Using co-occurrence network structure to extract synonymous gene and protein names from MEDLINE abstracts.
BACKGROUND
Text-mining can assist biomedical researchers in reducing information overload by extracting useful knowledge from large collections of text. We developed a novel text-mining method based on analyzing the network structure created by symbol co-occurrences as a way to extend the capabilities of knowledge extraction. The method was applied to the task of automatic gene and protein name synonym extraction.


RESULTS
Performance was measured on a test set consisting of about 50,000 abstracts from one year of MEDLINE. Synonyms retrieved from curated genomics databases were used as a gold standard. The system obtained a maximum F-score of 22.21% (23.18% precision and 21.36% recall), with high efficiency in the use of seed pairs.


CONCLUSIONS
The method performs comparably with other studied methods, does not rely on sophisticated named-entity recognition, and requires little initial seed knowledge.
Cohen
A M
AM
Department of Medical Informatics and Clinical Epidemiology, School of Medicine, Oregon Health & Science University, 3181 S,W, Sam Jackson Park Road, Portland, Oregon 97239-3098, USA. cohenaa@ohsu.edu
Hersh
W R
WR
Dubay
C
C
Spackman
K
K
D000465:Algorithms; D001185:Artificial Intelligence; D001331:Automation; D019295:Computational Biology / Q000379:methods*; D003196:Computer Graphics; D003201:Computers; D003628:Database Management Systems; D016206:Databases, Bibliographic; D030541:Databases, Genetic; D015972:Gene Expression Regulation, Neoplastic; D016678:Genome; D006801:Humans; D016247:Information Storage and Retrieval; D007256:Information Systems; D016239:MEDLINE; D009323:Natural Language Processing; D009369:Neoplasms / Q000235:genetics; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D011381:Programming Languages; D015203:Reproducibility of Results; D012984:Software; D012985:Software Design; D009626:Terminology as Topic; D018875:Vocabulary, Controlled