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ARN: analysis and prediction by adipogenic professional database

Adipogenesis is the process of cell differentiation by which mesenchymal stem cells become adipocytes. Extensive research is ongoing to identify genes, their protein products, and microRNAs that correlate with fat cell development. The existing databases have focused on certain types of regulatory f...

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Detalles Bibliográficos
Autores principales: Huang, Yan, Wang, Li, Zan, and Lin-sen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4977645/
https://www.ncbi.nlm.nih.gov/pubmed/27503118
http://dx.doi.org/10.1186/s12918-016-0321-0
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author Huang, Yan
Wang, Li
Zan, and Lin-sen
author_facet Huang, Yan
Wang, Li
Zan, and Lin-sen
author_sort Huang, Yan
collection PubMed
description Adipogenesis is the process of cell differentiation by which mesenchymal stem cells become adipocytes. Extensive research is ongoing to identify genes, their protein products, and microRNAs that correlate with fat cell development. The existing databases have focused on certain types of regulatory factors and interactions. However, there is no relationship between the results of the experimental studies on adipogenesis and these databases because of the lack of an information center. This information fragmentation hampers the identification of key regulatory genes and pathways. Thus, it is necessary to provide an information center that is quickly and easily accessible to researchers in this field. We selected and integrated data from eight external databases based on the results of text-mining, and constructed a publicly available database and web interface (URL: http://210.27.80.93/arn/), which contained 30873 records related to adipogenic differentiation. Then, we designed an online analysis tool to analyze the experimental data or form a scientific hypothesis about adipogenesis through Swanson’s literature-based discovery process. Furthermore, we calculated the “Impact Factor” (“IF”) value that reflects the importance of each node by counting the numbers of relation records, expression records, and prediction records for each node. This platform can support ongoing adipogenesis research and contribute to the discovery of key regulatory genes and pathways. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12918-016-0321-0) contains supplementary material, which is available to authorized users.
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spelling pubmed-49776452016-08-10 ARN: analysis and prediction by adipogenic professional database Huang, Yan Wang, Li Zan, and Lin-sen BMC Syst Biol Database Adipogenesis is the process of cell differentiation by which mesenchymal stem cells become adipocytes. Extensive research is ongoing to identify genes, their protein products, and microRNAs that correlate with fat cell development. The existing databases have focused on certain types of regulatory factors and interactions. However, there is no relationship between the results of the experimental studies on adipogenesis and these databases because of the lack of an information center. This information fragmentation hampers the identification of key regulatory genes and pathways. Thus, it is necessary to provide an information center that is quickly and easily accessible to researchers in this field. We selected and integrated data from eight external databases based on the results of text-mining, and constructed a publicly available database and web interface (URL: http://210.27.80.93/arn/), which contained 30873 records related to adipogenic differentiation. Then, we designed an online analysis tool to analyze the experimental data or form a scientific hypothesis about adipogenesis through Swanson’s literature-based discovery process. Furthermore, we calculated the “Impact Factor” (“IF”) value that reflects the importance of each node by counting the numbers of relation records, expression records, and prediction records for each node. This platform can support ongoing adipogenesis research and contribute to the discovery of key regulatory genes and pathways. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12918-016-0321-0) contains supplementary material, which is available to authorized users. BioMed Central 2016-08-08 /pmc/articles/PMC4977645/ /pubmed/27503118 http://dx.doi.org/10.1186/s12918-016-0321-0 Text en © The Author(s). 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Database
Huang, Yan
Wang, Li
Zan, and Lin-sen
ARN: analysis and prediction by adipogenic professional database
title ARN: analysis and prediction by adipogenic professional database
title_full ARN: analysis and prediction by adipogenic professional database
title_fullStr ARN: analysis and prediction by adipogenic professional database
title_full_unstemmed ARN: analysis and prediction by adipogenic professional database
title_short ARN: analysis and prediction by adipogenic professional database
title_sort arn: analysis and prediction by adipogenic professional database
topic Database
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4977645/
https://www.ncbi.nlm.nih.gov/pubmed/27503118
http://dx.doi.org/10.1186/s12918-016-0321-0
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