## DeProViR
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.
## Installation
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:
```r
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:
```r
browseVignettes("DeProViR")
```
To install the development version in `R`, run:
```r
if(!requireNamespace("devtools", quietly = TRUE)) {
install.packages("devtools")
}
devtools::install_github("mrbakhsh/DeProViR")
```
## Contribute
Check the github page for [source code](https://github.com/mrbakhsh/DeProViR)
## License
This project is licensed under the MIT License - see the LICENSE.md
file for more details.
