title: A structural alignment kernel for protein structures.
abstract: BACKGROUND
  This work aims to develop computational methods to annotate protein structures in an automated fashion. We employ a support vector machine (SVM) classifier to map from a given class of structures to their corresponding structural (SCOP) or functional (Gene Ontology) annotation. In particular, we build upon recent work describing various kernels for protein structures, where a kernel is a similarity function that the classifier uses to compare pairs of structures.
  
  
  RESULTS
  We describe a kernel that is derived in a straightforward fashion from an existing structural alignment program, MAMMOTH. We find in our benchmark experiments that this kernel significantly out-performs a variety of other kernels, including several previously described kernels. Furthermore, in both benchmarks, classifying structures using MAMMOTH alone does not work as well as using an SVM with the MAMMOTH kernel.
  
  
  BACKGROUND
  http://noble.gs.washington.edu/proj/3dkernel
authors.affiliation: Department of Genome Sciences, University of Washington, Seattle, WA, USA.
authors.forename: Jian
authors.initials: J
authors.lastname: Qiu
authors.forename: Martial
authors.initials: M
authors.lastname: Hue
authors.forename: Asa
authors.initials: A
authors.lastname: Ben-Hur
authors.forename: Jean-Philippe
authors.initials: JP
authors.lastname: Vert
authors.forename: William Stafford
authors.initials: WS
authors.lastname: Noble
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D008969:Molecular Sequence Data; D010363:Pattern Recognition, Automated / Q000379:methods*; D011506:Proteins / Q000737:chemistry*; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*; D017386:Sequence Homology, Amino Acid
chemical_list: D011506:Proteins