Machine learning workflow to enhance predictions of Adverse Drug Reactions (ADRs) through drug-gene interactions: application to drugs for cutaneous diseases

Abstract Adverse drug reactions (ADRs) pose critical public health issues, affecting over 6% of hospitalized patients. While knowledge of potential drug-drug interactions (DDI) is necessary to prevent ADR, the rapid pace of drug discovery makes it challenging to maintain a strong insight into DDIs....

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Autores principales:	Kalpana Raja, Matthew Patrick, James T. Elder, Lam C. Tsoi
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/4ba7224201d24ca9b7d08693a91a8245
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spelling	oai:doaj.org-article:4ba7224201d24ca9b7d08693a91a82452021-12-02T12:31:48ZMachine learning workflow to enhance predictions of Adverse Drug Reactions (ADRs) through drug-gene interactions: application to drugs for cutaneous diseases10.1038/s41598-017-03914-32045-2322https://doaj.org/article/4ba7224201d24ca9b7d08693a91a82452017-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-03914-3https://doaj.org/toc/2045-2322Abstract Adverse drug reactions (ADRs) pose critical public health issues, affecting over 6% of hospitalized patients. While knowledge of potential drug-drug interactions (DDI) is necessary to prevent ADR, the rapid pace of drug discovery makes it challenging to maintain a strong insight into DDIs. In this study, we present a novel literature-mining framework for enhancing the predictions of DDIs and ADR types by integrating drug-gene interactions (DGIs). The ADR types were adapted from a DDI corpus, including i) adverse effect; ii) effect at molecular level; iii) effect related to pharmacokinetics; and iv) DDIs without known ADRs. By using random forest classifier our approach achieves an F-score of 0.87 across the ADRs classification using only the DDI features. We then enhanced the performance of the classifier by including DGIs (F-score = 0.90), and applied the classification model trained with the DDI corpus to identify the drugs that might interact with the drugs for cutaneous diseases. We successfully predict previously known ADRs for drugs prescribed to cutaneous diseases, and are also able to identify promising new ADRs.Kalpana RajaMatthew PatrickJames T. ElderLam C. TsoiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-11 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Kalpana Raja Matthew Patrick James T. Elder Lam C. Tsoi Machine learning workflow to enhance predictions of Adverse Drug Reactions (ADRs) through drug-gene interactions: application to drugs for cutaneous diseases
description	Abstract Adverse drug reactions (ADRs) pose critical public health issues, affecting over 6% of hospitalized patients. While knowledge of potential drug-drug interactions (DDI) is necessary to prevent ADR, the rapid pace of drug discovery makes it challenging to maintain a strong insight into DDIs. In this study, we present a novel literature-mining framework for enhancing the predictions of DDIs and ADR types by integrating drug-gene interactions (DGIs). The ADR types were adapted from a DDI corpus, including i) adverse effect; ii) effect at molecular level; iii) effect related to pharmacokinetics; and iv) DDIs without known ADRs. By using random forest classifier our approach achieves an F-score of 0.87 across the ADRs classification using only the DDI features. We then enhanced the performance of the classifier by including DGIs (F-score = 0.90), and applied the classification model trained with the DDI corpus to identify the drugs that might interact with the drugs for cutaneous diseases. We successfully predict previously known ADRs for drugs prescribed to cutaneous diseases, and are also able to identify promising new ADRs.
format	article
author	Kalpana Raja Matthew Patrick James T. Elder Lam C. Tsoi
author_facet	Kalpana Raja Matthew Patrick James T. Elder Lam C. Tsoi
author_sort	Kalpana Raja
title	Machine learning workflow to enhance predictions of Adverse Drug Reactions (ADRs) through drug-gene interactions: application to drugs for cutaneous diseases
title_short	Machine learning workflow to enhance predictions of Adverse Drug Reactions (ADRs) through drug-gene interactions: application to drugs for cutaneous diseases
title_full	Machine learning workflow to enhance predictions of Adverse Drug Reactions (ADRs) through drug-gene interactions: application to drugs for cutaneous diseases
title_fullStr	Machine learning workflow to enhance predictions of Adverse Drug Reactions (ADRs) through drug-gene interactions: application to drugs for cutaneous diseases
title_full_unstemmed	Machine learning workflow to enhance predictions of Adverse Drug Reactions (ADRs) through drug-gene interactions: application to drugs for cutaneous diseases
title_sort	machine learning workflow to enhance predictions of adverse drug reactions (adrs) through drug-gene interactions: application to drugs for cutaneous diseases
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/4ba7224201d24ca9b7d08693a91a8245
work_keys_str_mv	AT kalpanaraja machinelearningworkflowtoenhancepredictionsofadversedrugreactionsadrsthroughdruggeneinteractionsapplicationtodrugsforcutaneousdiseases AT matthewpatrick machinelearningworkflowtoenhancepredictionsofadversedrugreactionsadrsthroughdruggeneinteractionsapplicationtodrugsforcutaneousdiseases AT jamestelder machinelearningworkflowtoenhancepredictionsofadversedrugreactionsadrsthroughdruggeneinteractionsapplicationtodrugsforcutaneousdiseases AT lamctsoi machinelearningworkflowtoenhancepredictionsofadversedrugreactionsadrsthroughdruggeneinteractionsapplicationtodrugsforcutaneousdiseases
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Machine learning workflow to enhance predictions of Adverse Drug Reactions (ADRs) through drug-gene interactions: application to drugs for cutaneous diseases

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