Text mining with GeneIndexer

GeneIndexer has been around now for a couple of years but seems too be getting some marketing dollars spent on it with a full page advertisement in the latest issue of Nature and a sale to the NIH Library. The tool enables researchers to reveal biological significance in a set of co-regulated or associated genes. Applications include:

• Using keywords to identify and prioritize genes most relevant to any given research question. Keywords can be any string of words, e.g. disease names, molecular pathways, or Gene Ontology classifications.
• Uncovering implicit, as well as explicit, functional relationships among genes--discover new genes and propose hypotheses above and beyond what is explicitly described in the literature.
• Building hierarchical trees of genes in which gene subsets are clustered into functionally related groups. This allows researchers to navigate large gene collections easily and adds a new dimension to the analysis and discovery process.

Because GeneIndexer includes all of the genes contained in Entrez Gene and OMIM databases, and uses artificial intelligence and computational linguistic techniques, rather than human curators to identify conceptual gene relationships it is possibly the most up-to-date and accurate system of its kind.