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This article is part of the supplement: Second International Symposium on Semantic Mining in Biomedicine (SMBM)

Open Access Proceedings

Automatic recognition of topic-classified relations between prostate cancer and genes using MEDLINE abstracts

Hong-Woo Chun1*, Yoshimasa Tsuruoka2, Jin-Dong Kim1, Rie Shiba3, Naoki Nagata4, Teruyoshi Hishiki4 and Jun'ichi Tsujii1256

Author Affiliations

1 Department of Computer Science, Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan

2 School of Computer Science, University of Manchester, UK

3 Japan Biological Information Research Center, Japan Biological Informatics Consortium, Japan

4 Biological Information Research Center, National Institute of Advanced Industrial Science and Technology, Japan

5 SORST, Japan Science and Technology Corporation, Japan

6 National Centre for Text Minig (NaCTeM), Manchester, UK

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BMC Bioinformatics 2006, 7(Suppl 3):S4  doi:10.1186/1471-2105-7-S3-S4

Published: 24 November 2006

Abstract

Background

Automatic recognition of relations between a specific disease term and its relevant genes or protein terms is an important practice of bioinformatics. Considering the utility of the results of this approach, we identified prostate cancer and gene terms with the ID tags of public biomedical databases. Moreover, considering that genetics experts will use our results, we classified them based on six topics that can be used to analyze the type of prostate cancers, genes, and their relations.

Methods

We developed a maximum entropy-based named entity recognizer and a relation recognizer and applied them to a corpus-based approach. We collected prostate cancer-related abstracts from MEDLINE, and constructed an annotated corpus of gene and prostate cancer relations based on six topics by biologists. We used it to train the maximum entropy-based named entity recognizer and relation recognizer.

Results

Topic-classified relation recognition achieved 92.1% precision for the relation (an increase of 11.0% from that obtained in a baseline experiment). For all topics, the precision was between 67.6 and 88.1%.

Conclusion

A series of experimental results revealed two important findings: a carefully designed relation recognition system using named entity recognition can improve the performance of relation recognition, and topic-classified relation recognition can be effectively addressed through a corpus-based approach using manual annotation and machine learning techniques.