基于深层堆叠策略的登革热病毒广泛中和抗体的准确识别,具有多视角特征
Saeed Ahmed1,2, Nalini Schaduangrat1, Chonlatip Pipattanaboon3
1Faculty of Medical Technology, Center for Research Innovation and Biomedical Informatics, Mahidol University, Bangkok, 10700, Thailand.
Scientific reports
|December 10, 2025
概括
深堆-NAb准确地识别了针对登革热病毒 (DENV) 的广泛中和抗体 (bNAbs). 这种计算工具增强了bNAb的发现,改善了登革热治疗的开发.
科学领域:
- 免疫学 免疫学 免疫学
- 计算生物学 计算生物学
- 病毒学 病毒学
背景情况:
- 广泛中和抗体 (bNAbs) 对于对抗登革热病毒 (DENV) 感染至关重要.
- 实验性识别DENV bNAbs是资源密集型和耗时的.
- 在 silico 方法为高效的 bNAb 查提供了一个有希望的替代方案.
研究的目的:
- 开发一种高精度的计算方法,用于对所有四种DENV血清型进行bNAbs的识别.
- 改进现有的bNAb发现的in silico预测工具.
主要方法:
- 提出了Deepstack-NAb,这是一个堆叠组合模型,集成多个机器学习 (ML) 和深度学习 (DL) 算法.
- 从CDR-H3和表位数据中使用多源特征编码 (传统,NLP,PLM).
- 用于组合模型的特征选择和优化.
主要成果:
- 在交叉验证和独立测试中,Deepstack-NAb在基线模型和PredNAb相比表现优越.
- 在独立测试套件上实现了0.905准确度,0.922灵敏度和0.810MCC.
- 与PredNAb相比,显示出显著的改善,包括MCC增加20.65%.
结论:
- Deepstack-NAb是一个强大的计算工具,用于准确的DENV bNAb识别.
- 该方法显示出出色的预测和概括能力.
- 有潜力显著减少bNAb查中的假阴性,加速治疗开发.
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