Package: DeProViR
Type: Package
Title: A Deep-Learning Framework Based on Pre-trained Sequence Embeddings for 
   Predicting Host-Viral Protein-Protein Interactions
Version: 1.3.1
Authors@R: 
    person("Matineh", "Rahmatbakhsh", ,"matinerb.94@gmail.com",role = c("aut","trl","cre"))
Description: 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.
License: MIT+ file LICENSE
Encoding: UTF-8
URL: https://github.com/mrbakhsh/DeProViR
BugReports: https://github.com/mrbakhsh/DeProViR/issues
Depends: 
    keras
Imports: 
    caret,
    data.table,
    dplyr,
    fmsb,
    ggplot2,
    grDevices,
    pROC,
    PRROC,
    readr,
    stats,
    BiocFileCache,
    utils 
VignetteBuilder: knitr
Suggests:
   rmarkdown,
   tensorflow,
   BiocStyle,
   RUnit,
   knitr,
   BiocGenerics
biocViews: 
    Proteomics,
    SystemsBiology,
    NetworkInference,
    NeuralNetwork,
    Network
RoxygenNote: 7.3.1
PackageStatus: Deprecated