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使用多个机器学习模型对PRRSV抗病毒的数据驱动发现

Wafa Yousaf¹, Abdul Haseeb¹, Yongheng Shen²

¹Shanxi Key Laboratory for Modernization of TCVM, College of Veterinary Medicine, Shanxi Agricultural University, Taigu, Shanxi, China.

Frontiers in veterinary science

|December 22, 2025

概括

这项研究使用蛋白质学和机器学习识别了针对猪生殖和呼吸系统综合征病毒 (PRRSV) 的抗病毒 (AVPs). 随机森林模型显示了最好的预测性能,用于识别潜在的PRRSV治疗方法.

科学领域:

生物化学和分子生物学
计算生物学和生物信息学
病毒学病毒学

背景情况:

蛋白质和网络分析对于理解生物过程和预测抗病毒性至关重要.
抗病毒 (AVP) 显示出广泛的抗病毒能力,但目前的数据库缺乏足够精确的注释.
猪生殖和呼吸系统综合征病毒 (PRRSV) 构成重大威胁,需要新的治疗策略.

研究的目的:

选不同表达的蛋白质和在健康和感染PRRSV的猪组织.
使用机器学习 (ML) 和深度学习 (DL) 计算方法预测新型AVP.
为了确定潜在的AVP治疗方法来对抗PRRSV.

主要方法:

蛋白质组学被用来量化来自肺部,小肠和大肠样本的蛋白质和.
机器学习 (ML) 和深度学习 (DL) 模型是使用物理化学特征 (如氨基酸组成,二级结构和水友性) 开发的.
使用深度学习图形神经网络 (GNN),与传统的ML模型进行基准测试,包括随机森林 (RF) 和支持矢量机器 (SVM).

主要成果:

氨酸,氨酸和白氨酸被确定为AVPs的显著预测特征.
随机森林 (RF) 模型实现了0.95 ± 2的最高曲线下面积 (AUC),超过了GNN和SVM模型 (0.94 ± 1).
发现氨基酸组成是识别AVP的关键预测因素.

结论:

将蛋白质组学与计算建模相结合,成功识别了针对PRRSV的抗病毒潜力的.
射频模型在AVP预测方面表现出优越的区分能力.
这些发现为实验验证和开发PRRSV AVPs作为治疗方法提供了宝贵的资源.

关键词:

PRRSVV 在这个问题上.氨基酸的组成氨基酸的组成抗病毒类的抗病毒.深度学习是一种深度学习.机器学习是机器学习.质谱测量质谱测量质谱测量质谱测量质量测量质谱测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量物理化学特性物理化学特性

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