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PosMed: ranking genes and bioresources based on Semantic Web Association Study
Author(s) -
Yuko Makita,
Norio Kobayashi,
Yuko Yoshida,
Koji Doi,
Yoshiki Mochizuki,
Koro Nishikata,
Akihiro Matsushima,
Satoshi Takahashi,
Manabu Ishii,
Terue Takatsuki,
Rinki Bhatia,
Zolzaya Khadbaatar,
Hajime Watabe,
Hiroshi Masuya,
Tetsuro Toyoda
Publication year - 2013
Publication title -
nucleic acids research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 9.008
H-Index - 537
eISSN - 1362-4954
pISSN - 0305-1048
DOI - 10.1093/nar/gkt474
Subject(s) - biology , web resource , annotation , computational biology , ontology , information retrieval , in silico , candidate gene , phenotype , gene annotation , ranking (information retrieval) , computer science , gene , bioinformatics , world wide web , genetics , genome , philosophy , epistemology
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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