DeProViR

This is the development version of DeProViR; for the stable release version, see DeProViR.

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


Bioconductor version: Development (3.21)

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 (from within R, enter citation("DeProViR")):

Installation

To install this package, start R (version "4.5") and enter:


if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

# The following initializes usage of Bioc devel
BiocManager::install(version='devel')

BiocManager::install("DeProViR")

For older versions of R, please refer to the appropriate Bioconductor release.

Documentation

Reference Manual PDF

Details

biocViews Network, NetworkInference, NeuralNetwork, Proteomics, Software, SystemsBiology
Version 1.3.0
In Bioconductor since BioC 3.19 (R-4.4) (0.5 years)
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
Windows Binary (x86_64)
macOS Binary (x86_64)
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
Package Short Url https://bioconductor.org/packages/DeProViR/
Package Downloads Report Download Stats