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