title: A close look at protein function prediction evaluation protocols.
abstract: BACKGROUND
  The recently held Critical Assessment of Function Annotation challenge (CAFA2) required its participants to submit predictions for a large number of target proteins regardless of whether they have previous annotations or not. This is in contrast to the original CAFA challenge in which participants were asked to submit predictions for proteins with no existing annotations. The CAFA2 task is more realistic, in that it more closely mimics the accumulation of annotations over time. In this study we compare these tasks in terms of their difficulty, and determine whether cross-validation provides a good estimate of performance.
  
  
  RESULTS
  The CAFA2 task is a combination of two subtasks: making predictions on annotated proteins and making predictions on previously unannotated proteins. In this study we analyze the performance of several function prediction methods in these two scenarios. Our results show that several methods (structured support vector machine, binary support vector machines and guilt-by-association methods) do not usually achieve the same level of accuracy on these two tasks as that achieved by cross-validation, and that predicting novel annotations for previously annotated proteins is a harder problem than predicting annotations for uncharacterized proteins. We also find that different methods have different performance characteristics in these tasks, and that cross-validation is not adequate at estimating performance and ranking methods.
  
  
  CONCLUSIONS
  These results have implications for the design of computational experiments in the area of automated function prediction and can provide useful insight for the understanding and design of future CAFA competitions.
authors.affiliation: Department of Computer Science, Colorado State University, Fort Collins, 80523 CO USA.
authors.forename: Indika
authors.initials: I
authors.lastname: Kahanda
authors.affiliation: Computational Bioscience Program, University of Colorado School of Medicine, Aurora, 80045 CO USA.
authors.forename: Christopher S
authors.initials: CS
authors.lastname: Funk
authors.affiliation: Department of Computer Science, Colorado State University, Fort Collins, 80523 CO USA.
authors.forename: Fahad
authors.initials: F
authors.lastname: Ullah
authors.affiliation: Department of Computing and Information Systems, University of Melbourne, 3010 Parkville, Victoria, Australia.
authors.forename: Karin M
authors.initials: KM
authors.lastname: Verspoor
authors.affiliation: Department of Computer Science, Colorado State University, Fort Collins, 80523 CO USA.
authors.forename: Asa
authors.initials: A
authors.lastname: Ben-Hur
mesh_terms: D030562:Databases, Protein; D011506:Proteins / Q000502:physiology*
chemical_list: D011506:Proteins
keywords: Automated function prediction; Gene Ontology; Machine learning; Support vector machines