New kernels for protein structural motif discovery and function classification

Authors:
Chang Wang;Stephen D. Scott
Affiliations:
University of Massachusetts, Amherst, MA;University of Nebraska, Lincoln, NE
Venue:
ICML '05 Proceedings of the 22nd international conference on Machine learning
Year:
2005

Citing 2
Cited 1

Making large-scale support vector machine learning practical

Advances in kernel methods
ParSeq: searching motifs with structural and biochemical properties

Bioinformatics

Structural alignment based kernels for protein structure classification

Proceedings of the 24th international conference on Machine learning

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Abstract

We present new, general-purpose kernels for protein structure analysis, and describe how to apply them to structural motif discovery and function classification. Experiments show that our new methods are faster than conventional techniques, are capable of finding structural motifs, and are very effective in function classification. In addition to strong cross-validation results, we found possible new oxidoreductases and cytochrome P450 reductases and a possible new structural motif in cytochrome P450 reductases.