Since whole genome sequences were first elucidated, knowledge-based ranking of candidate genes has become one of the most important bioinformatics tasks in the forward-genetics and positional-cloning approaches to identify phenotype-responsible gene mutations.

This task requires creating a form of artificial intelligence that can solve a genetic researcher’s problems by learning computationally a vast amount of information accumulated in documents and published data.

We have developed a system named “PosMed,” an artificial intelligence that guides you to the key information waiting to be discovered in the sea of data.

Please check our publication for details.

PosMed suggests highly promising candidate genes in a given chromosomal interval(s). PosMed generates reasonable inferences connecting the user's keywords and genes (i.e., Keyword-> Gene1-> the interval.)

In the case where few candidates are found by direct keyword search, PosMed automatically proceeds to infer other candidates via estimated gene-gene relationships, and shows indirect connections (i.e. Keyword-> Gene1-> Gene2-> the interval.)

Thus, PosMed intelligently assists your choice of candidate genes from various phenotypic keywords.

An user manual and a tutorial titled “How to search candidate genes in positional-cloning studies” are available.

Motivation: Statistical analysis of links on the semantic web is important for various evaluation purposes such as quantifying an individual’s scientific research output based on citation links. SPARQL has been proposed as a standardized query language for the semantic web and is intuitively understandable; however, it does not adequately support statistical evaluation of semantic links. Results: We have extended SPARQL to a novel Resource Description Framework (RDF) query language termed GRASQL to generate inferences connecting semantic Boolean-based deduction and statistical evaluation of RDF resources. We have verified the descriptive capability of GRASQL by writing GRASQL queries for practical biomedical search patterns including in silico positional cloning studies and for ranking researchers in a specific domain of expertise by introducing k index, the number of papers containing specific keywords that are published in a fixed period by a researcher. We have also developed a search engine termed GRASE, which executes a restricted variety of GRASQL queries by requesting a dynamic and comprehensive evaluation of statistical significance of intersections between each group of documents assigned to URIs and those documents matching user-specified keywords and omics conditions. By performing practical in silico positional cloning searches with GRASE, we show the relevance of our approach on the Semantic Web for biomedical knowledge discovery problem solving. Availability: GRASE is used as the search engine for the Positional Medline (PosMed) service and Researcher Finder service at http://omicspace.riken.jp/PosMed/.