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Machine learning method specifically designed for pre-miRNA prediction. It takes advantage of unlabeled sequences to improve the prediction rates even when there are just a few positive examples, when the negative examples are unreliable or are not good representatives of its class. Furthermore, the method can automatically search for negative examples if the user is unable to provide them. MiRNAss can find a good boundary to divide the pre-miRNAs from other groups of sequences; it automatically optimizes the threshold that defines the classes boundaries, and thus, it is robust to high class imbalance. Each step of the method is scalable and can handle large volumes of data.
| Version: | 1.5 |
| Imports: | Matrix, stats, Rcpp, CORElearn, RSpectra |
| LinkingTo: | Rcpp |
| Published: | 2020-10-20 |
| DOI: | 10.32614/CRAN.package.miRNAss |
| Author: | Cristian Yones |
| Maintainer: | Cristian Yones <cyones at sinc.unl.edu.ar> |
| License: | Apache License 2.0 |
| NeedsCompilation: | yes |
| Materials: | README NEWS |
| CRAN checks: | miRNAss results |
| Reference manual: | miRNAss.pdf |
| Vignettes: |
miRNAss usage |
| Package source: | miRNAss_1.5.tar.gz |
| Windows binaries: | r-devel: miRNAss_1.5.zip, r-release: miRNAss_1.5.zip, r-oldrel: miRNAss_1.5.zip |
| macOS binaries: | r-release (arm64): miRNAss_1.5.tgz, r-oldrel (arm64): miRNAss_1.5.tgz, r-release (x86_64): miRNAss_1.5.tgz, r-oldrel (x86_64): miRNAss_1.5.tgz |
| Old sources: | miRNAss 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.