Title |
Dataset size and composition impact the reliability of performance benchmarks for peptide-MHC binding predictions
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Published in |
BMC Bioinformatics, July 2014
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DOI | 10.1186/1471-2105-15-241 |
Pubmed ID | |
Authors |
Yohan Kim, John Sidney, Søren Buus, Alessandro Sette, Morten Nielsen, Bjoern Peters |
Abstract |
It is important to accurately determine the performance of peptide:MHC binding predictions, as this enables users to compare and choose between different prediction methods and provides estimates of the expected error rate. Two common approaches to determine prediction performance are cross-validation, in which all available data are iteratively split into training and testing data, and the use of blind sets generated separately from the data used to construct the predictive method. In the present study, we have compared cross-validated prediction performances generated on our last benchmark dataset from 2009 with prediction performances generated on data subsequently added to the Immune Epitope Database (IEDB) which served as a blind set. |
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Unknown | 2 | 50% |
Demographic breakdown
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Members of the public | 1 | 25% |
Mendeley readers
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Unknown | 92 | 95% |
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Researcher | 27 | 28% |
Student > Ph. D. Student | 17 | 18% |
Student > Bachelor | 10 | 10% |
Other | 8 | 8% |
Student > Master | 7 | 7% |
Other | 11 | 11% |
Unknown | 17 | 18% |
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Biochemistry, Genetics and Molecular Biology | 18 | 19% |
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Engineering | 5 | 5% |
Other | 9 | 9% |
Unknown | 17 | 18% |