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Protein function prediction via graph kernels




Motivation: 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.

Author(s): Borgwardt, KM. and Ong, CS. and Schönauer, S. and Vishwanathan, . and Smola, AJ. and Kriegel, H-P.
Journal: Bioinformatics
Volume: 21
Number (issue): Suppl. 1: ISMB 2005 Proceedings
Pages: i47-i56
Year: 2005
Month: June
Day: 0

Department(s): Empirical Inference
Bibtex Type: Article (article)

Digital: 0
DOI: 10.1093/bioinformatics/bti1007
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF


  title = {Protein function prediction via graph kernels},
  author = {Borgwardt, KM. and Ong, CS. and Sch{\"o}nauer, S. and Vishwanathan, . and Smola, AJ. and Kriegel, H-P.},
  journal = {Bioinformatics},
  volume = {21},
  number = {Suppl. 1: ISMB 2005 Proceedings},
  pages = {i47-i56},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = jun,
  year = {2005},
  month_numeric = {6}