miRNAss: Genome-Wide Discovery of Pre-miRNAs with few Labeled Examples

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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