Cargando…
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...
Autores principales: | , , , , , , , , , , , , , , |
---|---|
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 |
_version_ | 1782274566994788352 |
---|---|
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. |
format | Online Article Text |
id | pubmed-3692089 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT makitayuko posmedrankinggenesandbioresourcesbasedonsemanticwebassociationstudy AT kobayashinorio posmedrankinggenesandbioresourcesbasedonsemanticwebassociationstudy AT yoshidayuko posmedrankinggenesandbioresourcesbasedonsemanticwebassociationstudy AT doikoji posmedrankinggenesandbioresourcesbasedonsemanticwebassociationstudy AT mochizukiyoshiki posmedrankinggenesandbioresourcesbasedonsemanticwebassociationstudy AT nishikatakoro posmedrankinggenesandbioresourcesbasedonsemanticwebassociationstudy AT matsushimaakihiro posmedrankinggenesandbioresourcesbasedonsemanticwebassociationstudy AT takahashisatoshi posmedrankinggenesandbioresourcesbasedonsemanticwebassociationstudy AT ishiimanabu posmedrankinggenesandbioresourcesbasedonsemanticwebassociationstudy AT takatsukiterue posmedrankinggenesandbioresourcesbasedonsemanticwebassociationstudy AT bhatiarinki posmedrankinggenesandbioresourcesbasedonsemanticwebassociationstudy AT khadbaatarzolzaya posmedrankinggenesandbioresourcesbasedonsemanticwebassociationstudy AT watabehajime posmedrankinggenesandbioresourcesbasedonsemanticwebassociationstudy AT masuyahiroshi posmedrankinggenesandbioresourcesbasedonsemanticwebassociationstudy AT toyodatetsuro posmedrankinggenesandbioresourcesbasedonsemanticwebassociationstudy |