title: The impact of incomplete knowledge on the evaluation of protein function prediction: a structured-output learning perspective.
abstract: BACKGROUND
  The automated functional annotation of biological macromolecules is a problem of computational assignment of biological concepts or ontological terms to genes and gene products. A number of methods have been developed to computationally annotate genes using standardized nomenclature such as Gene Ontology (GO). However, questions remain about the possibility for development of accurate methods that can integrate disparate molecular data as well as about an unbiased evaluation of these methods. One important concern is that experimental annotations of proteins are incomplete. This raises questions as to whether and to what degree currently available data can be reliably used to train computational models and estimate their performance accuracy.
  
  
  RESULTS
  We study the effect of incomplete experimental annotations on the reliability of performance evaluation in protein function prediction. Using the structured-output learning framework, we provide theoretical analyses and carry out simulations to characterize the effect of growing experimental annotations on the correctness and stability of performance estimates corresponding to different types of methods. We then analyze real biological data by simulating the prediction, evaluation and subsequent re-evaluation (after additional experimental annotations become available) of GO term predictions. Our results agree with previous observations that incomplete and accumulating experimental annotations have the potential to significantly impact accuracy assessments. We find that their influence reflects a complex interplay between the prediction algorithm, performance metric and underlying ontology. However, using the available experimental data and under realistic assumptions, our results also suggest that current large-scale evaluations are meaningful and almost surprisingly reliable.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Computer Science and Informatics, Indiana University, Bloomington, IN, USA, Department of Microbiology and Department of Computer Science and Software Engineering, Miami University, Oxford, OH, USA.
authors.forename: Yuxiang
authors.initials: Y
authors.lastname: Jiang
authors.affiliation: Department of Computer Science and Informatics, Indiana University, Bloomington, IN, USA, Department of Microbiology and Department of Computer Science and Software Engineering, Miami University, Oxford, OH, USA.
authors.forename: Wyatt T
authors.initials: WT
authors.lastname: Clark
authors.affiliation: Department of Computer Science and Informatics, Indiana University, Bloomington, IN, USA, Department of Microbiology and Department of Computer Science and Software Engineering, Miami University, Oxford, OH, USA Department of Computer Science and Informatics, Indiana University, Bloomington, IN, USA, Department of Microbiology and Department of Computer Science and Software Engineering, Miami University, Oxford, OH, USA.
authors.forename: Iddo
authors.initials: I
authors.lastname: Friedberg
authors.affiliation: Department of Computer Science and Informatics, Indiana University, Bloomington, IN, USA, Department of Microbiology and Department of Computer Science and Software Engineering, Miami University, Oxford, OH, USA.
authors.forename: Predrag
authors.initials: P
authors.lastname: Radivojac
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods; D063990:Gene Ontology; D058977:Molecular Sequence Annotation; D011506:Proteins / Q000235:genetics / Q000502:physiology*; D016415:Sequence Alignment
chemical_list: D011506:Proteins