Input file for prediction | Usage | Box plot | t-SNE 3D plot | Input file for similarity network fusion (SNF) | Similarity network fusion (SNF) | Support vector machine model | K nearest neighbor model | Random forest model | XGBoost model | Linear discriminant analysis model | Naive bayes model | Artificial neural network model
Package: MBMethPred 0.1.4.4
MBMethPred: Medulloblastoma Subgroups Prediction
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.
Authors:
MBMethPred_0.1.4.4.tar.gz
MBMethPred_0.1.4.4.zip(r-4.7)MBMethPred_0.1.4.4.zip(r-4.6)MBMethPred_0.1.4.4.zip(r-4.5)
MBMethPred_0.1.4.4.tgz(r-4.6-any)MBMethPred_0.1.4.4.tgz(r-4.5-any)
MBMethPred_0.1.4.4.tar.gz(r-4.7-any)MBMethPred_0.1.4.4.tar.gz(r-4.6-any)
MBMethPred_0.1.4.4.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
MBMethPred/json (API)
| # Install 'MBMethPred' in R: |
| install.packages('MBMethPred', repos = c('https://sharifrahmanie.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/sharifrahmanie/mbmethpred/issues
Last updated from:b9cc9208ae. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 260 | ||
| source / vignettes | OK | 283 | ||
| linux-release-x86_64 | OK | 594 | ||
| macos-release-arm64 | OK | 264 | ||
| macos-oldrel-arm64 | OK | 274 | ||
| windows-devel | OK | 154 | ||
| windows-release | OK | 203 | ||
| windows-oldrel | OK | 153 | ||
| wasm-release | OK | 206 |
Exports:BoxPlotConfusionMatrixKNearestNeighborModelLinearDiscriminantAnalysisModelModelMetricsNaiveBayesModelNeuralNetworkModelNewDataPredictionResultRandomForestModelReadMethylFileReadSNFDataSimilarityNetworkFusionSupportVectorMachineModelTSNEPlotXGBoostModel
Dependencies:alluvialbackportsbase64encbitbit64bitopsbslibcachemcaretcaToolsclassclicliprclockcodetoolsconfigcpp11crayondata.tablediagramdigestdplyre1071evaluateExPositionfarverfastmapfontawesomeforeachfsfuturefuture.applygenericsggplot2globalsgluegowergtablehardhatherehighrhmshtmltoolshtmlwidgetsipredisobanditeratorsjquerylibjsonlitekerasKernSmoothknitrlabelinglatticelavalifecyclelistenvlubridatemagrittrMASSMatrixmemoisemimeModelMetricsnlmennetnumDerivparallellypillarpkgconfigplyrpngprettyGraphsprettyunitspROCprocessxprodlimprogressprogressrproxypspurrrR6randomForestrappdirsRColorBrewerRcppRcppTOMLreadrrecipesreshape2reticulaterglrlangrmarkdownrpartrprojrootrstudioapiRtsneS7sassscalesshapeSNFtoolsparsevctrsSQUAREMstringistringrsurvivaltensorflowtfautographtfrunstibbletidyrtidyselecttimechangetimeDatetinytextzdbutf8vctrsviridisLitevroomwhiskerwithrxfunxgboostyamlzeallot
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Box plot | BoxPlot |
| Confusion matrix | ConfusionMatrix |
| Training data | Data1 dataset1 |
| Data2 | Data2 dataset2 |
| Data3 | Data3 dataset3 |
| K nearest neighbor model | KNearestNeighborModel |
| Linear discriminant analysis model | LinearDiscriminantAnalysisModel |
| Model metrics | ModelMetrics |
| Naive bayes model | NaiveBayesModel |
| Artificial neural network model | NeuralNetworkModel |
| New data prediction result | NewDataPredictionResult |
| Random forest model | RandomForestModel |
| Input file for prediction | ReadMethylFile |
| Input file for similarity network fusion (SNF) | ReadSNFData |
| RLabels | RLabels Subgroups |
| Similarity network fusion (SNF) | SimilarityNetworkFusion |
| Support vector machine model | SupportVectorMachineModel |
| t-SNE 3D plot | TSNEPlot |
| XGBoost model | XGBoostModel |
