FFPred -- feature-based function prediction

What you can do:
An integrated feature-based function prediction server for vertebrate proteomes.
Highlights:
  • The FFPred server adopts a machine-learning approach to perform function prediction in protein feature space using feature characteristics predicted from amino acid sequence.
  • The features are scanned against a library of support vector machines representing over 300 Gene Ontology (GO) classes and probabilistic confidence scores returned for each annotation term.
  • The GO term library has been modelled on human protein annotations; however, benchmark performance testing showed robust performance across higher eukaryotes.
  • FFPred offers important advantages over traditional function prediction servers in its ability to annotate distant homologues and orphan protein sequences, and achieves greater coverage and classification accuracy than other feature-based prediction servers.
  • A user may upload an amino acid and receive annotation predictions via email.
  • Feature information is provided as easy to interpret graphics displayed on the sequence of interest, allowing for back-interpretation of the associations between features and function classes.
Keywords:
  • function prediction
  • vertebrate proteome
  • annotation prediction
  • protein function
  • gene ontology
This record last updated: 07-25-2008

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