Using quantitative dose-response data to benchmark B-cell epitope prediction for antipeptide antibodies
Pages 629 - 636
Abstract
A mechanistically framed consensus on benchmark data and benchmarking procedures for B-cell epitope prediction methods has yet to emerge, thus presenting a critical window of opportunity to establish standards of practice that circumvent epistemic inconsistencies of casting the epitope-prediction task as a binary classification problem. As an alternative to dichotomous qualitative data that historically have been the basis for benchmarking B-cell epitope prediction, quantitative dose-response data on antibody-mediated biological effects are more meaningful from an information-theoretic perspective in that the said effects may be expressed as probabilities (e.g., of functional inhibition by antibody) for which the Shannon information entropy (SIE) can be evaluated as a measure of informativeness; accordingly, half-maximal biological effects (e.g., at median inhibitory concentrations of antibody) correspond to maximally informative data while undetectable and maximal biological effects correspond to minimally informative data. This applies to benchmarking B-cell epitope prediction for the design of peptide-based immunogens that elicit antipeptide antibodies with functionally relevant cross-reactivity. Presently, the Immune Epitope Database (IEDB) contains relatively few quantitative dose-response data on such cross-reactivity. Only a small fraction of these IEDB data are maximally informative, and more of them are minimally informative (i.e., with zero SIE). Nevertheless, the much more numerous qualitative data in the IEDB suggest how to overcome the paucity of informative benchmark data.
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Index Terms
- Using quantitative dose-response data to benchmark B-cell epitope prediction for antipeptide antibodies
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October 2012
725 pages
ISBN:9781450316705
DOI:10.1145/2382936
- General Chair:
- Sanjay Ranka,
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