DeProViR
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 gmail.com>
citation("DeProViR")):
Installation
To install this package, start R (version "4.4") and enter:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("DeProViR")
For older versions of R, please refer to the appropriate Bioconductor release.
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("DeProViR")| Introduction to DeProViR | HTML | R Script |
| Reference Manual | ||
| NEWS | Text | |
| LICENSE | Text |
Details
| 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 |
| System Requirements | |
| URL | https://github.com/mrbakhsh/DeProViR |
| Bug Reports | https://github.com/mrbakhsh/DeProViR/issues |
See More
| Suggests | rmarkdown, tensorflow, BiocStyle, RUnit, knitr, BiocGenerics |
| Linking To | |
| Enhances | |
| Depends On Me | |
| Imports Me | |
| Suggests Me | |
| Links To Me | |
| Build Report | Build Report |
Package Archives
Follow Installation instructions to use this package in your R session.
| Source Package | DeProViR_1.0.0.tar.gz |
| Windows Binary (x86_64) | DeProViR_1.0.0.zip |
| macOS Binary (x86_64) | DeProViR_1.0.0.tgz |
| macOS Binary (arm64) | |
| Source Repository | git clone https://git.bioconductor.org/packages/DeProViR |
| Source Repository (Developer Access) | git clone git@git.bioconductor.org:packages/DeProViR |
| Bioc Package Browser | https://code.bioconductor.org/browse/DeProViR/ |
| Package Short Url | https://bioconductor.org/packages/DeProViR/ |
| Package Downloads Report | Download Stats |