biostudies-literatureUnknown10(45)Sajnani VIdentification of protein-protein interaction (PPI) sites is crucial for understanding molecular recognition. Experimental identification of PPI is expensive, time-consuming, and laborious. A large number of computational methods addressed this problem. However, no computations specifically addressed PPI site prediction for the frequently mutating influenza A virus (IAV) genome that invades human hosts. For the first time, we report the prediction of PPI sites on the IAV genome (protein sequences). The method was benchmarked across various machine-learning models, optimizing class imbalance and unlabeled data types. The best-performing models were (i) the gradient boosting model, augmented with minority class oversampling and positive unlabeled (PU) learning and (ii) the protein-specific bACS omega54185-54199https://www.ebi.ac.uk/biostudies/studies/S-EPMC12631352biostudies-literatureIdentification of Protein-Protein Interaction (PPI) Sites on the Influenza A (H1N1) Viral Genome Using Gradient Boosting and Artificial Neural Network (ANN) Models.PMC12631352Das SSajnani VBandyopadhyay DAli OfalseIdentification of Protein-Protein Interaction (PPI) Sites on the Influenza A (H1N1) Viral Genome Using Gradient Boosting and Artificial Neural Network (ANN) Models.Identification of protein-protein interaction (PPI) sites is crucial for understanding molecular recognition. Experimental identification of PPI is expensive, time-consuming, and laborious. A large number of computational methods addressed this problem. However, no computations specifically addressed PPI site prediction for the frequently mutating influenza A virus (IAV) genome that invades human hosts. For the first time, we report the prediction of PPI sites on the IAV genome (protein sequences). The method was benchmarked across various machine-learning models, optimizing class imbalance and unlabeled data types. The best-performing models were (i) the gradient boosting model, augmented with minority class oversampling and positive unlabeled (PU) learning and (ii) the protein-specific b2025-01-01T00:00:00Z2025 Nov2026-06-05T17:37:58.162Z2026-05-19T03:11:48.901ZS-EPMC126313524128085110.1021/acsomega.5c06314