The spectrum kernel: a string kernel for SVM protein classification.
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.
Leslie
Christina
C
Department of Computer Science, Columbia University, New York, NY 10027, USA. cleslie.noble@cs.columbia.edu
Eskin
Eleazar
E
Noble
William Stafford
WS
D001185:Artificial Intelligence; D011506:Proteins / Q000737:chemistry* / Q000145:classification*
D011506:Proteins