# --------------------------------------------
# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
# --------------------------------------------
 
cff-version: 1.2.0
message: 'To cite package "DeProViR" in publications use:'
type: software
license: MIT
title: 'DeProViR: A Deep-Learning Framework Based on Pre-trained Sequence Embeddings
  for Predicting Host-Viral Protein-Protein Interactions'
version: 1.1.0
abstract: 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.
authors:
- family-names: Rahmatbakhsh
  given-names: Matineh
  email: matinerb.94@gmail.com
repository: https://bioc.r-universe.dev
repository-code: https://github.com/mrbakhsh/DeProViR
url: https://github.com/mrbakhsh/DeProViR
contact:
- family-names: Rahmatbakhsh
  given-names: Matineh
  email: matinerb.94@gmail.com