We introduce a new sequence-similarity kernel, the spectrum kernel, for use with support vector machines (SVMs) in a discriminative approach to the protein classification problem. Our kernel is conceptually simple and efficient to compute and, in experiments on the SCOP database, performs well in comparison with state-of-the-art methods for homology detection. Moreover, our method produces an SVM classifier that allows linear time classification of test sequences. Our experiments provide evidence that string-based kernels, in conjunction with SVMs, could offer a viable and computationally efficient alternative to other methods of protein classification and homology detection. The spectrum kernel: A string kernel for SVM protein classification
Christina Leslie, Eleazar Eskin and William Stafford Noble
Proceedings of the Pacific Symposium on Biocomputing, 2002. pp. 564-575.
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