Protein-Protein Interaction Extraction Based on Convex Combination Kernel Function

DOI: 10.4236/jcc.2013.15002   PDF   HTML     2,551 Downloads   5,274 Views   Citations

Abstract

Owing to the effect of classified models was different in Protein-Protein Interaction(PPI) extraction, which was made by different single kernel functions, and only using single kernel function hardly trained the optimal classified model to extract PPI, this paper presents a strategy to find the optimal kernel function from a kernel function set. The strategy is that in the kernel function set which consists of different single kernel functions, endlessly finding the last two kernel functions on the performance in PPI extraction, using their optimal kernel function to replace them, until there is only one kernel function and it’s the final optimal kernel function. Finally, extracting PPI using the classified model made by this kernel function. This paper conducted the PPI extraction experiment on AIMed corpus, the experimental result shows that the optimal convex combination kernel function this paper presents can effectively improve the extraction performance than single kernel function, and it gets the best precision which reaches 65.0 among the similar PPI extraction systems.

 

Share and Cite:

Chen, P. , Guo, J. , Yu, Z. , Wei, S. , Zhou, F. and Yan, X. (2013) Protein-Protein Interaction Extraction Based on Convex Combination Kernel Function. Journal of Computer and Communications, 1, 9-13. doi: 10.4236/jcc.2013.15002.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] L. H. Qian and G. D. Zhou, “Tree Kernel-Based Protein-Protein Interaction Extraction from Biomedical Literature [J],” Journal of Biomedical Informatics, Vol. 45, No. 3, 2012, pp. 535-543. http://dx.doi.org/10.1016/j.jbi.2012.02.004
[2] S. Kim, J. Yoon, J. Yang and S. Park, “Walk-Weighted Subsequence Kernels for Protein-Protein Interaction Extraction [J],” MBC Bioinformatics, Vol. 11, 2010, p. 107.
[3] A. Airola, S. Pyysalo, J. Bjrne, T. Pahikkala, F. Ginter and T. Salakoski, “All-Paths Graphkernel for Protein-Protein Interaction Extraction with Evaluation of Crosscorpus Learning [J],” BMC Bioinformatics, Vol. 9, No. S1, 2008.
[4] Y. Niu, D. Otasek and I. Jurisica, “Evaluation of Linguistic Features Useful in Extraction of Interactions from Pub-Med; Application to Annotating Known, High-Through-put and Predicted Interactions in I2D [J],” Bioinformatics, Vol. 26, No. 1, 2020, pp. 111-119.
[5] Y. Miyao, et al., “Evaluating Contributions of Natural Language Parsers to Protein-Protein Interaction Extraction [J],” Bioinformatics, Vol. 25, 2009, pp. 394-400. http://dx.doi.org/10.1093/bioinformatics/btn631
[6] B. Liu, L. H. Qian, H. L. Wang and G. D. Zhou, “Dependency-Driven Feature-Based Learning for Extracting Protein-Protein Interactions from Biomedical Text,” Proceedings of COLING’, Poster, 2010, pp. 757-765.
[7] Q.-C. Bui, S. Katrenko and P. M. A. Sloot, “A Hybrid Approach to Extract Protein-Protein Interactions [J],” Bioinformatics, Vol. 27, No. 2, 2011, pp. 259-265. http://dx.doi.org/10.1093/bioinformatics/btq620
[8] R. Sætre, K. Sagae and J. Tsujii, “Syntactic Features for Protein-Protein Interaction Extraction,” Proceedings of LBM’07, Vol. 319, 2007, pp. 6.1-6.14.
[9] R. Sætre, K. Yoshida, M. Miwa, T. Matsuzaki, Y. Kano and J. Tsujii, “Extracting Protein Interactions from Text with the Unified AkaneRE Event Extraction System [J],” IEEE/ACM Transactions on Computing Biological Bioinformatics, Vol. 7, No. 3, 2010, pp. 442-453. http://dx.doi.org/10.1109/TCBB.2010.46
[10] C. Plake, et al., “Optimizing Syntax Patterns for Discovering Protein-Protein Interactions,” Proceedings of the ACM Symposium on Applied Computing, 2005, ACM Press, New York, pp. 195-201.
[11] S. Kim, et al., “Walk-Weighted Subsequence Kernels for Protein-Protein Interaction Extraction [J],” BMC Bioinformatics, Vol. 11, 2010, p. 107. http://dx.doi.org/10.1186/1471-2105-11-107
[12] R. Sætre, et al., “Extracting Protein-Interactions from Text with the Unified AkaneRE Event Extraction System [J],” IEEE/ACM Transactions on Computational Biology and Bioinformatics, Vol. 99, 2010, pp. 442-453. http://dx.doi.org/10.1109/TCBB.2010.46

  
comments powered by Disqus

Copyright © 2020 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.