Package: RANKS 1.1
RANKS: Ranking of Nodes with Kernelized Score Functions
Implementation of Kernelized score functions and other semi-supervised learning algorithms for node label ranking to analyze biomolecular networks. RANKS can be easily applied to a large set of different relevant problems in computational biology, ranging from automatic protein function prediction, to gene disease prioritization and drug repositioning, and more in general to any bioinformatics problem that can be formalized as a node label ranking problem in a graph. The modular nature of the implementation allows to experiment with different score functions and kernels and to easily compare the results with baseline network-based methods such as label propagation and random walk algorithms, as well as to enlarge the algorithmic scheme by adding novel user-defined score functions and kernels.
Authors:
RANKS_1.1.tar.gz
RANKS_1.1.zip(r-4.5)RANKS_1.1.zip(r-4.4)RANKS_1.1.zip(r-4.3)
RANKS_1.1.tgz(r-4.4-x86_64)RANKS_1.1.tgz(r-4.4-arm64)RANKS_1.1.tgz(r-4.3-x86_64)RANKS_1.1.tgz(r-4.3-arm64)
RANKS_1.1.tar.gz(r-4.5-noble)RANKS_1.1.tar.gz(r-4.4-noble)
RANKS_1.1.tgz(r-4.4-emscripten)RANKS_1.1.tgz(r-4.3-emscripten)
RANKS.pdf |RANKS.html✨
RANKS/json (API)
| # Install 'RANKS' in R: |
| install.packages('RANKS', repos = c('https://gvalentini58.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 2 years agofrom:5d89a45f24. Checks:OK: 9. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Oct 08 2024 |
| R-4.5-win-x86_64 | OK | Oct 08 2024 |
| R-4.5-linux-x86_64 | OK | Oct 08 2024 |
| R-4.4-win-x86_64 | OK | Oct 08 2024 |
| R-4.4-mac-x86_64 | OK | Oct 08 2024 |
| R-4.4-mac-aarch64 | OK | Oct 08 2024 |
| R-4.3-win-x86_64 | OK | Oct 08 2024 |
| R-4.3-mac-x86_64 | OK | Oct 08 2024 |
| R-4.3-mac-aarch64 | OK | Oct 08 2024 |
Exports:cauchy.kernelcompute.acccompute.Fdo.cv.datado.GBAdo.loo.RANKSdo.RANKSdo.RWdo.RWRdo.stratified.cv.dataeav.scoreeav.w.scorefind.optimal.thresh.cvgaussian.kernelGBAmaxGBAsumidentity.kernelinv.multiquadric.kernelker.score.classifier.cvker.score.classifier.holdoutker.score.cvker.score.holdoutKNN.scoreKNN.w.scorelabel.proplabelsfromscoreslaplacian.kernellinear.kernelmultiple.ker.score.cvmultiple.ker.score.thresh.cvMultiple.labels.from.scoresmultiple.RW.cvNN.scoreNN.w.scorenorm1p.step.rw.kernelpoly.kernelRWRW.cvrw.kernelRWRselection.testsingle.eav.scoresingle.eav.w.scoresingle.KNN.scoresingle.KNN.w.scoresingle.NN.scoresingle.NN.w.scoresingle.WSLD.scoreUnit.sphere.normWSLD.score
Dependencies:BHBiocGenericsgraphlimmaNetPreProcPerfMeasRBGLstatmod
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| RANKS: Ranking of Nodes with Kernelized Score Functions | RANKS-package RANKS |
| GBA cross-validation experiments with multiple classes | do.GBA |
| RANKS leave-one-out experiments with multiple classes | do.loo.RANKS |
| RANKS cross-validation experiments with multiple classes | do.RANKS |
| Random walk cross-validation experiments with multiple classes | do.RW |
| Random walk with restart cross-validation experiments with multiple classes | do.RWR |
| Function to find the optimal RANKS score thereshold | find.optimal.thresh.cv |
| Guilt By Association (GBA) using the maximum rule | GBAmax |
| Guilt By Association (GBA) using the sum rule | GBAsum |
| Multiple cross-validation with RANKS for classification | ker.score.classifier.cv |
| RANKS held-out procedure for a single class | ker.score.classifier.holdout ker.score.holdout |
| RANKS cross-validation for a single class | ker.score.cv |
| Kernel functions | cauchy.kernel gaussian.kernel identity.kernel inv.multiquadric.kernel Kernel functions laplacian.kernel linear.kernel poly.kernel |
| Label propagation | label.prop |
| RANKS multiple cross-validation for a single class | multiple.ker.score.cv |
| Function for RANKS multiple cross-validation and optimal threshold finding for a single class | multiple.ker.score.thresh.cv |
| Random walk, GBA and labelprop multiple cross-validation for a single class | multiple.RW.cv |
| Random walk on a graph | RW |
| Random walk, GBA and labelprop cross-validation for a single class | RW.cv |
| Random walk kernel | p.step.rw.kernel p.step.rw.kernel,graph-method p.step.rw.kernel,matrix-method p.step.rw.kernel-methods rw.kernel rw.kernel,graph-method rw.kernel,matrix-method rw.kernel-methods |
| Random walk with Restart on a graph | RWR |
| Multiple vertex score functions | eav.score eav.score,graph-method eav.score,matrix-method eav.score-methods KNN.score KNN.score,graph-method KNN.score,matrix-method KNN.score-methods Methods for scoring multiple vertices NN.score NN.score,graph-method NN.score,matrix-method NN.score-methods WSLD.score WSLD.score,graph-method WSLD.score,matrix-method WSLD.score-methods |
| Single vertex score functions | Methods for scoring a single vertex single.eav.score single.eav.score,graph-method single.eav.score,matrix-method single.eav.score-methods single.KNN.score single.KNN.score,graph-method single.KNN.score,matrix-method single.KNN.score-methods single.NN.score single.NN.score,graph-method single.NN.score,matrix-method single.NN.score-methods single.WSLD.score single.WSLD.score,graph-method single.WSLD.score,matrix-method single.WSLD.score-methods |
| Utility functions | compute.acc compute.F do.cv.data do.stratified.cv.data labelsfromscores Multiple.labels.from.scores norm1 selection.test Unit.sphere.norm |
| Multiple vertex score functions - weighted version | eav.w.score eav.w.score,graph-method eav.w.score,matrix-method eav.w.score-methods KNN.w.score KNN.w.score,graph-method KNN.w.score,matrix-method KNN.w.score-methods Methods for scoring multiple vertices - weighted version NN.w.score NN.w.score,graph-method NN.w.score,matrix-method NN.w.score-methods |
| Single vertex score functions - weighted version | Methods for scoring a single vertex - weighted version single.eav.w.score single.eav.w.score,graph-method single.eav.w.score,matrix-method single.eav.w.score-methods single.KNN.w.score single.KNN.w.score,graph-method single.KNN.w.score,matrix-method single.KNN.w.score-methods single.NN.w.score single.NN.w.score,graph-method single.NN.w.score,matrix-method single.NN.w.score-methods |
