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Prediction of Drug Side Effects with a Refined Negative Sample Selection Strategy

Drugs are an important way to treat various diseases. However, they inevitably produce side effects, bringing great risks to human bodies and pharmaceutical companies. How to predict the side effects of drugs has become one of the essential problems in drug research. Designing efficient computationa...

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Detalles Bibliográficos
Autores principales: Liang, Haiyan, Chen, Lei, Zhao, Xian, Zhang, Xiaolin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7232712/
https://www.ncbi.nlm.nih.gov/pubmed/32454877
http://dx.doi.org/10.1155/2020/1573543
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author Liang, Haiyan
Chen, Lei
Zhao, Xian
Zhang, Xiaolin
author_facet Liang, Haiyan
Chen, Lei
Zhao, Xian
Zhang, Xiaolin
author_sort Liang, Haiyan
collection PubMed
description Drugs are an important way to treat various diseases. However, they inevitably produce side effects, bringing great risks to human bodies and pharmaceutical companies. How to predict the side effects of drugs has become one of the essential problems in drug research. Designing efficient computational methods is an alternative way. Some studies paired the drug and side effect as a sample, thereby modeling the problem as a binary classification problem. However, the selection of negative samples is a key problem in this case. In this study, a novel negative sample selection strategy was designed for accessing high-quality negative samples. Such strategy applied the random walk with restart (RWR) algorithm on a chemical-chemical interaction network to select pairs of drugs and side effects, such that drugs were less likely to have corresponding side effects, as negative samples. Through several tests with a fixed feature extraction scheme and different machine-learning algorithms, models with selected negative samples produced high performance. The best model even yielded nearly perfect performance. These models had much higher performance than those without such strategy or with another selection strategy. Furthermore, it is not necessary to consider the balance of positive and negative samples under such a strategy.
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spelling pubmed-72327122020-05-23 Prediction of Drug Side Effects with a Refined Negative Sample Selection Strategy Liang, Haiyan Chen, Lei Zhao, Xian Zhang, Xiaolin Comput Math Methods Med Research Article Drugs are an important way to treat various diseases. However, they inevitably produce side effects, bringing great risks to human bodies and pharmaceutical companies. How to predict the side effects of drugs has become one of the essential problems in drug research. Designing efficient computational methods is an alternative way. Some studies paired the drug and side effect as a sample, thereby modeling the problem as a binary classification problem. However, the selection of negative samples is a key problem in this case. In this study, a novel negative sample selection strategy was designed for accessing high-quality negative samples. Such strategy applied the random walk with restart (RWR) algorithm on a chemical-chemical interaction network to select pairs of drugs and side effects, such that drugs were less likely to have corresponding side effects, as negative samples. Through several tests with a fixed feature extraction scheme and different machine-learning algorithms, models with selected negative samples produced high performance. The best model even yielded nearly perfect performance. These models had much higher performance than those without such strategy or with another selection strategy. Furthermore, it is not necessary to consider the balance of positive and negative samples under such a strategy. Hindawi 2020-05-09 /pmc/articles/PMC7232712/ /pubmed/32454877 http://dx.doi.org/10.1155/2020/1573543 Text en Copyright © 2020 Haiyan Liang et al. http://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
Liang, Haiyan
Chen, Lei
Zhao, Xian
Zhang, Xiaolin
Prediction of Drug Side Effects with a Refined Negative Sample Selection Strategy
title Prediction of Drug Side Effects with a Refined Negative Sample Selection Strategy
title_full Prediction of Drug Side Effects with a Refined Negative Sample Selection Strategy
title_fullStr Prediction of Drug Side Effects with a Refined Negative Sample Selection Strategy
title_full_unstemmed Prediction of Drug Side Effects with a Refined Negative Sample Selection Strategy
title_short Prediction of Drug Side Effects with a Refined Negative Sample Selection Strategy
title_sort prediction of drug side effects with a refined negative sample selection strategy
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7232712/
https://www.ncbi.nlm.nih.gov/pubmed/32454877
http://dx.doi.org/10.1155/2020/1573543
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