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deepredeff: Deep Learning Prediction of Effectors

A tool that contains trained deep learning models for predicting effector proteins. 'deepredeff' has been trained to identify effector proteins using a set of known experimentally validated effectors from either bacteria, fungi, or oomycetes. Documentation is available via several vignettes, and the paper by Kristianingsih and MacLean (2020) <doi:10.1101/2020.07.08.193250>.

Version: 0.1.1
Depends: R (≥ 2.10)
Imports: Biostrings, dplyr, ggplot2, ggthemes, keras, magrittr, purrr, reticulate, rlang, seqinr, tensorflow
Suggests: covr, kableExtra, knitr, rmarkdown, stringr, testthat
Published: 2021-07-16
DOI: 10.32614/CRAN.package.deepredeff
Author: Ruth Kristianingsih ORCID iD [aut, cre, cph]
Maintainer: Ruth Kristianingsih <ruth.kristianingsih30 at gmail.com>
BugReports: https://github.com/ruthkr/deepredeff/issues/
License: MIT + file LICENSE
URL: https://github.com/ruthkr/deepredeff/
NeedsCompilation: no
Materials: README NEWS
CRAN checks: deepredeff results

Documentation:

Reference manual: deepredeff.pdf
Vignettes: overview
predict

Downloads:

Package source: deepredeff_0.1.1.tar.gz
Windows binaries: r-devel: deepredeff_0.1.1.zip, r-release: deepredeff_0.1.1.zip, r-oldrel: deepredeff_0.1.1.zip
macOS binaries: r-release (arm64): deepredeff_0.1.1.tgz, r-oldrel (arm64): deepredeff_0.1.1.tgz, r-release (x86_64): deepredeff_0.1.1.tgz, r-oldrel (x86_64): deepredeff_0.1.1.tgz
Old sources: deepredeff archive

Linking:

Please use the canonical form https://CRAN.R-project.org/package=deepredeff to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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