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Nucleic Acids Research Advance Access published online on May 25, 2009
Nucleic Acids Research, doi:10.1093/nar/gkp384
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© 2009 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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PosMed (Positional Medline): prioritizing genes with an artificial neural network comprising medical documents to accelerate positional cloning

Yuko Yoshida1, Yuko Makita1, Naohiko Heida1, Satomi Asano1, Akihiro Matsushima1, Manabu Ishii1, Yoshiki Mochizuki1, Hiroshi Masuya2, Shigeharu Wakana2, Norio Kobayashi1 and Tetsuro Toyoda1,*

1Bioinformatics And Systems Engineering (BASE) division, RIKEN. 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan and 2BioResource Center, RIKEN. 3-1-1 Koyadai,Tsukuba-shi, Ibaraki 305-0074, Japan

*To whom correspondence should be addressed. Tel: +81 45 503 9610; Fax: +81 45 503 9553; Email: toyoda{at}base.riken.jp

Received January 31, 2009. Revised April 18, 2009. Accepted April 28, 2009.

PosMed (http://omicspace.riken.jp/) prioritizes candidate genes for positional cloning by employing our original database search engine GRASE, which uses an inferential process similar to an artificial neural network comprising documental neurons (or ‘documentrons’) that represent each document contained in databases such as MEDLINE and OMIM. Given a user-specified query, PosMed initially performs a full-text search of each documentron in the first-layer artificial neurons and then calculates the statistical significance of the connections between the hit documentrons and the second-layer artificial neurons representing each gene. When a chromosomal interval(s) is specified, PosMed explores the second-layer and third-layer artificial neurons representing genes within the chromosomal interval by evaluating the combined significance of the connections from the hit documentrons to the genes. PosMed is, therefore, a powerful tool that immediately ranks the candidate genes by connecting phenotypic keywords to the genes through connections representing not only gene–gene interactions but also other biological interactions (e.g. metabolite–gene, mutant mouse–gene, drug–gene, disease–gene and protein–protein interactions) and ortholog data. By utilizing orthologous connections, PosMed facilitates the ranking of human genes based on evidence found in other model species such as mouse. Currently, PosMed, an artificial superbrain that has learned a vast amount of biological knowledge ranging from genomes to phenomes (or ‘omic space’), supports the prioritization of positional candidate genes in humans, mouse, rat and Arabidopsis thaliana.


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