ranktreeensemble
Science Score: 13.0%
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Low similarity (8.6%) to scientific vocabulary
Repository
Basic Info
- Host: GitHub
- Owner: TransBioInfoLab
- Language: R
- Default Branch: main
- Size: 568 KB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 2
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
ranktreeEnsemble: an R package for implementing ensemble methods of rank-based trees as single-sample predictors for gene expression classification
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* The package is also available at R CRAN: https://CRAN.R-project.org/package=ranktreeEnsemble
* A python version can be found at: https://github.com/RuijieYin/EnsembleMethodsofRankBasedTreespy
Authors
Ruijie Yin (ruijieyin428@gmail.com), Chen Ye (cxy364@miami.edu) and Min Lu (m.lu6@umiami.edu)
Reference
Lu M. Yin R. and Chen X.S. Ensemble Methods of Rank-Based Trees for Single Sample Classification with Gene Expression Profiles. Journal of Translational Medicine. 22, 140 (2024). https://doi.org/10.1186/s12967-024-04940-2
Description
Fast computing an ensemble of rank-based trees via boosting or random forest on binary and multi-class problems. It converts continuous gene expression profiles into ranked gene pairs, for which the variable importance indices are computed and adopted for dimension reduction. Decision rules can be extracted from trees.
Installation
install.packages("ranktreeEnsemble")
library(ranktreeEnsemble)
Examples
- Build a Random Rank Forest with Variable Importance: ``` data(tnbc) obj <- rforest(subtype~., data = tnbc[1:100,c(1:5,337)]) importance(obj) predict(obj)$label predict(obj, tnbc[101:110,1:5])$label
pair() to convert continuous variables to binary ranked pairs
tnbc[101:110,1:5] datp <- pair(tnbc[101:110,1:5]) datp predict(obj, datp, newdata.pair = TRUE)$label ```
- Extract Interpretable Decision Rules: ``` objr <- extract.rules(obj) objr$rule[1:5,] predict(objr)$label[1:5]
objrs <- select.rules(objr,tnbc[110:130,c(1:5,337)]) predict(objrs, tnbc[111:120,1:5])$label objrs$rule[1:5,] ```
- Build a Boosting model with LogitBoost Cost with Variable Importance:
objb <- rboost(subtype~., data = tnbc[1:100,c(1:5,337)]) importance(objb) predict(objb)$label predict(objb, tnbc[101:110,1:5])$label
Owner
- Name: Translational Statistical Bioinformatics Lab
- Login: TransBioInfoLab
- Kind: organization
- Email: TransBioInfoLab@gmail.com
- Location: Miami, FL
- Website: https://transbioinfolab.org
- Repositories: 27
- Profile: https://github.com/TransBioInfoLab
GitHub Events
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- Watch event: 1
Last Year
- Watch event: 1
Packages
- Total packages: 1
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Total downloads:
- cran 190 last-month
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 3
- Total maintainers: 1
cran.r-project.org: ranktreeEnsemble
Ensemble Models of Rank-Based Trees with Extracted Decision Rules
- Homepage: https://github.com/TransBioInfoLab/ranktreeEnsemble/
- Documentation: http://cran.r-project.org/web/packages/ranktreeEnsemble/ranktreeEnsemble.pdf
- License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
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Latest release: 0.23
published about 2 years ago
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Maintainers (1)
Dependencies
- R >= 3.5.0 depends
- Rcpp >= 1.0.10 imports
- data.tree * imports
- gbm * imports
- methods * imports
- randomForestSRC * imports