CODA: Integrating multi-level context-oriented directed associations for analysis of drug effects

Abstract In silico network-based methods have shown promising results in the field of drug development. Yet, most of networks used in the previous research have not included context information even though biological associations actually do appear in the specific contexts. Here, we reconstruct an a...

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Autores principales:	Hasun Yu, Jinmyung Jung, Seyeol Yoon, Mijin Kwon, Sunghwa Bae, Soorin Yim, Jaehyun Lee, Seunghyun Kim, Yeeok Kang, Doheon Lee
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e86ff0bfc6124139b31a2f0eadf7d511
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spelling	oai:doaj.org-article:e86ff0bfc6124139b31a2f0eadf7d5112021-12-02T11:52:19ZCODA: Integrating multi-level context-oriented directed associations for analysis of drug effects10.1038/s41598-017-07448-62045-2322https://doaj.org/article/e86ff0bfc6124139b31a2f0eadf7d5112017-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-07448-6https://doaj.org/toc/2045-2322Abstract In silico network-based methods have shown promising results in the field of drug development. Yet, most of networks used in the previous research have not included context information even though biological associations actually do appear in the specific contexts. Here, we reconstruct an anatomical context-specific network by assigning contexts to biological associations using protein expression data and scientific literature. Furthermore, we employ the context-specific network for the analysis of drug effects with a proximity measure between drug targets and diseases. Distinct from previous context-specific networks, intercellular associations and phenomic level entities such as biological processes are included in our network to represent the human body. It is observed that performances in inferring drug-disease associations are increased by adding context information and phenomic level entities. In particular, hypertension, a disease related to multiple organs and associated with several phenomic level entities, is analyzed in detail to investigate how our network facilitates the inference of drug-disease associations. Our results indicate that the inclusion of context information, intercellular associations, and phenomic level entities can contribute towards a better prediction of drug-disease associations and provide detailed insight into understanding of how drugs affect diseases in the human body.Hasun YuJinmyung JungSeyeol YoonMijin KwonSunghwa BaeSoorin YimJaehyun LeeSeunghyun KimYeeok KangDoheon LeeNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-12 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Hasun Yu Jinmyung Jung Seyeol Yoon Mijin Kwon Sunghwa Bae Soorin Yim Jaehyun Lee Seunghyun Kim Yeeok Kang Doheon Lee CODA: Integrating multi-level context-oriented directed associations for analysis of drug effects
description	Abstract In silico network-based methods have shown promising results in the field of drug development. Yet, most of networks used in the previous research have not included context information even though biological associations actually do appear in the specific contexts. Here, we reconstruct an anatomical context-specific network by assigning contexts to biological associations using protein expression data and scientific literature. Furthermore, we employ the context-specific network for the analysis of drug effects with a proximity measure between drug targets and diseases. Distinct from previous context-specific networks, intercellular associations and phenomic level entities such as biological processes are included in our network to represent the human body. It is observed that performances in inferring drug-disease associations are increased by adding context information and phenomic level entities. In particular, hypertension, a disease related to multiple organs and associated with several phenomic level entities, is analyzed in detail to investigate how our network facilitates the inference of drug-disease associations. Our results indicate that the inclusion of context information, intercellular associations, and phenomic level entities can contribute towards a better prediction of drug-disease associations and provide detailed insight into understanding of how drugs affect diseases in the human body.
format	article
author	Hasun Yu Jinmyung Jung Seyeol Yoon Mijin Kwon Sunghwa Bae Soorin Yim Jaehyun Lee Seunghyun Kim Yeeok Kang Doheon Lee
author_facet	Hasun Yu Jinmyung Jung Seyeol Yoon Mijin Kwon Sunghwa Bae Soorin Yim Jaehyun Lee Seunghyun Kim Yeeok Kang Doheon Lee
author_sort	Hasun Yu
title	CODA: Integrating multi-level context-oriented directed associations for analysis of drug effects
title_short	CODA: Integrating multi-level context-oriented directed associations for analysis of drug effects
title_full	CODA: Integrating multi-level context-oriented directed associations for analysis of drug effects
title_fullStr	CODA: Integrating multi-level context-oriented directed associations for analysis of drug effects
title_full_unstemmed	CODA: Integrating multi-level context-oriented directed associations for analysis of drug effects
title_sort	coda: integrating multi-level context-oriented directed associations for analysis of drug effects
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/e86ff0bfc6124139b31a2f0eadf7d511
work_keys_str_mv	AT hasunyu codaintegratingmultilevelcontextorienteddirectedassociationsforanalysisofdrugeffects AT jinmyungjung codaintegratingmultilevelcontextorienteddirectedassociationsforanalysisofdrugeffects AT seyeolyoon codaintegratingmultilevelcontextorienteddirectedassociationsforanalysisofdrugeffects AT mijinkwon codaintegratingmultilevelcontextorienteddirectedassociationsforanalysisofdrugeffects AT sunghwabae codaintegratingmultilevelcontextorienteddirectedassociationsforanalysisofdrugeffects AT soorinyim codaintegratingmultilevelcontextorienteddirectedassociationsforanalysisofdrugeffects AT jaehyunlee codaintegratingmultilevelcontextorienteddirectedassociationsforanalysisofdrugeffects AT seunghyunkim codaintegratingmultilevelcontextorienteddirectedassociationsforanalysisofdrugeffects AT yeeokkang codaintegratingmultilevelcontextorienteddirectedassociationsforanalysisofdrugeffects AT doheonlee codaintegratingmultilevelcontextorienteddirectedassociationsforanalysisofdrugeffects
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CODA: Integrating multi-level context-oriented directed associations for analysis of drug effects

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