A novel framework for phage-host prediction via logical probability theory and network sparsification.

ABSTRACT: Bacterial resistance has emerged as one of the greatest threats to human health, and phages have shown tremendous potential in addressing the issue of drug-resistant bacteria by lysing host. The identification of phage-host interactions (PHI) is crucial for addressing bacterial infections. Some existing computational methods for predicting PHI are suboptimal in terms of prediction efficiency due to the limited types of available information. Despite the emergence of some supporting information, the generalizability of models using this information is limited by the small scale of the databases. Additionally, most existing models overlook the sparsity of association data, which severely impacts their predictive performance as well. In this study, we propose a dual-view sparse network model

SUBMITTER: Wei A 

PROVIDER: S-EPMC11711101 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature