BERTrand-peptide:TCR结合预测使用来自变压器的双向编码器表示,加上随机的TCR配对
Alexander Myronov1,2, Giovanni Mazzocco2, Paulina Król2
1Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland.
Bioinformatics (Oxford, England)
|August 3, 2023
概括
本研究介绍了BERTrand,这是一种用于预测:TCR结合的深度学习模型. BERTrand显示了改进的交叉一般化,在新序列上表现优于现有方法.
科学领域:
- 免疫信息学是指免疫信息学.
- 计算生物学 计算生物学
- 机器学习在免疫学中的应用
背景情况:
- T细胞受体 (TCR) 测序增加了:TCR结合数据.
- 现有的机器学习模型预测固定表位素的结合,但与新抗争.
- 准确预测:TCR相互作用对于理解免疫反应至关重要.
研究的目的:
- 开发一种具有增强跨概括能力的:TCR结合预测模型.
- 为了提高预测模型的性能,以前未见的序列.
- 为免疫信息学研究提供强大的计算工具.
主要方法:
- 已知:TCR结合剂的数据集被精选,并增加了来自健康捐赠者的T细胞库的负诱.
- 从自然语言处理中改编的深度学习技术被用于模型培训.
- 该BERTrand模型被训练来预测:TCR结合相互作用.
主要成果:
- 伯特兰德模型实现了0.69 AUROC的交叉基一般化性能.
- 与现有方法相比,BERTrand在未见的序列上进行评估时表现出优异的性能.
- 该模型有效地将预测推广到新表位.
结论:
- 开发的BERTrand模型可以更好地预测:TCR相互作用,特别是对于新.
- 这一进步对T细胞表位发现和疫苗设计有重大影响.
- 该研究提供了开放式访问数据集和代码,用于进一步研究免疫信息学.
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