title: Protein function prediction via graph kernels.
abstract: BACKGROUND
  Computational approaches to protein function prediction infer protein function by finding proteins with similar sequence, structure, surface clefts, chemical properties, amino acid motifs, interaction partners or phylogenetic profiles. We present a new approach that combines sequential, structural and chemical information into one graph model of proteins. We predict functional class membership of enzymes and non-enzymes using graph kernels and support vector machine classification on these protein graphs.
  
  
  RESULTS
  Our graph model, derivable from protein sequence and structure only, is competitive with vector models that require additional protein information, such as the size of surface pockets. If we include this extra information into our graph model, our classifier yields significantly higher accuracy levels than the vector models. Hyperkernels allow us to select and to optimally combine the most relevant node attributes in our protein graphs. We have laid the foundation for a protein function prediction system that integrates protein information from various sources efficiently and effectively.
  
  
  BACKGROUND
  More information available via www.dbs.ifi.lmu.de/Mitarbeiter/borgwardt.html.
authors.affiliation: Institute for Computer Science, Ludwig-Maximilians-University Munich Oettingenstrasss e 67, 80538 Munich, Germany. borgwardt@dbs.ifi.lmu.de
authors.forename: Karsten M
authors.initials: KM
authors.lastname: Borgwardt
authors.forename: Cheng Soon
authors.initials: CS
authors.lastname: Ong
authors.forename: Stefan
authors.initials: S
authors.lastname: Schönauer
authors.forename: S V N
authors.initials: SV
authors.lastname: Vishwanathan
authors.forename: Alex J
authors.initials: AJ
authors.lastname: Smola
authors.forename: Hans-Peter
authors.initials: HP
authors.lastname: Kriegel
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D004798:Enzymes / Q000737:chemistry*; D015233:Models, Statistical; D011487:Protein Conformation; D017433:Protein Structure, Secondary; D020539:Sequence Analysis, Protein / Q000379:methods; D012984:Software
chemical_list: D004798:Enzymes