Journal
BIOINFORMATICS
Volume 24, Issue 16, Pages I126-I132Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btn299
Keywords
-
Ask authors/readers for more resources
Motivation: Text mining in the biomedical domain aims at helping researchers to access information contained in scientific publications in a faster, easier and more complete way. One step towards this aim is the recognition of named entities and their subsequent normalization to database identifiers. Normalization helps to link objects of potential interest, such as genes, to detailed information not contained in a publication; it is also key for integrating different knowledge sources. From an information retrieval perspective, normalization facilitates indexing and querying. Gene mention normalization (GN) is particularly challenging given the high ambiguity of gene names: they refer to orthologous or entirely different genes, are named after phenotypes and other biomedical terms, or they resemble common English words. Results: We present the first publicly available system, GNAT, reported to handle inter-species GN. Our method uses extensive background knowledge on genes to resolve ambiguous names to EntrezGene identifiers. It performs comparably to single-species approaches proposed by us and others. On a benchmark set derived from BioCreative 1 and 2 data that contains genes from 13 species, GNAT achieves an F-measure of 81.4 (90.8 precision at 73.8 recall). For the single-species task, we report an F-measure of 85.4 on human genes.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available