661, AUROC of 0.969, and AUPR of 0.977. Finally, one key parameter in the multiple-feature sampling scheme was analyzed. Hindawi 2022-04-01 /pmc/articles/PMC8993545/ /pubmed/35401786 http://dx.doi.org/10.1155/2022/9547317 Text en Copyright © 2022 Zixin Wu and Lei Chen. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Wu, Zixin
Chen, Lei
Similarity-Based Method with Multiple-Feature Sampling for Predicting Drug Side Effects
title Similarity-Based Method with Multiple-Feature Sampling for Predicting Drug Side Effects
title_full Similarity-Based Method with Multiple-Feature Sampling for Predicting Drug Side Effects
title_fullStr Similarity-Based Method with Multiple-Feature Sampling for Predicting Drug Side Effects
title_full_unstemmed Similarity-Based Method with Multiple-Feature Sampling for Predicting Drug Side Effects
title_short Similarity-Based Method with Multiple-Feature Sampling for Predicting Drug Side Effects
title_sort similarity-based method with multiple-feature sampling for predicting drug side effects
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8993545/
https://www.ncbi.nlm.nih.gov/pubmed/35401786
http://dx.doi.org/10.1155/2022/9547317
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