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.

提出了一个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。实验证明了该方法的有效性和卓越的性能。