Package: sigminer.prediction 0.2.0
sigminer.prediction: Train and Predict Cancer Subtype with Keras Model based on Mutational Signatures
Mutational signatures represent mutational processes occured in cancer evolution, thus are stable and genetic resources for subtyping. This tool provides functions for training neutral network models to predict the subtype a sample belongs to based on 'keras' and 'sigminer' packages.
Authors:
sigminer.prediction_0.2.0.tar.gz
sigminer.prediction_0.2.0.zip(r-4.7)sigminer.prediction_0.2.0.zip(r-4.6)sigminer.prediction_0.2.0.zip(r-4.5)
sigminer.prediction_0.2.0.tgz(r-4.6-any)sigminer.prediction_0.2.0.tgz(r-4.5-any)
sigminer.prediction_0.2.0.tar.gz(r-4.7-any)sigminer.prediction_0.2.0.tar.gz(r-4.6-any)
sigminer.prediction_0.2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
sigminer.prediction/json (API)
| # Install 'sigminer.prediction' in R: |
| install.packages('sigminer.prediction', repos = c('https://shixiangwang.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/shixiangwang/sigminer.prediction/issues
kerasmutational-signaturesprostate-cancersigminer
Last updated from:0572bfc65f. Checks:7 ERROR, 2 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | ERROR | 165 | ||
| source / vignettes | OK | 239 | ||
| linux-release-x86_64 | ERROR | 182 | ||
| macos-release-arm64 | ERROR | 96 | ||
| macos-oldrel-arm64 | ERROR | 89 | ||
| windows-devel | ERROR | 166 | ||
| windows-release | ERROR | 150 | ||
| windows-oldrel | ERROR | 162 | ||
| wasm-release | OK | 170 |
Exports:batch_modeling_and_fittingcopy_modellist_trained_modelsload_trained_modelmodeling_and_fittingprepare_datatidy
Dependencies:abindbackportsbase64encBiobaseBiocGenericsBiocManagerbootbroomcarcarDatacaretclasscliclockclustercodetoolscolorspaceconfigcorrplotcowplotcpp11data.tableDerivdiagramdigestDNAcopydoBydoParalleldplyre1071farverforeachforecastFormulafracdifffurrrfuturefuture.applygenericsggplot2ggpubrggrepelggsciggsignifglobalsgluegowergridBasegridExtragtablehardhathereipredisobanditeratorsjsonlitekerasKernSmoothlabelinglatticelavalifecyclelistenvlme4lmtestlubridatemaftoolsmagrittrMASSMatrixMatrixModelsmgcvmicrobenchmarkminqaModelMetricsmodelrnlmenloptrNMFnnetnumDerivparallellypbkrtestpheatmappillarpkgconfigplyrpngpolynompROCprocessxprodlimprogressrproxypspurrrquantregR6rappdirsrbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRcppTOMLRdpackrecipesreformulasregistryreshape2reticulateRhtslibrlangrngtoolsrpartrprojrootrstatixrstudioapiS7scalesshapesigminerSparseMsparsevctrsSQUAREMstringistringrsurvivaltensorflowtfautographtfrunstibbletidyrtidyselecttimechangetimeDatetzdburcautf8vctrsviridisLitewhiskerwithryamlzeallotzoo
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Construct A Batch of Keras Models | batch_modeling_and_fitting |
| Copy Model File | copy_model |
| List Current Available Trained Keras Models | list_trained_models |
| Load Trained Models | load_trained_model |
| Create 5-layer Keras Model and Fitting Datasets | modeling_and_fitting |
| Prepare Training and Test Dataset | prepare_data |
| Tidy Modeling Result | tidy |
