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Correlated Protein Function Prediction via Maximization of Data-Knowledge Consistency

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Research in Computational Molecular Biology (RECOMB 2014)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 8394))

Abstract

Protein function prediction in conventional computational approaches is usually conducted one function at a time, fundamentally. As a result, the functions are treated as separate target classes. However, biological processes are highly correlated, which makes functions assigned to proteins are not independent. Therefore, it would be beneficial to make use of function category correlations in predicting protein function. We propose a novel Maximization of Data-Knowledge Consistency (MDKC) approach to exploit function category correlations for protein function prediction. Our approach banks on the assumption that two proteins are likely to have large overlap in their annotated functions if they are highly similar according to certain experimental data. We first establish a new pairwise protein similarity using protein annotations from knowledge perspective. Then by maximizing the consistency between the established knowledge similarity upon annotations and the data similarity upon biological experiments, putative functions are assigned to unannotated proteins. Most importantly, function category correlations are elegantly incorporated through the knowledge similarity. Comprehensive experimental evaluations on Saccharomyces cerevisiae data demonstrate promising results that validate the performance of our methods.

This work was partially supported by US NSF IIS-1117965, IIS-1302675, IIS- 1344152.

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References

  1. Sharan, R., Ulitsky, I., Shamir, R.: Network-based prediction of protein function. Mol. System Biol. 3(1) (2007)

    Google Scholar 

  2. Schwikowski, B., Uetz, P., Fields, S.: A network of protein- protein interactions in yeast. Nat. Biotech. 18, 1257–1261 (2000)

    Article  Google Scholar 

  3. Hishigaki, H., Nakai, K., Ono, T., Tanigami, A., Takagi, T.: Assessment of prediction accuracy of protein function from protein-protein interaction data. Yeast 18(6), 523–531 (2001)

    Article  Google Scholar 

  4. Chua, H., Sung, W., Wong, L.: Exploiting indirect neighbours and topological weight to predict protein function from protein-protein interactions. Bioinformatics 22(13), 1623–1630 (2006)

    Article  Google Scholar 

  5. Chua, H., Sung, W., Wong, L.: Using indirect protein interactions for the prediction of Gene Ontology functions. BMC Bioinformatics 8(suppl. 4), S8 (2007)

    Google Scholar 

  6. Nabieva, E., Jim, K., Agarwal, A., Chazelle, B., Singh, M.: Whole-proteome prediction of protein function via graph-theoretic analysis of interaction maps. Bioinformatics 21, 302–310 (2005)

    Article  Google Scholar 

  7. Weston, J., Elisseeff, A., Zhou, D., Leslie, C., Noble, W.: Protein ranking: from local to global structure in the protein similarity network. Proc. Natl. Acad. Sci. USA 101(17), 6559 (2004)

    Article  Google Scholar 

  8. Vazquez, A., Flammini, A., Maritan, A., Vespignani, A.: Global protein function prediction from protein-protein interaction networks. Nat. Biotechnol. 21, 697–700 (2003)

    Article  Google Scholar 

  9. Karaoz, U., Murali, T., Letovsky, S., Zheng, Y., Ding, C., Cantor, C., Kasif, S.: Whole-genome annotation by using evidence integration in functional-linkage networks. Proc. Natl Acad. Sci. USA 101(9), 2888–2893 (2004)

    Article  Google Scholar 

  10. Liang, S., Shuiwang, J., Jieping, Y.: Adaptive diffusion kernel learning from biological networks for protein function prediction. BMC Bioinformatics 9, 162 (2008)

    Article  Google Scholar 

  11. Wang, H., Huang, H., Ding, C.: Function-function correlated multi-label protein function prediction over interaction networks. In: Chor, B. (ed.) RECOMB 2012. LNCS, vol. 7262, pp. 302–313. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  12. Whisstock, J., Lesk, A.: Prediction of protein function from protein sequence and structure. Q. Rev. Biophysics 36(3), 307–340 (2004)

    Article  Google Scholar 

  13. Lanckriet, G., Deng, M., Cristianini, N., Jordan, M., Noble, W.: Kernel-based data fusion and its application to protein function prediction in yeast. In: Proc. of Pacific Symp. on Biocomputing, vol. 9, pp. 300–311 (2004)

    Google Scholar 

  14. Tsuda, K., Noble, W.: Learning kernels from biological networks by maximizing entropy. Bioinformatics 20, 326–333 (2004)

    Article  Google Scholar 

  15. Shi, L., Cho, Y., Zhang, A.: ANN Based Protein Function Prediction Using Integrated Protein-Protein Interaction Data. In: Proc. of International Joint Conf. on Bioinformatics, Systems Biol. and Intelligent Comp., pp. 271–277 (2009)

    Google Scholar 

  16. Shin, H., Lisewski, A., Lichtarge, O.: Graph sharpening plus graph integration: a synergy that improves protein functional classification. Bioinformatics 23(23), 3217 (2007)

    Article  Google Scholar 

  17. Sun, L., Ji, S., Ye, J.: Adaptive diffusion kernel learning from biological networks for protein function prediction. BMC Bioinformatics 9(1), 162 (2008)

    Article  Google Scholar 

  18. Wang, H., Huang, H., Ding, C.: Protein function prediction via laplacian network partitioning incorporating function category correlations. In: Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, pp. 2049–2055. AAAI Press (2013)

    Google Scholar 

  19. Wang, H., Huang, H., Ding, C.: Image Annotation Using Multi-label Correlated Green’s Function. In: Proc. of IEEE ICCV 2009, pp. 2029–2034 (2009)

    Google Scholar 

  20. Wang, H., Ding, C., Huang, H.: Multi-label linear discriminant analysis. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part VI. LNCS, vol. 6316, pp. 126–139. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  21. Wang, H., Huang, H., Ding, C.: Multi-label feature transform for image classifications. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 793–806. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  22. Wang, H., Huang, H., Ding, C.: Image annotation using bi-relational graph of images and semantic labels. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2011 (CVPR 2011), pp. 793–800 (2011)

    Google Scholar 

  23. Mewes, H., Heumann, K., Kaps, A., Mayer, K., Pfeiffer, F., Stocker, S., Frishman, D.: MIPS: a database for genomes and protein sequences. Nucleic Acids Res. 27(1), 44 (1999)

    Article  Google Scholar 

  24. Cai, D., He, X., Wu, X., Han, J.: Non-negative matrix factorization on manifold. In: Proc. of ICDM (2008)

    Google Scholar 

  25. Gu, Q., Zhou, J.: Co-clustering on manifolds. In: Proc. of SIGKDD (2009)

    Google Scholar 

  26. Cai, D., He, X., Han, J., Huang, T.S.: Graph regularized non-negative matrix factorization for data representation. IEEE Trans. Pattern Analysis Mach. Intell. 99 (2010)

    Google Scholar 

  27. Ding, C., Li, T., Peng, W., Park, H.: Orthogonal nonnegative matrix t-factorizations for clustering. In: SIGKDD (2006)

    Google Scholar 

  28. Ding, C., Li, T., Jordan, M.: Convex and semi-nonnegative matrix factorizations. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(1), 45–55 (2010)

    Article  Google Scholar 

  29. Wang, H., Nie, F., Huang, H., Makedon, F.: Fast nonnegative matrix tri-factorization for large-scale data co-clustering. In: Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, vol. 2, pp. 1553–1558. AAAI Press (2011)

    Google Scholar 

  30. Wang, H., Nie, F., Huang, H., Ding, C.: Dyadic transfer learning for cross-domain image classification. In: Proc. of ICCV, pp. 551–556. IEEE (2011)

    Google Scholar 

  31. Wang, H., Nie, F., Huang, H., Ding, C.: Nonnegative matrix tri-factorization based high-order co-clustering and its fast implementation. In: Proceedings of ICDM (2011)

    Google Scholar 

  32. Wang, H., Huang, H., Ding, C., Nie, F.: Predicting protein-protein interactions from multimodal biological data sources via nonnegative matrix tri-factorization. In: Chor, B. (ed.) RECOMB 2012. LNCS, vol. 7262, pp. 314–325. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  33. Li, T., Ding, C., Jordan, M.: Solving consensus and semi-supervised clustering problems using nonnegative matrix factorization. In: Proc. of ICDM (2007)

    Google Scholar 

  34. Benson, D., Karsch-Mizrachi, I., Lipman, D.: GenBank. Nucleic Acids Res. 34, D16–D20 (2006)

    Google Scholar 

  35. Kullback, S., Leibler, R.: On information and sufficiency. The Annals of Mathematical Statistics, 79–86 (1951)

    Google Scholar 

  36. Stark, C., Breitkreutz, B., Reguly, T., Boucher, L., Breitkreutz, A., Tyers, M.: BioGRID: a general repository for interaction datasets. Nucleic Acids Res. 34(database issue), D535 (2006)

    Google Scholar 

  37. Deane, C., Salwinski, L., Xenarios, I., Eisenberg, D.: Protein Interactions Two Methods for Assessment of the Reliability of High Throughput Observations. Mol. & Cellular Proteomics 1(5), 349–356 (2002)

    Article  Google Scholar 

  38. Pei, P., Zhang, A.: A topological measurement for weighted protein interaction network. In: Proceedings of the 2005 IEEE Computational Systems Bioinformatics Conference, pp. 268–278 (2005)

    Google Scholar 

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Authors and Affiliations

  1. Department of Electrical Engineering and Computer Science, Colorado School of Mines, Golden, Colorado, 80401, USA

    Hua Wang

  2. Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, Texas, 76019, USA

    Heng Huang & Chris Ding

Authors
  1. Hua Wang
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  2. Heng Huang
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  3. Chris Ding
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Editors and Affiliations

  1. School of Computer Science, Tel Aviv University, 69978, Tel Aviv, Israel

    Roded Sharan

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Wang, H., Huang, H., Ding, C. (2014). Correlated Protein Function Prediction via Maximization of Data-Knowledge Consistency. In: Sharan, R. (eds) Research in Computational Molecular Biology. RECOMB 2014. Lecture Notes in Computer Science(), vol 8394. Springer, Cham. https://doi.org/10.1007/978-3-319-05269-4_26

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  • DOI: https://doi.org/10.1007/978-3-319-05269-4_26

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-05268-7

  • Online ISBN: 978-3-319-05269-4

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