Abstract
As the sizes of biomedical literature databases increase, there is an urgent need to develop intelligent systems that automatically discover Protein-Protein interactions from text. Despite resource-intensive efforts to create manually curated interaction databases, the sheer volume of biological literature databases makes it impossible to achieve significant coverage. In this paper, we describe a scalable hierarchical Support Vector Machine(SVM) based framework to efficiently mine protein interactions with high precision. In addition, we describe a convolution tree-vector kernel based on syntactic similarity of natural language text to further enhance the mining process. By using the inherent syntactic similarity of interaction phrases as a kernel method, we are able to significantly improve the classification quality. Our hierarchical framework allows us to reduce the search space dramatically with each stage, while sustaining a high level of accuracy. We test our framework on a corpus of over 10000 manually annotated phrases gathered from various sources. The convolution kernel technique identifies sentences describing interactions with a precision of 95% and a recall of 92%, yielding significant improvements over previous machine learning techniques.
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References
Alfarano, C., et al.: The Biomolecular Interaction Network Database and related tools 2005 update. Nucleic Acids Res. 33, D418–D424 (2005)
Blaschke, C., et al.: Automatic extraction of biological information from scientific text: protein-protein interactions. In: Proc. Int. Conf. Intell. Syst. Mol. Biol., pp. 60–67 (1999)
Brown, K.R., et al.: Online predicted human interaction database. Bioinformatics 21, 2076–2082 (2005)
Chatr-aryamontri, A., et al.: MINT: the Molecular INTeraction database. Nucleic Acids Res. 35, D572–D574 (2007)
Collins, M., Duffy, N.: New ranking algorithms for parsing and tagging: Kernels over discrete structures (2002)
Collins, M.: Head-Driven Statistical Models for Natural Language Parsing. Computational Linguistics (2003)
Donaldson, I., et al.: PreBIND and Textomy–mining the biomedical literature for proteinprotein interactions using a support vector machine. BMC Bioinformatics 4, 11 (2003)
Fukuda, K., et al.: Toward information extraction: identifying protein names from biological papers. In: Pac. Symp. Biocomput., pp. 707–718 (1998)
Genia Project: Mining literature for knowledge in molecular biology (2008), http://wwwtsujii.is.s.u-tokyo.ac.jp/GENIA/home/wiki.cgi
Gilfillan, I.: A database of proteins that are known to interact. Genome Biology 1; Reports220 (November 2000)
Joachims, T.: Text categorization with support vector machines: learning with many relevant features. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 137–142. Springer, Heidelberg (1998)
Joachims, T.: Making large-scale SVM learning practical. In: Advances in Kernel Methods-Support Vector Learning (1999)
Lee, K.J., Hwang, Y.S., Kim, S., Rim, H.C.: Biomedical named entity recognition using two-phase model based on SVMs. J. Bio. med. Inform. 37, 436–447 (2004)
Marcotte, E.M., et al.: Mining literature for protein-protein interactions. Bioinformatics 17, 359–363 (2001)
Ramani, A.K., et al.: Consolidating the set of known human protein-protein interactions in preparation for large-scale mapping of the human interactome. Genome Biol. 6, R40 (2005)
Rosario, B., Hearst, A.: Multi-way Relation Classification: Application to Protein-Protein Interaction. In: Human Language Technology Conference on Empirical Methods in Natural Language Processing (2005)
Rindflesch, T.C., et al.: Mining molecular binding terminology from biomedical text. In: Proc. AMIA Symp., pp. 127–131 (1999)
Temkin, J.M., Gilder, M.R.: Extraction of protein interaction information from unstructured text using a context-free grammar. Bioinformatics 19, 2046–2053 (2003)
Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, Heidelberg (1995)
Yu, H., et al.: Automatic extraction of gene and protein synonyms from MEDLINE and journal articles. In: Proc. AMIA Symp., pp. 919–923 (2002)
Culotta, A., Sorensen, J.: Dependency Tree Kernels for Relation Extraction. In: Proceedings of ACL 2004 (2004)
Bunescu, R., Mooney, R.J.: Subsequence kernels for relation extraction. In: Proceedings of the 19th Conference on Neural Information Processing Systems, Vancouver, British Columbia (2005)
Collins, M., Duffy, N.: Convolution kernels for natural language. In: NIPS 2001 (2001)
Yuka, T., Tsujii, J.: Part-of-Speech Annotation of Biology Research Abstracts. In: The Proceedings of 4th International Conference on Language Resource and Evaluation (LREC 2004), Lisbon, Portugal, May 2004, pp. 1267–1270 (2004)
Collins, M.: A New Statistical Parser Based on Bigram Lexical Dependencies. In: Proceedings of the 34th Annual Meeting of the ACL, Santa Cruz
Porter, M.F.: An algorithm for suffix stripping. Program 14(3), 130–137 (1980)
Biocreative 2: http://biocreative.sourceforge.net/biocreative_2.html
Shin, et al.: Identifying Protein-Protein Interaction Sentences Using Boosting and Kernel Method. In: Second BioCreative Challenge Evaluation Workshop (2007)
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Narayanan, R., Misra, S., Lin, S., Choudhary, A. (2010). Mining Protein Interactions from Text Using Convolution Kernels. In: Theeramunkong, T., et al. New Frontiers in Applied Data Mining. PAKDD 2009. Lecture Notes in Computer Science(), vol 5669. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14640-4_9
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DOI: https://doi.org/10.1007/978-3-642-14640-4_9
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