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Interpretable Drug-to-Drug Network Features for Predicting Adverse Drug Reactions

Recent years have witnessed booming data on drugs and their associated adverse drug reactions (ADRs). It was reported that these ADRs have resulted in a high hospitalisation rate worldwide. Therefore, a tremendous amount of research has been carried out to predict ADRs in the early phases of drug de...

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Autores principales: Zhou, Fangyu, Uddin, Shahadat
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9957267/
https://www.ncbi.nlm.nih.gov/pubmed/36833144
http://dx.doi.org/10.3390/healthcare11040610
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author Zhou, Fangyu
Uddin, Shahadat
author_facet Zhou, Fangyu
Uddin, Shahadat
author_sort Zhou, Fangyu
collection PubMed
description Recent years have witnessed booming data on drugs and their associated adverse drug reactions (ADRs). It was reported that these ADRs have resulted in a high hospitalisation rate worldwide. Therefore, a tremendous amount of research has been carried out to predict ADRs in the early phases of drug development, with the goal of reducing possible future risks. The pre-clinical and clinical phases of drug research can be time-consuming and cost-ineffective, so academics are looking forward to more extensive data mining and machine learning methods to be applied in this field of study. In this paper, we try to construct a drug-to-drug network based on non-clinical data sources. The network presents underlying relationships between drug pairs according to their common ADRs. Then, multiple node-level and graph-level network features are extracted from this network, e.g., weighted degree centrality, weighted PageRanks, etc. After concatenating the network features to the original drug features, they were fed into seven machine learning models, e.g., logistic regression, random forest, support vector machine, etc., and were compared to the baseline, where there were no network-based features considered. These experiments indicate that all the tested machine-learning methods would benefit from adding these network features. Among all these models, logistic regression (LR) had the highest mean AUROC score (82.1%) across all ADRs tested. Weighted degree centrality and weighted PageRanks were identified to be the most critical network features in the LR classifier. These pieces of evidence strongly indicate that the network approach can be vital in future ADR prediction, and this network-based approach could also be applied to other health informatics datasets.
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spelling pubmed-99572672023-02-25 Interpretable Drug-to-Drug Network Features for Predicting Adverse Drug Reactions Zhou, Fangyu Uddin, Shahadat Healthcare (Basel) Article Recent years have witnessed booming data on drugs and their associated adverse drug reactions (ADRs). It was reported that these ADRs have resulted in a high hospitalisation rate worldwide. Therefore, a tremendous amount of research has been carried out to predict ADRs in the early phases of drug development, with the goal of reducing possible future risks. The pre-clinical and clinical phases of drug research can be time-consuming and cost-ineffective, so academics are looking forward to more extensive data mining and machine learning methods to be applied in this field of study. In this paper, we try to construct a drug-to-drug network based on non-clinical data sources. The network presents underlying relationships between drug pairs according to their common ADRs. Then, multiple node-level and graph-level network features are extracted from this network, e.g., weighted degree centrality, weighted PageRanks, etc. After concatenating the network features to the original drug features, they were fed into seven machine learning models, e.g., logistic regression, random forest, support vector machine, etc., and were compared to the baseline, where there were no network-based features considered. These experiments indicate that all the tested machine-learning methods would benefit from adding these network features. Among all these models, logistic regression (LR) had the highest mean AUROC score (82.1%) across all ADRs tested. Weighted degree centrality and weighted PageRanks were identified to be the most critical network features in the LR classifier. These pieces of evidence strongly indicate that the network approach can be vital in future ADR prediction, and this network-based approach could also be applied to other health informatics datasets. MDPI 2023-02-17 /pmc/articles/PMC9957267/ /pubmed/36833144 http://dx.doi.org/10.3390/healthcare11040610 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zhou, Fangyu
Uddin, Shahadat
Interpretable Drug-to-Drug Network Features for Predicting Adverse Drug Reactions
title Interpretable Drug-to-Drug Network Features for Predicting Adverse Drug Reactions
title_full Interpretable Drug-to-Drug Network Features for Predicting Adverse Drug Reactions
title_fullStr Interpretable Drug-to-Drug Network Features for Predicting Adverse Drug Reactions
title_full_unstemmed Interpretable Drug-to-Drug Network Features for Predicting Adverse Drug Reactions
title_short Interpretable Drug-to-Drug Network Features for Predicting Adverse Drug Reactions
title_sort interpretable drug-to-drug network features for predicting adverse drug reactions
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9957267/
https://www.ncbi.nlm.nih.gov/pubmed/36833144
http://dx.doi.org/10.3390/healthcare11040610
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