Medulloblastoma Subgroups Prediction
Overview
Utilizing a combination of machine learning models (Random Forest, Naive Bayes, K-Nearest Neighbor, Support Vector Machines, Extreme Gradient Boosting, and Linear Discriminant Analysis) and a deep Artificial Neural Network model, 'MBMethPred' can predict medulloblastoma subgroups, including wingless (WNT), sonic hedgehog (SHH), Group 3, and Group 4 from DNA methylation beta values. See Sharif Rahmani E, Lawarde A, Lingasamy P, Moreno SV, Salumets A and Modhukur V (2023), MBMethPred: a computational framework for the accurate classification of childhood medulloblastoma subgroups using data integration and AI-based approaches. Front. Genet. 14:1233657. <doi: 10.3389/fgene.2023.1233657> for more details.
Install
Health
NOTEdependencies in R coder-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-windows-x86_64
r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc
Base packages in Suggests/Enhances imported in NAMESPACE: ‘stats’ ‘utils’
r-devel-windows-x86_64
Base packages in Suggests/Enhances imported in NAMESPACE: 'stats' 'utils'
NOTERd contentsr-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc
Rd files without \usage: ‘KNearestNeighborModel.Rd’ ‘LinearDiscriminantAnalysisModel.Rd’ ‘NaiveBayesModel.Rd’ ‘NeuralNetworkModel.Rd’ ‘RandomForestModel.Rd’ ‘SupportVectorMachineModel.Rd’ ‘XGBoostModel.Rd’ \arguments should not be documented without \usage.
- NOTE8 OK, 5 NOTENow
- NOTE12 OK, 1 NOTE2026-07-11
- OK13 OK2026-06-09
- ERROR12 OK, 1 ERROR2026-06-08
- OK13 OK2026-05-02
- ERROR11 OK, 1 ERROR2026-04-25
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- NOTE12 OK, 2 NOTE2026-03-10
Documentation
- Help pages
- 19
- Examples that run
- 79%15 run in a plain check · 1 only when donttest examples are enabled (R CMD check --as-cran) · 3 never run
- Exported functions with examples
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 16%
Downloads
Repository
- PRs merged (all time)
- not measured
- Median days to close, 50 most recently updated closed issues
- 6
- Median days to close, 50 most recently updated closed PRs
- not measured
Checks run against github.com/sharifrahmanie/mbmethpred on 2026-10-18.
No development-tooling practices detected in the upstream repository.
Dependencies
Installing MBMethPred adds 119 packages (61 with compiled code, 81 MB of source tarballs). Heaviest: stringi 11 MB, ggplot2 6.0 MB, data.table 5.7 MB.
Code & Tests
Datasets
People & History
Pick two releases to compare their code metrics.
- RR 4.6.0 released · 2026-04-24
- 0.1.4.4Latest
- 0.1.4.32025-06-17 · diff ↗
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- 0.1.4.22023-09-18 · diff ↗
- 0.1.42023-07-11 · diff ↗
- 0.1.22023-07-03 · diff ↗
- 0.1.12023-07-01 · diff ↗
- RR 4.3.0 released · 2023-04-21
- 0.1.02022-12-13
- RR 4.2.0 released · 2022-04-22
Package metadata
- First published
- 2022-12-13
- Total releases
- 7 / 4 yrs
- License
- GPL
- Minimum R
- ≥ 3.5.0
- Bundled data
- 2.9 MB / 4 files
- Download size
- 3.0 MB
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