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.
Version: 0.1.4.3 Depends: R (≥ 3.5.0) Imports: stringr, ggplot2, parallel, caTools, caret, keras, MASS, Rtsne, SNFtool, class, dplyr, e1071, pROC, randomForest, readr, reshape2, reticulate, rgl, tensorflow, xgboost Suggests: knitr, rmarkdown, testthat, utils, stats, scales Published: 2025-06-17 DOI: 10.32614/CRAN.package.MBMethPred Author: Edris Sharif Rahmani [aut, ctb, cre], Ankita Sunil Lawarde [aut, ctb], Vijayachitra Modhukur [aut, ctb] Maintainer: Edris Sharif Rahmani <rahmani.biotech at gmail.com> BugReports: https://github.com/sharifrahmanie/MBMethPred/issues License: GPL-2 | GPL-3 [expanded from: GPL] URL: https://github.com/sharifrahmanie/MBMethPred NeedsCompilation: no Materials: README CRAN checks: MBMethPred results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=MBMethPred to link to this page.
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