Multi-modal features-based human-herpesvirus protein-protein interaction prediction by using LightGBM.

ABSTRACT: The identification of human-herpesvirus protein-protein interactions (PPIs) is an essential and important entry point to understand the mechanisms of viral infection, especially in malignant tumor patients with common herpesvirus infection. While natural language processing (NLP)-based embedding techniques have emerged as powerful approaches, the application of multi-modal embedding feature fusion to predict human-herpesvirus PPIs is still limited. Here, we established a multi-modal embedding feature fusion-based LightGBM method to predict human-herpesvirus PPIs. In particular, we applied document and graph embedding approaches to represent sequence, network and function modal features of human and herpesviral proteins. Training our LightGBM models through our compiled non-rigorous and rig

SUBMITTER: Yang X 

PROVIDER: S-EPMC10818167 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature