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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
|
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 |
Reference manual: | deepredeff.pdf |
Vignettes: |
overview predict |
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 |
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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.