Advertisement

The Un-normalized Graph p-Laplacian Based Semi-supervised Learning Method and Protein Function Prediction Problem

  • Loc TranEmail author
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 244)

Abstract

Protein function prediction is a fundamental problem in modern biology. In this paper, we present the un-normalized graph p-Laplacian semi-supervised learning methods. These methods will be applied to the protein network constructed from the gene expression data to predict the functions of all proteins in the network. These methods are based on the assumption that the labels of two adjacent proteins in the network are likely to be the same. The experiments show that that the un-normalized graph p-Laplacian semi-supervised learning methods are at least as good as the current state of the art method (the un-normalized graph Laplacian based semi-supervised learning method) but often lead to better classification accuracy performance measures.

Keywords

Gene Expression Data Protein Function Prediction Predict Protein Function Adjacent Protein Automate Function Prediction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Shin, H.H., Lisewski, A.M., Lichtarge, O.: Graph sharpening plus graph integration: a synergy that improves protein functional classification. Bioinformatics 23, 3217–3224 (2007)CrossRefGoogle Scholar
  2. 2.
    Pearson, W.R., Lipman, D.J.: Improved tools for biological sequence comparison. Proceedings of the National Academy of Sciences of the United States of America 85, 2444–2448 (1998)CrossRefGoogle Scholar
  3. 3.
    Lockhart, D.J., Dong, H., Byrne, M.C., Follettie, M.T., Gallo, M.V., Chee, M.S., Mittmann, M., Wang, C., Kobayashi, M., Horton, H., Brown, E.L.: Expression monitoring by hybridization to high-density oligonucleotide arrays. Nature Biotechnology 14, 1675–1680 (1996)CrossRefGoogle Scholar
  4. 4.
    Shi, L., Cho, Y., Zhang, A.: Prediction of Protein Function from Connectivity of Protein Interaction Networks. International Journal of Computational Bioscience 1(1) (2010)Google Scholar
  5. 5.
    Lanckriet, G.R.G., Deng, M., Cristianini, N., Jordan, M.I., Noble, W.S.: Kernel-based data fusion and its application to protein function prediction in yeast. In: Pacific Symposium on Biocomputing, PSB (2004)Google Scholar
  6. 6.
    Tsuda, K., Shin, H.H., Schoelkopf, B.: Fast protein classification with multiple networks. Bioinformatics (ECCB 2005) 21(suppl. 2), ii59–ii65 (2005)Google Scholar
  7. 7.
    Tran, L.: Application of three graph Laplacian based semi-supervised learning methods to protein function prediction problem. CoRR abs/1211.4289 (2012)Google Scholar
  8. 8.
    Schwikowski, B., Uetz, P., Fields, S.: A network of protein–protein interactions in yeast. Nature Biotechnology 18, 1257–1261 (2000)CrossRefGoogle Scholar
  9. 9.
    Tran, L.: Hypergraph and protein function prediction with gene expression data. CoRR abs/1212.0388 (2012)Google Scholar
  10. 10.
    Zhou, D., Huang, J., Schoelkopf, B.: Beyond Pairwise Classification and Clustering Using Hypergraphs, Max Planck Institute Technical Report 143, Max Planck Institute for Biological Cybernetics, Tbingen, Germany (2005)Google Scholar
  11. 11.
    Zhou, D., Huang, J., Schoelkopf, B.: Learning with Hypergraphs: Clustering, Classification, and Embedding. In: Schoelkopf, B., Platt, J.C., Hofmann, T. (eds.) Advances in Neural Information Processing System (NIPS), pp. 1601–1608. MIT Press, Cambridge (2007)Google Scholar
  12. 12.
    Pandey, G., Atluri, G., Steinbach, M., Kumar, V.: Association Analysis Techniques for Discovering Functional Modules from Microarray Data. In: Proc. ISMB Special Interest Group Meeting on Automated Function Prediction (2008)Google Scholar
  13. 13.
    Zhou, D., Schölkopf, B.: Regularization on Discrete Spaces. In: Kropatsch, W.G., Sablatnig, R., Hanbury, A. (eds.) DAGM 2005. LNCS, vol. 3663, pp. 361–368. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  14. 14.
    Zhou, D., Schoelkopf, B.: Discrete Regularization. In: Chapelle, O., Schoelkopf, B., Zien, A. (eds.) Semi-Supervised Learning, pp. 221–232. MIT Press, Cambridge (2006)Google Scholar
  15. 15.
    Pandey, G., Myers, L.C., Kumar, V.: Incorporating Functional Inter-relationships into Protein Function Prediction Algorithms. BMC Bioinformatics 10, 142 (2009)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.University of MinnesotaMinneapolisUSA

Personalised recommendations