A network-based classification model for deriving novel drug-disease associations and assessing their molecular actions.

The growing number and variety of genetic network datasets increases the feasibility of understanding how drugs and diseases are associated at the molecular level. Properly selected features of the network representations of existing drug-disease associations can be used to infer novel indications o...

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Autores principales:	Min Oh, Jaegyoon Ahn, Youngmi Yoon
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/14548046d03b4ece917b742310a02804
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spelling	oai:doaj.org-article:14548046d03b4ece917b742310a028042021-11-25T05:55:01ZA network-based classification model for deriving novel drug-disease associations and assessing their molecular actions.1932-620310.1371/journal.pone.0111668https://doaj.org/article/14548046d03b4ece917b742310a028042014-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0111668https://doaj.org/toc/1932-6203The growing number and variety of genetic network datasets increases the feasibility of understanding how drugs and diseases are associated at the molecular level. Properly selected features of the network representations of existing drug-disease associations can be used to infer novel indications of existing drugs. To find new drug-disease associations, we generated an integrative genetic network using combinations of interactions, including protein-protein interactions and gene regulatory network datasets. Within this network, network adjacencies of drug-drug and disease-disease were quantified using a scored path between target sets of them. Furthermore, the common topological module of drugs or diseases was extracted, and thereby the distance between topological drug-module and disease (or disease-module and drug) was quantified. These quantified scores were used as features for the prediction of novel drug-disease associations. Our classifiers using Random Forest, Multilayer Perceptron and C4.5 showed a high specificity and sensitivity (AUC score of 0.855, 0.828 and 0.797 respectively) in predicting novel drug indications, and displayed a better performance than other methods with limited drug and disease properties. Our predictions and current clinical trials overlap significantly across the different phases of drug development. We also identified and visualized the topological modules of predicted drug indications for certain types of cancers, and for Alzheimer's disease. Within the network, those modules show potential pathways that illustrate the mechanisms of new drug indications, including propranolol as a potential anticancer agent and telmisartan as treatment for Alzheimer's disease.Min OhJaegyoon AhnYoungmi YoonPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 10, p e111668 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Min Oh Jaegyoon Ahn Youngmi Yoon A network-based classification model for deriving novel drug-disease associations and assessing their molecular actions.
description	The growing number and variety of genetic network datasets increases the feasibility of understanding how drugs and diseases are associated at the molecular level. Properly selected features of the network representations of existing drug-disease associations can be used to infer novel indications of existing drugs. To find new drug-disease associations, we generated an integrative genetic network using combinations of interactions, including protein-protein interactions and gene regulatory network datasets. Within this network, network adjacencies of drug-drug and disease-disease were quantified using a scored path between target sets of them. Furthermore, the common topological module of drugs or diseases was extracted, and thereby the distance between topological drug-module and disease (or disease-module and drug) was quantified. These quantified scores were used as features for the prediction of novel drug-disease associations. Our classifiers using Random Forest, Multilayer Perceptron and C4.5 showed a high specificity and sensitivity (AUC score of 0.855, 0.828 and 0.797 respectively) in predicting novel drug indications, and displayed a better performance than other methods with limited drug and disease properties. Our predictions and current clinical trials overlap significantly across the different phases of drug development. We also identified and visualized the topological modules of predicted drug indications for certain types of cancers, and for Alzheimer's disease. Within the network, those modules show potential pathways that illustrate the mechanisms of new drug indications, including propranolol as a potential anticancer agent and telmisartan as treatment for Alzheimer's disease.
format	article
author	Min Oh Jaegyoon Ahn Youngmi Yoon
author_facet	Min Oh Jaegyoon Ahn Youngmi Yoon
author_sort	Min Oh
title	A network-based classification model for deriving novel drug-disease associations and assessing their molecular actions.
title_short	A network-based classification model for deriving novel drug-disease associations and assessing their molecular actions.
title_full	A network-based classification model for deriving novel drug-disease associations and assessing their molecular actions.
title_fullStr	A network-based classification model for deriving novel drug-disease associations and assessing their molecular actions.
title_full_unstemmed	A network-based classification model for deriving novel drug-disease associations and assessing their molecular actions.
title_sort	network-based classification model for deriving novel drug-disease associations and assessing their molecular actions.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/14548046d03b4ece917b742310a02804
work_keys_str_mv	AT minoh anetworkbasedclassificationmodelforderivingnoveldrugdiseaseassociationsandassessingtheirmolecularactions AT jaegyoonahn anetworkbasedclassificationmodelforderivingnoveldrugdiseaseassociationsandassessingtheirmolecularactions AT youngmiyoon anetworkbasedclassificationmodelforderivingnoveldrugdiseaseassociationsandassessingtheirmolecularactions AT minoh networkbasedclassificationmodelforderivingnoveldrugdiseaseassociationsandassessingtheirmolecularactions AT jaegyoonahn networkbasedclassificationmodelforderivingnoveldrugdiseaseassociationsandassessingtheirmolecularactions AT youngmiyoon networkbasedclassificationmodelforderivingnoveldrugdiseaseassociationsandassessingtheirmolecularactions
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A network-based classification model for deriving novel drug-disease associations and assessing their molecular actions.

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