毒力
重新分配
计算机科学
甲型流感病毒
约束(计算机辅助设计)
范畴变量
计算生物学
病毒学
人工智能
病毒
机器学习
基因
生物
遗传学
医学
传染病(医学专业)
2019年冠状病毒病(COVID-19)
数学
几何学
疾病
病理
作者
Rui Yin,Zihan Luo,Pei Zhuang,Min Zeng,Min Li,Zhuoyi Lin,Chee Keong Kwoh
标识
DOI:10.1016/j.jbi.2023.104388
摘要
Influenza viruses pose great threats to public health and cause enormous economic losses every year. Previous work has revealed the viral factors associated with the virulence of influenza viruses in mammals. However, taking prior viral knowledge represented by heterogeneous categorical and discrete information into account to explore virus virulence is scarce in the existing work. How to make full use of the preceding domain knowledge in virulence study is challenging but beneficial. This paper proposes a general framework named ViPal for virulence prediction in mice that incorporates discrete prior viral mutation and reassortment information based on all eight influenza segments. The posterior regularization technique is leveraged to transform prior viral knowledge into constraint features and integrated into the machine learning models. Experimental results on influenza genomic datasets validate that our proposed framework can improve virulence prediction performance over baselines. The comparison between ViPal and other existing methods shows the computational efficiency of our framework with comparable or superior performance. Moreover, the interpretable analysis through SHAP (SHapley Additive exPlanations) identifies the scores of constraint features contributing to the prediction. We hope this framework could provide assistance for the accurate detection of influenza virulence and facilitate flu surveillance.
科研通智能强力驱动
Strongly Powered by AbleSci AI