Predicts anticancer peptides using random forests trained on the n-gram encoded peptides. The implemented algorithm can be accessed from both the command line and shiny-based GUI. The CancerGram model is too large for CRAN and it has to be downloaded separately from the repository: <https://github.com/BioGenies/CancerGramModel>. For more information see: Burdukiewicz et al. (2020) <doi:10.3390/pharmaceutics12111045>.
| Version: | 1.0.0 |
| Depends: | R (≥ 3.5.0) |
| Imports: | biogram,devtools,pbapply,ranger,shiny,stringi,dplyr |
| Suggests: | DT,ggplot2,pander,rmarkdown,shinythemes,spelling |
| Published: | 2020-11-19 |
| DOI: | 10.32614/CRAN.package.CancerGram |
| Author: | Michal Burdukiewicz [cre, aut], Katarzyna Sidorczuk [aut], Filip Pietluch [ctb], Dominik Rafacz [ctb], Mateusz Bakala [ctb], Jadwiga Słowik [ctb] |
| Maintainer: | Michal Burdukiewicz <michalburdukiewicz at gmail.com> |
| BugReports: | https://github.com/BioGenies/CancerGram/issues |
| License: | GPL-3 |
| URL: | https://github.com/BioGenies/CancerGram |
| NeedsCompilation: | no |
| Language: | en-US |
| Citation: | CancerGram citation info |
| Materials: | README |
| CRAN checks: | CancerGram results |