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A Deep-Learning Framework Based on Pre-trained Sequence Embeddings for Predicting Host-Viral Protein-Protein Interactions

Bioconductor version: Release (3.19)

Emerging infectious diseases, exemplified by the zoonotic COVID-19 pandemic caused by SARS-CoV-2, are grave global threats. Understanding protein-protein interactions (PPIs) between host and viral proteins is essential for therapeutic targets and insights into pathogen replication and immune evasion. While experimental methods like yeast two-hybrid screening and mass spectrometry provide valuable insights, they are hindered by experimental noise and costs, yielding incomplete interaction maps. Computational models, notably DeProViR, predict PPIs from amino acid sequences, incorporating semantic information with GloVe embeddings. DeProViR employs a Siamese neural network, integrating convolutional and Bi-LSTM networks to enhance accuracy. It overcomes the limitations of feature engineering, offering an efficient means to predict host-virus interactions, which holds promise for antiviral therapies and advancing our understanding of infectious diseases.

Author: Matineh Rahmatbakhsh [aut, trl, cre]

Maintainer: Matineh Rahmatbakhsh <matinerb.94 at>

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Introduction to DeProViR HTML R Script
Reference Manual PDF


biocViews Network, NetworkInference, NeuralNetwork, Proteomics, Software, SystemsBiology
Version 1.0.0
In Bioconductor since BioC 3.19 (R-4.4) (< 6 months)
License MIT+ file LICENSE
Depends keras
Imports caret, data.table, dplyr, fmsb, ggplot2, grDevices, pROC, PRROC, readr, stats, BiocFileCache, utils
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