title: Parametric Bayesian priors and better choice of negative examples improve protein function prediction.
abstract: BACKGROUND
  Computational biologists have demonstrated the utility of using machine learning methods to predict protein function from an integration of multiple genome-wide data types. Yet, even the best performing function prediction algorithms rely on heuristics for important components of the algorithm, such as choosing negative examples (proteins without a given function) or determining key parameters. The improper choice of negative examples, in particular, can hamper the accuracy of protein function prediction.
  
  
  RESULTS
  We present a novel approach for choosing negative examples, using a parameterizable Bayesian prior computed from all observed annotation data, which also generates priors used during function prediction. We incorporate this new method into the GeneMANIA function prediction algorithm and demonstrate improved accuracy of our algorithm over current top-performing function prediction methods on the yeast and mouse proteomes across all metrics tested.
  
  
  BACKGROUND
  Code and Data are available at: http://bonneaulab.bio.nyu.edu/funcprop.html
authors.affiliation: Department of Computer Science, Center for Genomics and Systems Biology, New York University, New York, NY 10003, USA.
authors.forename: Noah
authors.initials: N
authors.lastname: Youngs
authors.forename: Duncan
authors.initials: D
authors.lastname: Penfold-Brown
authors.forename: Kevin
authors.initials: K
authors.lastname: Drew
authors.forename: Dennis
authors.initials: D
authors.lastname: Shasha
authors.forename: Richard
authors.initials: R
authors.lastname: Bonneau
mesh_terms: D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D001499:Bayes Theorem; D053263:Gene Regulatory Networks; D016678:Genome; D051379:Mice; D058977:Molecular Sequence Annotation; D025941:Protein Interaction Mapping; D011506:Proteins / Q000235:genetics / Q000378:metabolism / Q000502:physiology*; D020543:Proteome / Q000378:metabolism; D015003:Yeasts / Q000235:genetics / Q000378:metabolism
chemical_list: D011506:Proteins; D020543:Proteome