title: A consensus strategy for combining HLA-DR binding algorithms.
abstract: Diverse approaches have generated algorithms for predicting the binding of peptides and human leukocyte antigen DR (HLA-DR) molecules. As prediction tools appear online, interesting questions arise. Are the various predictions accurate? Do the predictions agree? Is there a value to combined results over individual predictions? Three directly accessible online HLA-DR binding prediction tools are evaluated here to address these questions. The three algorithms generate predictions for high affinity binding that partially agree. No one algorithm is consistently superior or inferior. When a consensus strategy provides a fourth prediction, it is consistently best or second best, regardless of the performance measure being utilized.
authors.affiliation: Office of Sponsored Projects and Research, University of California at San Francisco, Fresno, CA 93703, USA. ronna@ucsfresno.ca
authors.forename: Ronna R
authors.initials: RR
authors.lastname: Mallios
mesh_terms: D000465:Algorithms; D032921:Consensus; D006684:HLA-DR Antigens / Q000276:immunology / Q000378:metabolism*; D006801:Humans; D008432:Mathematical Computing
chemical_list: D006684:HLA-DR Antigens