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GTE:用于预测T细胞受体和表位体结合特异性的图形学习框架

Feng Jiang¹, Yuzhi Guo¹, Hehuan Ma¹

¹Department of Computer Science and Engineering, University of Texas at Arlington, 701 S. Nedderman Drive, TX 76019, United States.

Briefings in bioinformatics

|July 15, 2024

概括

本研究介绍了GTE,这是一个图形神经网络模型,通过分析网络拓学来预测T细胞受体 (TCR) 和表位相互作用. 通过结合非约束性对和解决数据不平衡,GTE提高了预测准确性.

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
机器学习机器学习

背景情况:

与主要基因相容性复合体 (MHC) 分子的T细胞受体 (TCR) 和 (表位) 相互作用是适应性免疫的核心.
预测这些相互作用对于理解免疫反应,疾病发病因子和开发免疫疗法至关重要.

研究的目的:

开发一种新的计算模型,GTE,利用TCR-Epitope交互网络的拓结构来提高预测准确性.
通过结合网络拓来解决现有的基于序列的方法的局限性.

主要方法:

开发了GTE,一个异质的Graph神经网络模型,采用诱导学习来捕获TCR-Epitope交互拓.
实施了动态边缘更新策略,以有效地构建负样本 (非约束对).
适应深度AUC最大化用于基于图表的数据不平衡缓解.

主要成果:

与现有方法相比,GTE在四个公共数据集中预测TCR-表皮质相互作用方面表现出卓越的表现.
该模型的有效性凸显了分子相互作用网络中拓结构的重要性.
这项研究证实了分析复杂分子网络拓学的好处.

结论:

通过有效利用网络拓学,GTE模型提供了一种强大的新方法来预测TCR-Epitope相互作用.
这项工作强调了基于图形的学习和拓特征提取在免疫信息学中的重要性.
这些发现为加强对免疫反应的理解和操纵铺平了道路.

关键词:

T细胞受体受体的 T 细胞受体.深度AUC最大化可以实现.标本特异性的特异性不同质图形神经网络的神经网络.免疫信息学是一种免疫信息学.感应式学习是一种感应式学习.

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