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

Bioinformatics (Oxford, England)

Volumn 29, Issue 9, 2013, Pages 1190-1198

  Youngs, Noah ^a   Penfold Brown, Duncan ^b   Drew, Kevin ^b   Shasha, Dennis ^b   Bonneau, Richard ^b  

^a POLYTECHNIC UNIVERSITY   (United States)

^b NONE

Author keywords

[No Author keywords available]

Indexed keywords

PROTEIN; PROTEOME;

ALGORITHM; ANIMAL; ARTICLE; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; GENE REGULATORY NETWORK; GENETICS; GENOME; METABOLISM; MOLECULAR GENETICS; MOUSE; PHYSIOLOGY; PROTEIN ANALYSIS; YEAST;

ALGORITHMS; ANIMALS; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; GENE REGULATORY NETWORKS; GENOME; MICE; MOLECULAR SEQUENCE ANNOTATION; PROTEIN INTERACTION MAPPING; PROTEINS; PROTEOME; YEASTS;

MLCS; MLOWN;

EID: 84886411294     PISSN: None     EISSN: 13674811     Source Type: None    
DOI: 10.1093/bioinformatics/btt110     Document Type: Article

References (0)