A correlation coefficient-based feature selection approach for virus-host protein-protein interaction prediction.

ABSTRACT: Prediction of virus-host protein-protein interactions (PPI) is a broad research area where various machine-learning-based classifiers are developed. Transforming biological data into machine-usable features is a preliminary step in constructing these virus-host PPI prediction tools. In this study, we have adopted a virus-host PPI dataset and a reduced amino acids alphabet to create tripeptide features and introduced a correlation coefficient-based feature selection. We applied feature selection across several correlation coefficient metrics and statistically tested their relevance in a structural context. We compared the performance of feature-selection models against that of the baseline virus-host PPI prediction models created using different classification algorithms without the feature

SUBMITTER: Ibrahim AH 

PROVIDER: S-EPMC10153705 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature