title: Support vector machines with profile-based kernels for remote protein homology detection.
abstract: Two new techniques for remote protein homology detection particulary suited for sparse data are introduced. These methods are based on position specific scoring matrices or profiles and use a support vector machine (SVM) for discrimination. The performance on standard benchmarks outperforms previous non-discriminative techniques and is comparable to that of other SVM-based methods while giving distinct advantages.
authors.affiliation: Department of Computer Science, Royal Holloway, University of London, UK. steven@cs.rhul.ac.uk
authors.forename: Steven
authors.initials: S
authors.lastname: Busuttil
authors.forename: John
authors.initials: J
authors.lastname: Abela
authors.forename: Gordon J
authors.initials: GJ
authors.lastname: Pace
mesh_terms: D000465:Algorithms; D020816:Amino Acid Motifs; D001185:Artificial Intelligence; D010363:Pattern Recognition, Automated / Q000379:methods*; D011506:Proteins / Q000032:analysis / Q000737:chemistry* / Q000145:classification; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*
chemical_list: D011506:Proteins