Community-Wide Evaluation of Computational Function Prediction.
A biological experiment is the most reliable way of assigning function to a protein. However, in the era of high-throughput sequencing, scientists are unable to carry out experiments to determine the function of every single gene product. Therefore, to gain insights into the activity of these molecules and guide experiments, we must rely on computational means to functionally annotate the majority of sequence data. To understand how well these algorithms perform, we have established a challenge involving a broad scientific community in which we evaluate different annotation methods according to their ability to predict the associations between previously unannotated protein sequences and Gene Ontology terms. Here we discuss the rationale, benefits, and issues associated with evaluating computational methods in an ongoing community-wide challenge.
Department of Veterinary Microbiology and Preventive Medicine, Iowa State University, Ames, IA, USA.
Iddo
I
Friedberg
Department of Computer Science and Informatics, Indiana University, Bloomington, IN, USA.
Predrag
P
Radivojac
D000465:Algorithms; D000818:Animals; D019295:Computational Biology / Q000379:methods; D063990:Gene Ontology; D006801:Humans; D000069550:Machine Learning; D058977:Molecular Sequence Annotation / Q000379:methods; D011506:Proteins / Q000235:genetics* / Q000378:metabolism
D011506:Proteins
Algorithms; Evaluation; Function prediction; Machine learning