ILBE for Computational Identification of Linear B-cell Epitopes by Integrating Sequence and Evolutionary Features.

ABSTRACT: Linear B-cell epitopes are critically important for immunological applications, such as vaccine design, immunodiagnostic test, and antibody production, as well as disease diagnosis and therapy. The accurate identification of linear B-cell epitopes remains challenging despite several decades of research. In this work, we have developed a novel predictor, Identification of Linear B-cell Epitope (iLBE), by integrating evolutionary and sequence-based features. The successive feature vectors were optimized by a Wilcoxon-rank sum test. Then the random forest (RF) algorithm using the optimal consecutive feature vectors was applied to predict linear B-cell epitopes. We combined the RF scores by the logistic regression to enhance the prediction accuracy. iLBE yielded an area under curve score of 0.

SUBMITTER: Hasan MM 

PROVIDER: S-EPMC8377379 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature