Package: BioM2 1.1.0
Shunjie Zhang
BioM2: Biologically Explainable Machine Learning Framework
Biologically Explainable Machine Learning Framework for Phenotype Prediction using omics data described in Chen and Schwarz (2017) <doi:10.48550/arXiv.1712.00336>.Identifying reproducible and interpretable biological patterns from high-dimensional omics data is a critical factor in understanding the risk mechanism of complex disease. As such, explainable machine learning can offer biological insight in addition to personalized risk scoring.In this process, a feature space of biological pathways will be generated, and the feature space can also be subsequently analyzed using WGCNA (Described in Horvath and Zhang (2005) <doi:10.2202/1544-6115.1128> and Langfelder and Horvath (2008) <doi:10.1186/1471-2105-9-559> ) methods.
Authors:
BioM2_1.1.0.tar.gz
BioM2_1.1.0.tar.gz(r-4.5-noble)BioM2_1.1.0.tar.gz(r-4.4-noble)
BioM2_1.1.0.tgz(r-4.4-emscripten)BioM2_1.1.0.tgz(r-4.3-emscripten)
BioM2.pdf |BioM2.html✨
BioM2/json (API)
NEWS
# Install 'BioM2' in R: |
install.packages('BioM2', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
- GO2ALLEGS_BP - An example about pathlistDB
- GO_Ancestor - Pathways in the GO database and their Ancestor
- GO_Ancestor_exact - Pathways in the GO database and their Ancestor
- MethylAnno - An example about FeatureAnno for methylation data
- MethylData_Test - An example about TrainData/TestData for methylation data
- TransAnno - An example about FeatureAnno for gene expression
- TransData_Test - An example about TrainData/TestData for gene expression
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 3 months agofrom:37b8bdf1c7. Checks:OK: 2. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 20 2024 |
R-4.5-linux | OK | Nov 20 2024 |
Exports:AddUnmappedbaseModelBioM2FindParaModuleHyBioM2PathwaysModulePlotCorModulePlotPathFearturePlotPathInnerPlotPathNetShowModuleStage1_FeartureSelectionStage2_FeartureSelectionVisMultiModule
Dependencies:abindafexAnnotationDbiaskpassbackportsbase64encBayesFactorbayestestRbbotkBHBiobaseBiocGenericsBiostringsbitbit64bitopsblobbootbroombslibBWStestcachemcallrcarcarDatacaretcaToolscheckmateclasscliclockclueclusterCMplotcodacodetoolscolorspacecontfraccorrelationcorrplotcowplotcpp11crayoncurldata.tabledatawizardDBIDEoptimRDerivdeSolvediagramdigestdiptestdoBydoParalleldplyrdqrngdynamicTreeCute1071effectsizeellipticevaluatefansifarverfastclusterfastmapflexmixFNNfontawesomeforeachforeignFormulafpcfsfuturefuture.applygenericsGenomeInfoDbGenomeInfoDbDataggcorrplotggforceggnetworkggplot2ggpubrggrepelggsciggsideggsignifggstatsplotggthemesglobalsgluegmpGO.dbgowergplotsgridExtragtablegtoolshardhathighrHmischtmlTablehtmltoolshtmlwidgetshttrhypergeoigraphimputeinsightipredIRangesirlbaisobanditeratorsjiebaRjiebaRDjquerylibjsonliteKEGGRESTkernlabKernSmoothknitrkSampleslabelinglatticelavalgrlifecyclelistenvlme4lmerTestlubridatemagrittrMASSMatrixMatrixModelsmatrixStatsmclustmemoisemgcvmicrobenchmarkmimeminqamlbenchmlr3mlr3clustermlr3datamlr3filtersmlr3fselectmlr3hyperbandmlr3learnersmlr3mbomlr3measuresmlr3miscmlr3pipelinesmlr3tuningmlr3tuningspacesmlr3versemlr3vizModelMetricsmodelrmodeltoolsmultcompViewmunsellmvtnormnetworknlmenloptrnnetnumDerivopensslpaletteerpalmerpenguinsparadoxparallellyparameterspatchworkpbapplypbkrtestperformancepillarpkgconfigplogrplyrPMCMRpluspngpolyclippolynomprabcluspreprocessCoreprismaticpROCprocessxprodlimprogressrproxyPRROCpspurrrquantregR6rappdirsRColorBrewerRcppRcppAnnoyRcppEigenRcppProgressrecipesrematch2reshapereshape2rlangrmarkdownRmpfrrobustbaseROCRrpartRSpectraRSQLiterstatixrstudioapiS4VectorssassscalesshapesitmosnaspacefillrSparseMSQUAREMstabmstatnet.commonstatsExpressionsstringistringrSuppDistssurvivalsyssystemfontstibbletidyrtidyselecttimechangetimeDatetinytextweenrtzdbUCSC.utilsutf8uuiduwotvctrsviridisviridisLitewebshotWGCNAwithrwordcloud2WRS2xfunXVectoryamlzeallotzlibbioc