BeeTLe: 一种面向不平衡数据的深度序列模型用于线性B细胞表位预测与分类及对数调整损失
BeeTLe: An Imbalance-Aware Deep Sequence Model for Linear B-Cell Epitope Prediction and Classification with Logit-Adjusted Losses
AI总结：
BeeTLe通过深度学习模型和对数调整损失技术，解决线性B细胞表位预测与分类中的类别不平衡问题，提升预测性能。
摘要：
识别和表征B细胞表位（即被抗体识别的抗原部分）的过程对于理解免疫系统至关重要，也对许多应用包括疫苗开发、治疗和诊断具有重要意义。计算表位预测具有挑战性但回报丰厚，因为它可以显著减少实验室工作的耗时和成本。现有的大多数工具性能不令人满意，只能区分表位和非表位。本文提出了一种基于深度学习的多任务框架，用于线性B细胞表位预测以及抗体类型特异性表位分类。具体来说，开发了一个基于序列的神经网络模型，使用循环层和Transformer块。我们提出了一种基于特征分解的氨基酸编码方法，以帮助模型学习表位的表示。我们引入了对标准交叉熵损失函数的修改，通过扩展对数调整技术来应对类别不平衡问题。在从最大公共表位数据库中整理的数据上进行的实验结果证明了所提方法的有效性，并与竞争方法相比表现出优越的性能。
英文摘要：
The process of identifying and characterizing B-cell epitopes, which are the portions of antigens recognized by antibodies, is important for our understanding of the immune system, and for many applications including vaccine development, therapeutics, and diagnostics. Computational epitope prediction is challenging yet rewarding as it significantly reduces the time and cost of laboratory work. Most of the existing tools do not have satisfactory performance and only discriminate epitopes from non-epitopes. This paper presents a new deep learning-based multi-task framework for linear B-cell epitope prediction as well as antibody type-specific epitope classification. Specifically, a sequenced-based neural network model using recurrent layers and Transformer blocks is developed. We propose an amino acid encoding method based on eigen decomposition to help the model learn the representations of epitopes. We introduce modifications to standard cross-entropy loss functions by extending a logit adjustment technique to cope with the class imbalance. Experimental results on data curated from the largest public epitope database demonstrate the validity of the proposed methods and the superior performance compared to competing ones.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
