Prediction of influenza A virus-human protein-protein interactions using XGBoost with continuous and discontinuous amino acids information.

ABSTRACT: Influenza A virus (IAV) has the characteristics of high infectivity and high pathogenicity, which makes IAV infection a serious public health threat. Identifying protein-protein interactions (PPIs) between IAV and human proteins is beneficial for understanding the mechanism of viral infection and designing antiviral drugs. In this article, we developed a sequence-based machine learning method for predicting PPI. First, we applied a new negative sample construction method to establish a high-quality IAV-human PPI dataset. Then we used conjoint triad (CT) and Moran autocorrelation (Moran) to encode biologically relevant features. The joint consideration utilizing the complementary information between contiguous and discontinuous amino acids provides a more comprehensive description of PPI in

SUBMITTER: Li B 

PROVIDER: S-EPMC11787804 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature