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使用机器学习分析a组轮状病毒的基因型特异性质的蛋白质决定因素

Myeongji Cho¹, Nara Been², Hyeon S Son³

¹Laboratory of Computational Virology & Viroinformatics, Graduate School of Public Health, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, South Korea; Public Health AI Lab, Graduate School of Public Health, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, South Korea; Institute of Health and Environment, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, South Korea.

Computers in biology and medicine

|April 9, 2025

概括

机器学习使用VP7和VP4蛋白质特征准确地分类A组轮状病毒 (RVA) 基因型. k-最近邻近算法表现出色,识别了用于预测病毒进化和宿主特异性的关键分子性质.

科学领域:

病毒学病毒学
生物信息学是一种生物信息学.
机器学习机器学习

背景情况:

组A轮状病毒 (RVA) 是哺乳动物和鸟类病毒性腹的主要原因.
VP7和VP4蛋白对RVA基因型特异性,感染性和宿主适应性至关重要.

研究的目的:

基于VP7和VP4蛋白质特性,使用机器学习对RVA基因型进行分类.
确定确定RVA基因型特异性的关键分子和物理化学特征.

主要方法:

从各种宿主物种收集了94个VP7和68个VP4蛋白序列.
应用了七种机器学习算法 (NB,LR,DT,RF,kNN,SVM,ANN) 来进行基因型分类.
使用准确度,精度,回忆,MCC和AUC进行评估的性能,具有特征选择.

主要成果:

k-最近邻近 (kNN) 算法获得了最高的准确性:VP7的97.87%和VP4.0的100%.
对于VP7,疏水性,正常化的范德瓦尔斯体积和白素成分是关键预测因素.
对于VP4,极性,正常化的范德瓦尔斯体积和极化性是最重要的.

结论:

VP7和VP4蛋白质的基因型特异性分子特征是RVA分类的可靠标记.
机器学习有效地根据氨基酸的物理化学特性预测RVA基因型.
研究结果提供了对RVA演变,宿主特异性和免疫逃避机制的见解.

关键词:

基因型的特异性基因型的特异性机器学习是机器学习.物理化学特性物理化学特性罗塔病毒是一种

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