使用XGBoost与连续和不连续的氨基酸信息预测流感A病毒-人类蛋白质-蛋白质相互作用
Binghua Li1,2,3, Xin Li1,2,3, Xiaoyu Li1,2,3
1College of Informatics, Huazhong Agricultural University, Wuhan, China.
PeerJ
|February 3, 2025
概括
我们开发了一种机器学习方法来预测流感A型病毒 (IAV) 和人类蛋白质与蛋白质相互作用 (PPI),确定了新抗病毒疗法的3,269种潜在药物标.
科学领域:
- 计算生物学是一种计算生物学.
- 病毒学 病毒学
- 生物信息学是一种生物信息学.
背景情况:
- 甲型流感病毒 (IAV) 由于其高传染性和致病性,对公共卫生构成重大风险.
- 了解宿主-病原体蛋白-蛋白相互作用 (PPI) 对于阐明病毒机制和开发抗病毒策略至关重要.
研究的目的:
- 开发一个基于序列的机器学习模型,用于预测IAV-人类PPI.
- 为了识别与IAV相互作用的新型宿主蛋白,以潜在的治疗向.
主要方法:
- 使用一种新的负样本构造方法,创建了一个高质量的IAV-人类PPI数据集.
- 联合三元 (CT) 和莫兰自相关 (Moran) 特性被用于基于序列的编码.
- 极端梯度提升 (XGBoost) 模型被优化并验证为PPI预测.
主要成果:
- 该XGBoost模型实现了高性能96.89%的准确性,98.79%的精度,和96.78%的F1-score.
- 确定了3,269种潜在的IAV相互作用的人类蛋白质.
- 功能性丰富分析 (基因本体学,途径分析) 和网络分析证实了预测蛋白在IAV感染中的相关性.
结论:
- 开发的机器学习方法有效地预测了IAV-人类PPI.
- 已确定的潜在标蛋白为开发新型抗流感疗法提供了有前途的途径.
- 这项研究为流感研究和药物发现提供了宝贵的资源和见解.
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