Accurate identification of neoantigens is important for advancing cancer immunotherapies. This study introduces Neoantigen MUlti-taSk Tower (NeoMUST), a model employing multi-task learning to effectively capture task-specific information across related tasks. Our results show that NeoMUST rivals existing algorithms in predicting the presentation of neoantigens via MHC-I molecules, while demonstrating a significantly shorter training time for enhanced computational efficiency. The use of multi-task learning enables NeoMUST to leverage shared knowledge and task dependencies, leading to improved performance metrics and a significant reduction in the training time. NeoMUST, implemented in Python, is freely accessible at the GitHub repository. Our model will facilitate neoantigen prediction and empower the development of effective cancer immunotherapeutic approaches.

中文翻译：

---

NeoMUST：用于新抗原呈现的准确且高效的多任务学习模型。

新抗原的准确鉴定对于推进癌症免疫疗法非常重要。本研究介绍了 Neoantigen 多任务塔 (NeoMUST)，这是一种采用多任务学习来有效捕获相关任务中的特定任务信息的模型。我们的结果表明，NeoMUST 在通过 MHC-I 分子预测新抗原的呈现方面可与现有算法相媲美，同时证明训练时间显着缩短，从而提高了计算效率。多任务学习的使用使 NeoMUST 能够利用共享知识和任务依赖性，从而提高性能指标并显着减少训练时间。NeoMUST 以 Python 实现，可在 GitHub 存储库中免费访问。我们的模型将促进新抗原预测并促进有效癌症免疫治疗方法的开发。