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cff-version: 1.2.0
message: 'To cite package "GenProSeq" in publications use:'
type: software
license: Artistic-2.0
title: 'GenProSeq: Generating Protein Sequences with Deep Generative Models'
version: 1.9.0
abstract: Generative modeling for protein engineering is key to solving fundamental
  problems in synthetic biology, medicine, and material science. Machine learning
  has enabled us to generate useful protein sequences on a variety of scales. Generative
  models are machine learning methods which seek to model the distribution underlying
  the data, allowing for the generation of novel samples with similar properties to
  those on which the model was trained. Generative models of proteins can learn biologically
  meaningful representations helpful for a variety of downstream tasks. Furthermore,
  they can learn to generate protein sequences that have not been observed before
  and to assign higher probability to protein sequences that satisfy desired criteria.
  In this package, common deep generative models for protein sequences, such as variational
  autoencoder (VAE), generative adversarial networks (GAN), and autoregressive models
  are available. In the VAE and GAN, the Word2vec is used for embedding. The transformer
  encoder is applied to protein sequences for the autoregressive model.
authors:
- family-names: Jung
  given-names: Dongmin
  email: dmdmjung@gmail.com
  orcid: https://orcid.org/0000-0001-7499-8422
repository: https://bioc.r-universe.dev
date-released: '2024-02-06'
contact:
- family-names: Jung
  given-names: Dongmin
  email: dmdmjung@gmail.com
  orcid: https://orcid.org/0000-0001-7499-8422