PosMed 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 (Yoshida, et al. 2009, Makita, et al. 2009). PosMed immediately ranks the candidate genes by connecting phenotypic keywords to the genes through connections representing gene–gene interactions other biological relationships, such as metabolite–gene, mutant mouse–gene, drug–gene, disease–gene, and protein–protein interactions, ortholog data, and gene–literature connections.To make proper relationships between genes and literature, we manually curate queries, which are defined by logical operation rules, against MEDLINE. For example, to detect a set of MEDLINE documents for the AT1G03880 gene in A. thaliana, we applied the following logical query: (‘AT1G03880’ OR ‘CRU2’ OR ‘CRB’ OR ‘CRUCIFERIN 2' OR ‘CRUCIFERIN B’) AND (‘Arabidopsis’) NOT (‘chloroplast RNA binding’). Curators refined these queries in mouse, rice and A. thaliana. For human and rat genes, we use mouse curation results via ortholog genes in PosMed.PosMed is available at "http://omicspace.riken.jp/PosMed":http://omicspace.riken.jp/PosMed References:Yoshida Y, et al. Nucleic Acids Res. 37(Web Server issue):W147-52. 2009.Makita Y, et al. Plant Cell Physiol. 2009 Jul;50(7):1249-59.
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Makita, Y., Bhatia, R., Deshpande, M. et al. Logical Operation Based Literature Association with Genes and its application, PosMed.. Nat Prec (2010). https://doi.org/10.1038/npre.2010.5071.1