CBIL-VHPLI: a model for predicting viral-host protein-lncRNA interactions based on machine learning and transfer learning.

ABSTRACT: Virus‒host protein‒lncRNA interaction (VHPLI) predictions are critical for decoding the molecular mechanisms of viral pathogens and host immune processes. Although VHPLI interactions have been predicted in both plants and animals, they have not been extensively studied in viruses. For the first time, we propose a new deep learning-based approach that consists mainly of a convolutional neural network and bidirectional long and short-term memory network modules in combination with transfer learning named CBIL‒VHPLI to predict viral-host protein‒lncRNA interactions. The models were first trained on large and diverse datasets (including plants, animals, etc.). Protein sequence features were extracted using a k-mer method combined with the one-hot encoding and composition-transition-distributio

SUBMITTER: Zhang M 

PROVIDER: S-EPMC11289117 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature