Li Jie, Wang Shiming, Chen Zhuo, Wang Yadong
School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.
Front Genet. 2020 Jan 24;10:1357. doi: 10.3389/fgene.2019.01357. eCollection 2019.
Pathogen-host interactions play an important role in understanding the mechanism by which a pathogen can infect its host. Some approaches for predicting pathogen-host association have been developed, but prediction accuracy is still low. In this paper, we propose a bipartite network module-based approach to improve prediction accuracy. First, a bipartite network with pathogens and hosts is constructed. Next, pathogens and hosts are divided into different modules respectively. Then, modular information on the pathogens and hosts is added into a bipartite network projection model and the association scores between pathogens and hosts are calculated. Finally, leave-one-out cross-validation is used to estimate the performance of the proposed method. Experimental results show that the proposed method performs better in predicting pathogen-host association than other methods, and some potential pathogen-host associations with higher prediction scores are also confirmed by the results of biological experiments in the publically available literature.
病原体与宿主的相互作用在理解病原体感染宿主的机制方面起着重要作用。已经开发了一些预测病原体与宿主关联的方法，但预测准确性仍然较低。在本文中，我们提出了一种基于二分网络模块的方法来提高预测准确性。首先，构建一个包含病原体和宿主的二分网络。接下来，将病原体和宿主分别划分为不同的模块。然后，将病原体和宿主的模块信息添加到二分网络投影模型中，并计算病原体与宿主之间的关联分数。最后，使用留一法交叉验证来评估所提出方法的性能。实验结果表明，所提出的方法在预测病原体与宿主关联方面比其他方法表现更好，并且公开文献中的生物学实验结果也证实了一些预测分数较高的潜在病原体与宿主关联。
