Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Predicting Protective Linear B-Cell Epitopes Using Evolutionary Information
BIBM '08 Proceedings of the 2008 IEEE International Conference on Bioinformatics and Biomedicine
Predicting MHC-II Binding Affinity Using Multiple Instance Regression
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Several computational methods for identifying epitopes in antigenic sequences have been proposed in the last decade. Many of these methods have been made accessible via the Internet as online web servers. However, the majority of these servers are not periodically updated to accumulate newly reported experimental data or to support additional major histocompatibility complex (MHC) allele-specic predictors. Furthermore, performing large scale studies using these servers is limited by the amount of accepted query data (some servers restrict the user to submit one protein sequence at a time) and is also limited by the lack of a unified output format which complicates the process of combining the results of several servers in order to obtain consensus predictions. To address these issues, we propose Epitopes Toolkit (EpiT), a platform for developing epitope prediction tools. The software, documentation and other supporting materials are available at http://ailab.cs.iastate.edu/epit/.