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 0.809 on the training dataset and outperformed other prediction models on a comprehensive independent dataset. iLBE is a powerful computational tool to identify the linear B-cell epitopes and would help to develop penetrating diagnostic tests. A web application with curated datasets for iLBE is freely accessible at http://kurata14.bio.kyutech.ac.jp/iLBE/.

线性 B 细胞表位对于免疫学应用至关重要，例如疫苗设计、免疫诊断测试和抗体生产，以及疾病诊断和治疗。尽管进行了几十年的研究，但线性 B 细胞表位的准确鉴定仍然具有挑战性。在这项工作中，我们通过整合进化和基于序列的特征开发了一种新的预测器，线性 B 细胞表位的识别 (iLBE)。连续特征向量通过 Wilcoxon 秩和检验进行优化。那么随机森林(RF) 算法使用最佳连续特征向量来预测线性 B 细胞表位。我们通过逻辑回归结合 RF 分数以提高预测准确性。iLBE 在训练数据集上的曲线下面积得分为 0.809，在综合独立数据集上的表现优于其他预测模型。iLBE 是一种强大的计算工具，用于识别线性 B 细胞表位，有助于开发穿透性诊断测试。可以在 http://kurata14.bio.kyutech.ac.jp/iLBE/ 上免费访问带有 iLBE 精选数据集的 Web 应用程序。