Bhattacharya, Sourangshu and Bhattacharyya, Chiranjib and Chandra, Nagasuma (2007) Structural Alignment based Kernels for Protein Structure Classification. In: ICML '07 Proceedings of the 24th international conference on Machine learning, New York, NY.
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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.
|Item Type:||Conference Paper|
|Additional Information:||Copyright of this article belongs to ACM Press.|
|Department/Centre:||Division of Electrical Sciences > Computer Science & Automation (Formerly, School of Automation)|
|Date Deposited:||17 Oct 2011 10:04|
|Last Modified:||17 Oct 2011 10:04|
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