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KERNEL-BASED DATA FUSION AND ITS APPLICATION TO PROTEIN FUNCTION PREDICTION IN YEAST
Abstract
Kernel methods provide a principled framework in which to represent many types of data, including vectors, strings, trees and graphs. As such, these methods are useful for drawing inferences about biological phenomena. We describe a method for combining multiple kernel representations in an optimal fashion, by formulating the problem as a convex optimization problem that can be solved using semidefinite programming techniques. The method is applied to the problem of predicting yeast protein functional classifications using a support vector machine (SVM) trained on five types of data. For this problem, the new method performs better than a previously-described Markov random field method, and better than the SVM trained on any single type of data.
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Cited by
Multimodal classification of Alzheimer's disease and mild cognitive impairment
Zhang, D., Wang, Y., Zhou, L., Yuan, H., & Shen, D. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.01.008 · Cited by 1,314
Chua, H. N., Sung, W.-K., & Wong, L. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btl145 · Cited by 572
Kernel methods for predicting protein-protein interactions
Ben-Hur, A., & Noble, W. S. (2005) · Bioinformatics · doi:10.1093/bioinformatics/bti1016 · Cited by 561
Self-citationHierarchical multi-label prediction of gene function
Barutcuoglu, Z., Schapire, R. E., & Troyanskaya, O. G. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btk048 · Cited by 490
Learning the Kernel Matrix with Semi-Definite Programming
Lanckriet, G. R. G., Christianini, N., Bartlett, P. L., Ghaoui, L. E., & Jordan, M. I. (2002) · QUT ePrints (Queensland University of Technology) · doi:10.5555/1005332.1005334 · Cited by 386
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Metadata
- Authors
- LANCKRIET, G. R. G., DENG, M., CRISTIANINI, N., JORDAN, M. I., & NOBLE, W. S.
- Venue
- Biocomputing 2004
- Publisher
- WORLD SCIENTIFIC
- Published
- 2003-12
- Pages
- 300-311
- OpenAlex
- W2013502943
- Cited by
- 321
- References
- 0
- FWCI
- 40.23
- Type
- conference paper
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: KERNEL-BASED DATA FUSION AND ITS APPLICATION TO PROTEIN FUNCTION PREDICTION IN YEAST. https://app.cochat.ai/papers/10.1142/9789812704856_0029
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- LANCKRIET, G. R. G., DENG, M., CRISTIANINI, N., JORDAN, M. I., & NOBLE, W. S. (2003). KERNEL-BASED DATA FUSION AND ITS APPLICATION TO PROTEIN FUNCTION PREDICTION IN YEAST. Biocomputing 2004. https://doi.org/10.1142/9789812704856_0029
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