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PosMed: ranking genes and bioresources based on Semantic Web Association Study

Positional MEDLINE (PosMed; http://biolod.org/PosMed) is a powerful Semantic Web Association Study engine that ranks biomedical resources such as genes, metabolites, diseases and drugs, based on the statistical significance of associations between user-specified phenotypic keywords and resources con...

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Autores principales: Makita, Yuko, Kobayashi, Norio, Yoshida, Yuko, Doi, Koji, Mochizuki, Yoshiki, Nishikata, Koro, Matsushima, Akihiro, Takahashi, Satoshi, Ishii, Manabu, Takatsuki, Terue, Bhatia, Rinki, Khadbaatar, Zolzaya, Watabe, Hajime, Masuya, Hiroshi, Toyoda, Tetsuro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3692089/
https://www.ncbi.nlm.nih.gov/pubmed/23761449
http://dx.doi.org/10.1093/nar/gkt474
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author Makita, Yuko
Kobayashi, Norio
Yoshida, Yuko
Doi, Koji
Mochizuki, Yoshiki
Nishikata, Koro
Matsushima, Akihiro
Takahashi, Satoshi
Ishii, Manabu
Takatsuki, Terue
Bhatia, Rinki
Khadbaatar, Zolzaya
Watabe, Hajime
Masuya, Hiroshi
Toyoda, Tetsuro
author_facet Makita, Yuko
Kobayashi, Norio
Yoshida, Yuko
Doi, Koji
Mochizuki, Yoshiki
Nishikata, Koro
Matsushima, Akihiro
Takahashi, Satoshi
Ishii, Manabu
Takatsuki, Terue
Bhatia, Rinki
Khadbaatar, Zolzaya
Watabe, Hajime
Masuya, Hiroshi
Toyoda, Tetsuro
author_sort Makita, Yuko
collection PubMed
description Positional MEDLINE (PosMed; http://biolod.org/PosMed) is a powerful Semantic Web Association Study engine that ranks biomedical resources such as genes, metabolites, diseases and drugs, based on the statistical significance of associations between user-specified phenotypic keywords and resources connected directly or inferentially through a Semantic Web of biological databases such as MEDLINE, OMIM, pathways, co-expressions, molecular interactions and ontology terms. Since 2005, PosMed has long been used for in silico positional cloning studies to infer candidate disease-responsible genes existing within chromosomal intervals. PosMed is redesigned as a workbench to discover possible functional interpretations for numerous genetic variants found from exome sequencing of human disease samples. We also show that the association search engine enhances the value of mouse bioresources because most knockout mouse resources have no phenotypic annotation, but can be associated inferentially to phenotypes via genes and biomedical documents. For this purpose, we established text-mining rules to the biomedical documents by careful human curation work, and created a huge amount of correct linking between genes and documents. PosMed associates any phenotypic keyword to mouse resources with 20 public databases and four original data sets as of May 2013.
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spelling pubmed-36920892013-06-25 PosMed: ranking genes and bioresources based on Semantic Web Association Study Makita, Yuko Kobayashi, Norio Yoshida, Yuko Doi, Koji Mochizuki, Yoshiki Nishikata, Koro Matsushima, Akihiro Takahashi, Satoshi Ishii, Manabu Takatsuki, Terue Bhatia, Rinki Khadbaatar, Zolzaya Watabe, Hajime Masuya, Hiroshi Toyoda, Tetsuro Nucleic Acids Res Articles Positional MEDLINE (PosMed; http://biolod.org/PosMed) is a powerful Semantic Web Association Study engine that ranks biomedical resources such as genes, metabolites, diseases and drugs, based on the statistical significance of associations between user-specified phenotypic keywords and resources connected directly or inferentially through a Semantic Web of biological databases such as MEDLINE, OMIM, pathways, co-expressions, molecular interactions and ontology terms. Since 2005, PosMed has long been used for in silico positional cloning studies to infer candidate disease-responsible genes existing within chromosomal intervals. PosMed is redesigned as a workbench to discover possible functional interpretations for numerous genetic variants found from exome sequencing of human disease samples. We also show that the association search engine enhances the value of mouse bioresources because most knockout mouse resources have no phenotypic annotation, but can be associated inferentially to phenotypes via genes and biomedical documents. For this purpose, we established text-mining rules to the biomedical documents by careful human curation work, and created a huge amount of correct linking between genes and documents. PosMed associates any phenotypic keyword to mouse resources with 20 public databases and four original data sets as of May 2013. Oxford University Press 2013-07 2013-06-11 /pmc/articles/PMC3692089/ /pubmed/23761449 http://dx.doi.org/10.1093/nar/gkt474 Text en © The Author(s) 2013. Published by Oxford University Press. http://creativecommons.org/licenses/by/3.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Articles
Makita, Yuko
Kobayashi, Norio
Yoshida, Yuko
Doi, Koji
Mochizuki, Yoshiki
Nishikata, Koro
Matsushima, Akihiro
Takahashi, Satoshi
Ishii, Manabu
Takatsuki, Terue
Bhatia, Rinki
Khadbaatar, Zolzaya
Watabe, Hajime
Masuya, Hiroshi
Toyoda, Tetsuro
PosMed: ranking genes and bioresources based on Semantic Web Association Study
title PosMed: ranking genes and bioresources based on Semantic Web Association Study
title_full PosMed: ranking genes and bioresources based on Semantic Web Association Study
title_fullStr PosMed: ranking genes and bioresources based on Semantic Web Association Study
title_full_unstemmed PosMed: ranking genes and bioresources based on Semantic Web Association Study
title_short PosMed: ranking genes and bioresources based on Semantic Web Association Study
title_sort posmed: ranking genes and bioresources based on semantic web association study
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3692089/
https://www.ncbi.nlm.nih.gov/pubmed/23761449
http://dx.doi.org/10.1093/nar/gkt474
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