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 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.
To use this package, the initial step involves installing both TensorFlow and Keras in Python, followed by establishing a connection to R. You can refer to the official TensorFlow documentation (https://tensorflow.rstudio.com) and the Keras documentation (https://keras.rstudio.com) for detailed instructions on these installations and connecting R with these libraries.
You can then install the DeProViR from bioconductor using:
if(!requireNamespace("BiocManager", quietly = TRUE)) {
install.packages("BiocManager")
}
BiocManager::install("DeProViR")To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("DeProViR")To install the development version in R, run:
if(!requireNamespace("devtools", quietly = TRUE)) {
install.packages("devtools")
}
devtools::install_github("mrbakhsh/DeProViR")Check the github page for source code
This project is licensed under the MIT License - see the LICENSE.md file for more details.