Selection and combination of machine learning classifiers for prediction of linear B-cell epitopes on proteins

• Published in: Journal of molecular recognition Vol. 19; no. 3; pp. 209 - 214
• Main Author: Söllner, Johannes
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Ltd 01.05.2006
• Subjects: Algorithms; Artificial Intelligence; classification algorithm; ensemble; epitope; Epitopes, B-Lymphocyte - chemistry; Epitopes, B-Lymphocyte - classification; Epitopes, B-Lymphocyte - immunology; HIV - metabolism; Linear Models; machine learning; neural network; Neural Networks (Computer); Pattern Recognition, Automated - methods; Proteins - chemistry; Proteins - classification; Proteins - immunology; Reproducibility of Results; ROC; ROC Curve; Viral Proteins - chemistry; Viral Proteins - classification; Viral Proteins - immunology
• ISSN: 0952-3499; 1099-1352
• DOI: 10.1002/jmr.770

Recently, 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.