Tagging gene and protein names in biomedical text

MOTIVATION The MEDLINE database of biomedical abstracts contains scientific knowledge about thousands of interacting genes and proteins. Automated text processing can aid in the comprehension and synthesis of this valuable information. The fundamental task of identifying gene and protein names is a necessary first step towards making full use of the information encoded in biomedical text. This remains a challenging task due to the irregularities and ambiguities in gene and protein nomenclature. We propose to approach the detection of gene and protein names in scientific abstracts as part-of-speech tagging, the most basic form of linguistic corpus annotation. RESULTS We present a method for tagging gene and protein names in biomedical text using a combination of statistical and knowledge-based strategies. This method incorporates automatically generated rules from a transformation-based part-of-speech tagger, and manually generated rules from morphological clues, low frequency trigrams, indicator terms, suffixes and part-of-speech information. Results of an experiment on a test corpus of 56K MEDLINE documents demonstrate that our method to extract gene and protein names can be applied to large sets of MEDLINE abstracts, without the need for special conditions or human experts to predetermine relevant subsets. AVAILABILITY The programs are available on request from the authors.

[1]  Thomas C. Rindflesch,et al.  EDGAR: extraction of drugs, genes and relations from the biomedical literature. , 1999, Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing.

[2]  G Demetriou,et al.  Two applications of information extraction to biological science journal articles: enzyme interactions and protein structures. , 1999, Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing.

[3]  T. Takagi,et al.  Toward information extraction: identifying protein names from biological papers. , 1998, Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing.

[4]  Biological Laboratories Divinity Avenue Cambridge Ma Usa. FlyBase.  FlyBase: a Drosophila database. , 1998, Nucleic acids research.

[5]  Nigel Collier,et al.  Extracting the Names of Genes and Gene Products with a Hidden Markov Model , 2000, COLING.

[6]  W. John Wilbur,et al.  Boosting naïve Bayesian learning on a large subset of MEDLINE , 2000, AMIA.

[7]  Eric Brill,et al.  Some Advances in Transformation-Based Part of Speech Tagging , 1994, AAAI.

[8]  Pat Langley,et al.  Elements of Machine Learning , 1995 .

[9]  John C. Platt,et al.  Fast training of support vector machines using sequential minimal optimization, advances in kernel methods , 1999 .

[10]  M. Ashburner,et al.  Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.

[11]  Nigel Collier,et al.  Automatic Term Identification and Classification in Biology Texts. , 1999 .

[12]  Betsy L. Humphreys,et al.  Technical Milestone: The Unified Medical Language System: An Informatics Research Collaboration , 1998, J. Am. Medical Informatics Assoc..

[13]  Donna R. Maglott,et al.  RefSeq and LocusLink: NCBI gene-centered resources , 2001, Nucleic Acids Res..

[14]  Proux,et al.  Detecting Gene Symbols and Names in Biological Texts: A First Step toward Pertinent Information Extraction. , 1998, Genome informatics. Workshop on Genome Informatics.

[15]  Allen C. Browne,et al.  Lexical methods for managing variation in biomedical terminologies. , 1994, Proceedings. Symposium on Computer Applications in Medical Care.