Package: LedPred 1.39.0
Aitor Gonzalez
LedPred: Learning from DNA to Predict Enhancers
This package aims at creating a predictive model of regulatory sequences used to score unknown sequences based on the content of DNA motifs, next-generation sequencing (NGS) peaks and signals and other numerical scores of the sequences using supervised classification. The package contains a workflow based on the support vector machine (SVM) algorithm that maps features to sequences, optimize SVM parameters and feature number and creates a model that can be stored and used to score the regulatory potential of unknown sequences.
Authors:
LedPred_1.39.0.tar.gz
LedPred_1.39.0.zip(r-4.5)LedPred_1.39.0.zip(r-4.4)LedPred_1.39.0.zip(r-4.3)
LedPred_1.39.0.tgz(r-4.4-any)LedPred_1.39.0.tgz(r-4.3-any)
LedPred_1.39.0.tar.gz(r-4.5-noble)LedPred_1.39.0.tar.gz(r-4.4-noble)
LedPred_1.39.0.tgz(r-4.4-emscripten)LedPred_1.39.0.tgz(r-4.3-emscripten)
LedPred.pdf |LedPred.html✨
LedPred/json (API)
NEWS
| # Install 'LedPred' in R: |
| install.packages('LedPred', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/aitgon/ledpred/issues
- crm.features - This is data to be included in my package
- feature.ranking - This is data to be included in my package
On BioConductor:LedPred-1.39.0(bioc 3.20)LedPred-1.38.0(bioc 3.19)
Last updated 2 months agofrom:7d6e85fdaf
Exports:createModelevaluateModelPerformanceLedPredmapFeaturesToCRMsmcTunerankFeaturesscoreDatatuneFeatureNb
Dependencies:akimabitopsbriocallrcaToolsclassclicolorspacecrayondescdiffobjdigeste1071evaluatefansifarverfsggplot2gluegplotsgtablegtoolsirrisobandjsonliteKernSmoothlabelinglatticelifecyclelpSolvemagrittrMASSMatrixmgcvmisc3dmunsellnlmepillarpkgbuildpkgconfigpkgloadplot3DplyrpraiseprocessxproxypsR6RColorBrewerRcppRCurlrematch2rlangROCRrprojrootscalessptestthattibbleutf8vctrsviridisLitewaldowithr
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Create the model with the optimal features | createModel |
| This is data to be included in my package | crm.features |
| Evaluate model performances | evaluateModelPerformance |
| This is data to be included in my package | feature.ranking |
| Creates an SVM model given a feature matrix | LedPred |
| R interface to bed_to_matrix REST in server | mapFeaturesToCRMs |
| Tuning the SVM parameters | mcTune |
| Ranking the features according to their importance | rankFeatures |
| Predicting new regulatory regions | scoreData |
| Selecting the optimal number of features | tuneFeatureNb |
