Package: miRNAss 1.5

Cristian Yones

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.

Authors:Cristian Yones

miRNAss_1.5.tar.gz
miRNAss_1.5.tar.gz(r-4.5-noble)miRNAss_1.5.tar.gz(r-4.4-noble)
miRNAss_1.5.tgz(r-4.4-emscripten)miRNAss_1.5.tgz(r-4.3-emscripten)
miRNAss.pdf |miRNAss.html
miRNAss/json (API)
NEWS

# Install 'miRNAss' in R:
install.packages('miRNAss', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:
  • celegans - Features extracted from hairpins of Caenorhabditis elegans.

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

cppopenmp

2.70 score 1 stars 3 scripts 168 downloads 3 exports 11 dependencies

Last updated 4 years agofrom:86518bc353. Checks:OK: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 27 2024
R-4.5-linux-x86_64OKNov 27 2024

Exports:adjacencyMatrixKNNeigenDecompositionmiRNAss

Dependencies:clusterCORElearnlatticeMatrixnnetplotrixRcppRcppEigenrpartrpart.plotRSpectra

miRNAss usage

Rendered frommiRNAss.Rnwusingutils::Sweaveon Nov 27 2024.

Last update: 2017-11-02
Started: 2017-05-06