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...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2017-08-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-017-07448-6 |
| _version_ | 1818431037887217664 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-14T15:42:56Z |
| format | Article |
| id | doaj.art-e86ff0bfc6124139b31a2f0eadf7d511 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-12-14T15:42:56Z |
| publishDate | 2017-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-e86ff0bfc6124139b31a2f0eadf7d5112022-12-21T22:55:34ZengNature PortfolioScientific Reports2045-23222017-08-017111210.1038/s41598-017-07448-6CODA: Integrating multi-level context-oriented directed associations for analysis of drug effectsHasun Yu0Jinmyung Jung1Seyeol Yoon2Mijin Kwon3Sunghwa Bae4Soorin Yim5Jaehyun Lee6Seunghyun Kim7Yeeok Kang8Doheon Lee9Department of Bio and Brain EngineeringDepartment of Bio and Brain EngineeringDepartment of Bio and Brain EngineeringDepartment of Bio and Brain EngineeringDepartment of Bio and Brain EngineeringDepartment of Bio and Brain EngineeringDepartment of Bio and Brain EngineeringDepartment of Bio and Brain EngineeringSD Genomics Co., Ltd.Department of Bio and Brain EngineeringAbstract 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.https://doi.org/10.1038/s41598-017-07448-6 |
| spellingShingle | 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 Scientific Reports |
| title | 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_short | 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 |
| url | https://doi.org/10.1038/s41598-017-07448-6 |
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