iBCE-EL: A New Ensemble Learning Framework for Improved Linear B-Cell Epitope Prediction - Manavalan, Balachandran; Govindaraj, Rajiv Gandhi; Shin, Tae Hwan; Kim, Myeong Ok; Lee, Gwang (Frontiers in Immunology, 2018)
Identification of B-cell epitopes (BCEs) is necessary for epitope-based vaccine development, antibody production, and disease prevention and diagnosis. Computational prediction of BCEs, which is necessary before classical wet lab validation, has gained tremendous interest recently. Although several computational methods have been developed, their accuracy is unreliable. Thus, developing a reliable model with significant prediction improvements is highly desirable. In this study, we first constructed a non-redundant dataset of 5,550 experimentally validated BCEs and 6,893 non-BCEs from the Immune Epitope Database. We then developed a novel ensemble learning framework for improved linear BCE predictor called iBCE-EL, a fusion of three independent predictors, namely, random forest, extremely randomised tree, and gradient boosting classifiers, which uses either dipeptide composition or a combination of dipeptide and physicochemical properties as input features. Cross-validation analysis on a benchmarking dataset showed that iBCE-EL performed better than individual classifiers, with an accuracy of 72.4%. Furthermore, iBCE-EL significantly outperformed the state-of-the-art method and six other machine learning-based methods developed in this study, with an accuracy of 71.8%, when objectively evaluated using an independent dataset. To the best of our knowledge, iBCE-EL is the first ensemble method for linear BCE prediction. It will facilitate the design of peptide-based vaccine adjuvants. iBCE-EL was implemented in a web-based platform, which is available at http://thegleelab.org/iBCE-EL.