Package: miRNAss
Type: Package
Title: Genome-Wide Discovery of Pre-miRNAs with few Labeled Examples
Version: 1.4
Author: Cristian Yones
Maintainer: Cristian Yones <cyones@sinc.unl.edu.ar>
Description: 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.
License: Apache License 2.0
Encoding: UTF-8
LazyData: true
Imports: Matrix, stats, Rcpp, CORElearn, RSpectra,
LinkingTo: Rcpp
NeedsCompilation: yes
RoxygenNote: 6.0.1
Packaged: 2018-01-02 20:18:26 UTC; cyones
Repository: CRAN
Date/Publication: 2018-01-03 01:22:17 UTC
Built: R 4.0.2; x86_64-w64-mingw32; 2020-08-02 11:32:14 UTC; windows
Archs: i386, x64
