A machine learning based approach for fungal species identification using barcode sequence data. The multi-class random forest model has been used for prediction purpose, where the gap-pair compositional feature was used to encode the barcode sequence data. The encoded dataset was used as input for prediction purpose. Though this approach has been developed for fungal species identification in particular, can be used for other species identification as well.
| Version: | 1.0.2 |
| Depends: | R (≥ 3.3.0) |
| Imports: | randomForest, Biostrings, BioSeqClass |
| Published: | 2019-05-27 |
| Author: | Prabina Kumar Meher |
| Maintainer: | Prabina Kumar Meher <meherprabin at yahoo.com> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| CRAN checks: | funbarRF results |
| Reference manual: | funbarRF.pdf |
| Package source: | funbarRF_1.0.2.tar.gz |
| Windows binaries: | r-devel: funbarRF_1.0.2.zip, r-release: funbarRF_1.0.2.zip, r-oldrel: funbarRF_1.0.2.zip |
| macOS binaries: | r-release: not available, r-oldrel: funbarRF_1.0.2.tgz |
| Old sources: | funbarRF archive |
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