Learning random-walk kernels for protein remote homology identification and motif discovery

Renqiang Min, Rui Kuang, Anthony Bonner, Zhaolei Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Random-walk based algorithms are good choices for solving many classification problems with limited labeled data and a large amount of unlabeled data. However, it is difficult to choose the optimal number of random steps, and the results are very sensitive to the parameter chosen. In this paper, we will discuss how to better identify protein remote homology than any other algorithm using a learned random-walk kernel based on a positive linear combination of random-walk kernels with different random steps, which leads to a convex combination of kernels. The resulting kernel has much better prediction performance than the state-of-the-art profile kernel for protein remote homology identification. On the SCOP benchmark dataset, the overall mean ROC 50 score on 54 protein families we obtained using the new kernel is above 0.90, which has almost perfect prediction performance on most of the 54 families and has significant improvement over the best published result; moreover, our approach based on learned random-walk kernels can effectively identify meaningful protein sequence motifs that are responsible for discriminating the memberships of protein sequences' remote homology in SCOP.

Original languageEnglish (US)
Title of host publicationSociety for Industrial and Applied Mathematics - 9th SIAM International Conference on Data Mining 2009, Proceedings in Applied Mathematics 133
Pages132-143
Number of pages12
StatePublished - Dec 1 2009
Event9th SIAM International Conference on Data Mining 2009, SDM 2009 - Sparks, NV, United States
Duration: Apr 30 2009May 2 2009

Publication series

NameSociety for Industrial and Applied Mathematics - 9th SIAM International Conference on Data Mining 2009, Proceedings in Applied Mathematics
Volume1

Other

Other9th SIAM International Conference on Data Mining 2009, SDM 2009
CountryUnited States
CitySparks, NV
Period4/30/095/2/09

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  • Cite this

    Min, R., Kuang, R., Bonner, A., & Zhang, Z. (2009). Learning random-walk kernels for protein remote homology identification and motif discovery. In Society for Industrial and Applied Mathematics - 9th SIAM International Conference on Data Mining 2009, Proceedings in Applied Mathematics 133 (pp. 132-143). (Society for Industrial and Applied Mathematics - 9th SIAM International Conference on Data Mining 2009, Proceedings in Applied Mathematics; Vol. 1).