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Using X-ray Crystallography, Biophysics, and Functional Assays to Determine the Mechanisms Governing T-cell Receptor Recognition of Cancer Antigens
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代的攻击和防御框架,以改进TCR-epitope绑定预测模型.

Pengfei Zhang1,2, Hao Mei1,2, Seojin Bang3

  • 1School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ 85281, United States.

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|July 15, 2025
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概括
此摘要是机器生成的。

本研究引入了一种攻击和防御框架,通过生成和从错误阳性结果中学习来改进T细胞受体 (TCR) -表皮质结合预测模型. 该方法增强了对抗模型的模型稳定性,这对于T细胞疗法和疫苗至关重要.

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科学领域:

  • 免疫学 免疫学 免疫学
  • 计算生物学 计算生物学
  • 人工智能的人工智能

背景情况:

  • 预测TCR-表皮质结合对于采用T细胞治疗和疫苗设计至关重要.
  • 现有模型由于有限的负样本数据而遭受错误阳性.
  • 目前的负样本生成方法无法解决模型特定的漏洞.

研究的目的:

  • 开发一个新的框架,系统地识别和缓解TCR-epitope预测模型中的弱点.
  • 为了提高TCR-epitope结合预测模型对虚假阳性的稳定性.
  • 创建一个全面的对抗负数据集,用于模型改进.

主要方法:

  • 提出一个代的攻击和防御框架,使用来自AI反 (RLAIF) 的强化学习.
  • 攻击阶段:生成生物学上不合理的序列来欺骗预测模型.
  • 防御阶段:将已识别的假阳性纳入微调数据集,以改善检测.

主要成果:

  • 成功地将框架应用于五种不同的TCR-epitope预测模型.
  • 显著改善了模型检测对抗假阳性结果的能力.
  • 创建了一个组合数据集,作为预测模型的基准测试工具.

结论:

  • 攻击和防御框架有效地提高了TCR-epitope预测模型的稳定性.
  • 生成的对抗性数据集改善了模型性能,并减少了假阳性.
  • 这种方法为改善生物预测模型提供了一种新的策略,使用有限的负性采样.