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SIDD: A Semantically Integrated Database towards a Global View of Human Disease

BACKGROUND: A number of databases have been developed to collect disease-related molecular, phenotypic and environmental features (DR-MPEs), such as genes, non-coding RNAs, genetic variations, drugs, phenotypes and environmental factors. However, each of current databases focused on only one or two...

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Autores principales: Cheng, Liang, Wang, Guohua, Li, Jie, Zhang, Tianjiao, Xu, Peigang, Wang, Yadong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3795748/
https://www.ncbi.nlm.nih.gov/pubmed/24146757
http://dx.doi.org/10.1371/journal.pone.0075504
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author Cheng, Liang
Wang, Guohua
Li, Jie
Zhang, Tianjiao
Xu, Peigang
Wang, Yadong
author_facet Cheng, Liang
Wang, Guohua
Li, Jie
Zhang, Tianjiao
Xu, Peigang
Wang, Yadong
author_sort Cheng, Liang
collection PubMed
description BACKGROUND: A number of databases have been developed to collect disease-related molecular, phenotypic and environmental features (DR-MPEs), such as genes, non-coding RNAs, genetic variations, drugs, phenotypes and environmental factors. However, each of current databases focused on only one or two DR-MPEs. There is an urgent demand to develop an integrated database, which can establish semantic associations among disease-related databases and link them to provide a global view of human disease at the biological level. This database, once developed, will facilitate researchers to query various DR-MPEs through disease, and investigate disease mechanisms from different types of data. METHODOLOGY: To establish an integrated disease-associated database, disease vocabularies used in different databases are mapped to Disease Ontology (DO) through semantic match. 4,284 and 4,186 disease terms from Medical Subject Headings (MeSH) and Online Mendelian Inheritance in Man (OMIM) respectively are mapped to DO. Then, the relationships between DR-MPEs and diseases are extracted and merged from different source databases for reducing the data redundancy. CONCLUSIONS: A semantically integrated disease-associated database (SIDD) is developed, which integrates 18 disease-associated databases, for researchers to browse multiple types of DR-MPEs in a view. A web interface allows easy navigation for querying information through browsing a disease ontology tree or searching a disease term. Furthermore, a network visualization tool using Cytoscape Web plugin has been implemented in SIDD. It enhances the SIDD usage when viewing the relationships between diseases and DR-MPEs. The current version of SIDD (Jul 2013) documents 4,465,131 entries relating to 139,365 DR-MPEs, and to 3,824 human diseases. The database can be freely accessed from: http://mlg.hit.edu.cn/SIDD.
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spelling pubmed-37957482013-10-21 SIDD: A Semantically Integrated Database towards a Global View of Human Disease Cheng, Liang Wang, Guohua Li, Jie Zhang, Tianjiao Xu, Peigang Wang, Yadong PLoS One Research Article BACKGROUND: A number of databases have been developed to collect disease-related molecular, phenotypic and environmental features (DR-MPEs), such as genes, non-coding RNAs, genetic variations, drugs, phenotypes and environmental factors. However, each of current databases focused on only one or two DR-MPEs. There is an urgent demand to develop an integrated database, which can establish semantic associations among disease-related databases and link them to provide a global view of human disease at the biological level. This database, once developed, will facilitate researchers to query various DR-MPEs through disease, and investigate disease mechanisms from different types of data. METHODOLOGY: To establish an integrated disease-associated database, disease vocabularies used in different databases are mapped to Disease Ontology (DO) through semantic match. 4,284 and 4,186 disease terms from Medical Subject Headings (MeSH) and Online Mendelian Inheritance in Man (OMIM) respectively are mapped to DO. Then, the relationships between DR-MPEs and diseases are extracted and merged from different source databases for reducing the data redundancy. CONCLUSIONS: A semantically integrated disease-associated database (SIDD) is developed, which integrates 18 disease-associated databases, for researchers to browse multiple types of DR-MPEs in a view. A web interface allows easy navigation for querying information through browsing a disease ontology tree or searching a disease term. Furthermore, a network visualization tool using Cytoscape Web plugin has been implemented in SIDD. It enhances the SIDD usage when viewing the relationships between diseases and DR-MPEs. The current version of SIDD (Jul 2013) documents 4,465,131 entries relating to 139,365 DR-MPEs, and to 3,824 human diseases. The database can be freely accessed from: http://mlg.hit.edu.cn/SIDD. Public Library of Science 2013-10-11 /pmc/articles/PMC3795748/ /pubmed/24146757 http://dx.doi.org/10.1371/journal.pone.0075504 Text en © 2013 Cheng et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Cheng, Liang
Wang, Guohua
Li, Jie
Zhang, Tianjiao
Xu, Peigang
Wang, Yadong
SIDD: A Semantically Integrated Database towards a Global View of Human Disease
title SIDD: A Semantically Integrated Database towards a Global View of Human Disease
title_full SIDD: A Semantically Integrated Database towards a Global View of Human Disease
title_fullStr SIDD: A Semantically Integrated Database towards a Global View of Human Disease
title_full_unstemmed SIDD: A Semantically Integrated Database towards a Global View of Human Disease
title_short SIDD: A Semantically Integrated Database towards a Global View of Human Disease
title_sort sidd: a semantically integrated database towards a global view of human disease
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3795748/
https://www.ncbi.nlm.nih.gov/pubmed/24146757
http://dx.doi.org/10.1371/journal.pone.0075504
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