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

ABSTRACT:

Motivation

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 f

SUBMITTER: Youngs N 

PROVIDER: S-EPMC3634187 | biostudies-literature | 2013 May

REPOSITORIES: biostudies-literature