Parametric Bayesian priors and better choice of negative examples improve protein function prediction.

Noah Youngs, Duncan Penfold-Brown, Kevin Drew, Dennis Shasha, Richard Bonneau

Research output: Contribution to journalArticlepeer-review


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. 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. Code and Data are available at:

Original languageEnglish (US)
Pages (from-to)1190-1198
Number of pages9
JournalUnknown Journal
Issue number9
StatePublished - May 1 2013

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics


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