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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: | 0.1.4.2 |
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: | 2023-09-18 |
DOI: | 10.32614/CRAN.package.MBMethPred |
Author: | Edris Sharif Rahmani
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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 |
CRAN checks: | MBMethPred results |
Reference manual: | MBMethPred.pdf |
Vignettes: |
MBMethPred introduction |
Package source: | MBMethPred_0.1.4.2.tar.gz |
Windows binaries: | r-devel: MBMethPred_0.1.4.2.zip, r-release: MBMethPred_0.1.4.2.zip, r-oldrel: MBMethPred_0.1.4.2.zip |
macOS binaries: | r-release (arm64): MBMethPred_0.1.4.2.tgz, r-oldrel (arm64): MBMethPred_0.1.4.2.tgz, r-release (x86_64): MBMethPred_0.1.4.2.tgz, r-oldrel (x86_64): MBMethPred_0.1.4.2.tgz |
Old sources: | MBMethPred archive |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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