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Medientyp: E-Artikel Titel: Selection and combination of machine learning classifiers for prediction of linear B‐cell epitopes on proteins Beteiligte: Söllner, Johannes Erschienen: Wiley, 2006 Erschienen in: Journal of Molecular Recognition, 19 (2006) 3, Seite 209-214 Sprache: Englisch DOI: 10.1002/jmr.770 ISSN: 0952-3499; 1099-1352 Schlagwörter: Molecular Biology ; Structural Biology Entstehung: Anmerkungen: Beschreibung: AbstractRecently, new machine learning classifiers for the prediction of linear B‐cell epitopes were presented. Here we show the application of Receiver Operator Characteristics (ROC) convex hulls to select optimal classifiers as well as possibilities to improve the post test probability (PTP) to meet real world requirements such as high throughput epitope screening of whole proteomes. The major finding is that ROC convex hulls present an easy to use way to rank classifiers based on their prediction conservativity as well as to select candidates for ensemble classifiers when validating against the antigenicity profile of 10 HIV‐1 proteins. We also show that linear models are at least equally efficient to model the available data when compared to multi‐layer feed‐forward neural networks. Copyright © 2006 John Wiley & Sons, Ltd.