IFNg_DeepKG:在生物医学应用中使用知识图RAG识别干扰素-马诱导位的新型模型.
Van The Le1, Juan Peter Timothy Yuune1, Yu-Yen Ou1,2
1Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, Taoyuan City 32003, Taiwan.
Journal of chemical information and modeling
|December 31, 2025
概括
一个新的深度学习框架,IFNg_DeepKG,通过将生物背景与序列数据集成来增强表位预测. 这种方法显著改善了用于疫苗和免疫治疗设计的干扰素-诱导表位体 (IFNgIE) 的识别.
科学领域:
- 计算免疫学计算免疫学
- 生物信息学是一种生物信息学.
- 机器学习在药物发现中的作用
背景情况:
- 准确识别干扰素-诱导表位 (IFNgIE) 对于设计有效的疫苗和免疫疗法至关重要.
- 当前的计算模型往往忽视了重要的生物背景,限制了它们对表位免疫性预测准确性的准确性.
研究的目的:
- 开发一个新的深度学习框架,IFNg_DeepKG,它将基于序列的模式与丰富的生物背景集成在一起,以改进IFNgIE预测.
- 通过更准确的表位标识来增强下一代疫苗和免疫疗法的设计.
主要方法:
- IFNg_DeepKG框架结合了一个预训练的蛋白质语言模型 (ESM2),一个定制的知识图 (KG) 与检索增强生成 (RAG) 以及一个多尺度卷积神经网络 (MSCNN).
- 该RAG-KG以外部生物信息 (来源蛋白,宿主,疾病关联) 丰富了序列嵌入,以改善免疫性预测.
主要成果:
- IFNg_DeepKG在独立测试数据集上取得了卓越的性能,AUC为0.99 (人类H_IFNgInd1) 和0.95 (老鼠M_IFNgInd1).
- 在具有挑战性的数据集上表现出强大的跨物种泛化,AUC为0.94 (H_IFNgInd2) 和0.93 (M_IFNgInd2).
- 成功识别和分类与COVID-19和阿尔茨海默病等疾病相关的临床相关表位.
结论:
- IFNg_DeepKG通过将基于序列的特征和生物背景相结合,显著推进了计算免疫学.
- 该框架提供了一个可扩展和强大的平台,用于合理的表位发现,精准医学,以及开发新型疫苗和免疫疗法.
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