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A computational model for predicting proteins encoded by circadian genes. The support vector machine has been employed with Laplace kernel for prediction of circadian proteins, where compositional, transitional and physico-chemical features were utilized as numeric features. User can predict for the test dataset using the proposed computational model. Besides, the user can also build their own training model using their training dataset, followed by prediction for the test set.
| Version: | 1.0.2 |
| Depends: | R (≥ 3.3.3) |
| Imports: | Biostrings, protr, Peptides, kernlab, e1071 |
| Published: | 2020-12-11 |
| DOI: | 10.32614/CRAN.package.PredCRG |
| Author: | Prabina Kumar Meher |
| Maintainer: | Prabina Kumar Meher <meherprabin at yahoo.com> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| In views: | Omics |
| CRAN checks: | PredCRG results |
| Reference manual: | PredCRG.pdf |
| Package source: | PredCRG_1.0.2.tar.gz |
| Windows binaries: | r-devel: PredCRG_1.0.2.zip, r-release: PredCRG_1.0.2.zip, r-oldrel: PredCRG_1.0.2.zip |
| macOS binaries: | r-release (arm64): PredCRG_1.0.2.tgz, r-oldrel (arm64): PredCRG_1.0.2.tgz, r-release (x86_64): PredCRG_1.0.2.tgz, r-oldrel (x86_64): PredCRG_1.0.2.tgz |
| Old sources: | PredCRG archive |
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