An Eigendecomposition Approach to Weighted Graph Matching Problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to Linear Optimization
Introduction to Linear Optimization
A Kernel Approach for Learning from almost Orthogonal Patterns
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Using the Fisher Kernel Method to Detect Remote Protein Homologies
Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
Fast String Kernels using Inexact Matching for Protein Sequences
The Journal of Machine Learning Research
New kernels for protein structural motif discovery and function classification
ICML '05 Proceedings of the 22nd international conference on Machine learning
Incorporating the loss function into discriminative clustering of structured outputs
IEEE Transactions on Neural Networks
Efficient methods for robust classification under uncertainty in kernel matrices
The Journal of Machine Learning Research
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Structural alignments are the most widely used tools for comparing proteins with low sequence similarity. The main contribution of this paper is to derive various kernels on proteins from structural alignments, which do not use sequence information. Central to the kernels is a novel alignment algorithm which matches substructures of fixed size using spectral graph matching techniques. We derive positive semi-definite kernels which capture the notion of similarity between substructures. Using these as base more sophisticated kernels on protein structures are proposed. To empirically evaluate the kernels we used a 40% sequence non-redundant structures from 15 different SCOP superfamilies. The kernels when used with SVMs show competitive performance with CE, a state of the art structure comparison program.