Abstract
We present a method for automatic extraction of protein interactions from scientific abstracts by combing machine learning and knowledge-based strategies. This method uses sample sentences, which are parsed by a link grammar parser, to learn extraction rules automatically. By incorporating heuristic rules based on morphological clues and domain specific knowledge, this method can remove the interactions that are not between proteins and improve the performance of extraction process. We present experimental results for a test set of MEDLINE abstracts. The results are encouraging and demonstrate the feasibility of our method to perform accurate extraction without need of manual rule building.
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References
Blaschke, A., Andrade, M.A., Ouzounis, C., Valencia, A.: Automatic extraction of biological information from scientific text: protein-protein interactions. In: Proceedings of the 5th Int. Conference on Intelligent Systems for Molecular Biology. AAAI Press (1999).
Califf, M.E.: Relational learning techniques for natural language information extraction. PhD thesis. University of Texas, Austin (1998).
Freitag, D.:Machine learning for information extraction in informal domains. In: Machine learning, 39. Kluwer Academic Publishers.(2000).
Fukuda, K., Tamura, A., Tsunoda, T., Takagi, T.: Toward information extraction: identifying protein names from biological papers. In: Proceedings of the Pacific Symposium on Biocomputing. (1998).
Marcotte, E.M., Xenarios, I., Eisenberg, D.: Mining literature for protein-protein interactions. Bioinformatics. 17(4). Oxford University Press (2001)
Medline Pubmed: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed
Ono, T., Hishigaki, H., Tanigami, A., Takagi, T.: Automated extraction of information on protein-protein interactions from the biological literature. In: Bioinformatics. 17(2) (2001).
Park, J.C., Kim, H.S., Kim, J.J.: Bidirectional Incremental Parsing for Automatic Pathway Identification with Combinatory Categorial Grammar. In: Proceedings of the Pacific Symposium on Biocomputing (2001).
Porter, M.F.: An algorithm for suffix stripping. In: Program 14 (1980).
Rindflesch, T.C., Tanabe, L., Weinstein, J., Hunter, L.: EDGAR: extraction of drugs, genes and relations from the biomedical literature. In: Proceedings of the Pacific Symposium on Biocomputing (2000).
Sleator, D., Temperley, D.: Parsing English with a Link Grammar. In: Proceedings of 3d International Workshop on Parsing Technologies (1993).
Soderland, S.: Learning information extraction rules for semi-structured and free text. In: Machine learning, 34. Kluwer Academic Publishers.(1999).
Tanabe, L., Wilbur, W.: Tagging gene and protein names in biomedical text. In: Bioinformatics. 18(8). Oxford University Press (2002).
Thomas, J., Milward, D., Ouzounis, C., Pulman, S., Carroll, M.: Automatic extraction of protein interactions from scientific abstracts. In: Proceedings of the Pacific Symposium on Biocomputing (2000).
Yakushiji, A., Tateisi, Y., Miyao, Y., Tsujii, J.: Event Extraction from Biomedical Papers Using a Full Parser. In: Proceedings of the Pacific Symposium on Biocomputing (2001).
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Phuong, T.M., Lee, D., Lee, K.H. (2003). Learning Rules to Extract Protein Interactions from Biomedical Text. In: Whang, KY., Jeon, J., Shim, K., Srivastava, J. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2003. Lecture Notes in Computer Science(), vol 2637. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36175-8_15
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DOI: https://doi.org/10.1007/3-540-36175-8_15
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