利用共现网络结构从MEDLINE摘要中提取同义基因和蛋白质名称。

Using co-occurrence network structure to extract synonymous gene and protein names from MEDLINE abstracts.

作者信息

Cohen A M, Hersh W R, Dubay C, Spackman K

机构信息

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.

出版信息

BMC Bioinformatics. 2005 Apr 22;6:103. doi: 10.1186/1471-2105-6-103.

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.

CONCLUSION

The method performs comparably with other studied methods, does not rely on sophisticated named-entity recognition, and requires little initial seed knowledge.