Package: MBMethPred 0.1.4.2
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_0.1.4.2.tar.gz
MBMethPred_0.1.4.2.zip(r-4.5)MBMethPred_0.1.4.2.zip(r-4.4)MBMethPred_0.1.4.2.zip(r-4.3)
MBMethPred_0.1.4.2.tgz(r-4.4-any)MBMethPred_0.1.4.2.tgz(r-4.3-any)
MBMethPred_0.1.4.2.tar.gz(r-4.5-noble)MBMethPred_0.1.4.2.tar.gz(r-4.4-noble)
MBMethPred_0.1.4.2.tgz(r-4.4-emscripten)MBMethPred_0.1.4.2.tgz(r-4.3-emscripten)
MBMethPred.pdf |MBMethPred.html✨
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 1 years agofrom:de3ad612cc. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 21 2024 |
| R-4.5-win | OK | Nov 21 2024 |
| R-4.5-linux | OK | Nov 21 2024 |
| R-4.4-win | OK | Nov 21 2024 |
| R-4.4-mac | OK | Nov 21 2024 |
| R-4.3-win | OK | Nov 21 2024 |
| R-4.3-mac | OK | Nov 21 2024 |
Exports:BoxPlotConfusionMatrixKNearestNeighborModelLinearDiscriminantAnalysisModelModelMetricsNaiveBayesModelNeuralNetworkModelNewDataPredictionResultRandomForestModelReadMethylFileReadSNFDataSimilarityNetworkFusionSupportVectorMachineModelTSNEPlotXGBoostModel
Dependencies:alluvialbackportsbase64encbitbit64bitopsbslibcachemcaretcaToolsclassclicliprclockcodetoolscolorspaceconfigcpp11crayondata.tablediagramdigestdplyre1071evaluateExPositionfansifarverfastmapfontawesomeforeachfsfuturefuture.applygenericsggplot2globalsgluegowergtablehardhatherehighrhmshtmltoolshtmlwidgetsipredisobanditeratorsjquerylibjsonlitekerasKernSmoothknitrlabelinglatticelavalifecyclelistenvlubridatemagrittrMASSMatrixmemoisemgcvmimeModelMetricsmunsellnlmennetnumDerivparallellypillarpkgconfigplyrpngprettyGraphsprettyunitspROCprocessxprodlimprogressprogressrproxypspurrrR6randomForestrappdirsRColorBrewerRcppRcppTOMLreadrrecipesreshape2reticulaterglrlangrmarkdownrpartrprojrootrstudioapiRtsnesassscalesshapeSNFtoolSQUAREMstringistringrsurvivaltensorflowtfautographtfrunstibbletidyrtidyselecttimechangetimeDatetinytextzdbutf8vctrsviridisLitevroomwhiskerwithrxfunxgboostyamlzeallot
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 |
