Package: BioM2 1.1.3

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:Shunjie Zhang [aut, cre], Junfang Chen [aut]

BioM2_1.1.3.tar.gz
BioM2_1.1.3.tar.gz(r-4.7-any)BioM2_1.1.3.tar.gz(r-4.6-any)
BioM2_1.1.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
BioM2/json (API)

# Install 'BioM2' in R:

install.packages('BioM2', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Datasets:

GO_Ancestor - Pathways in the GO database and their Ancestor
GO_Ancestor_exact - Pathways in the GO database and their Ancestor
GO2ALLEGS_BP - An example about pathlistDB
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

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.70 score 8 scripts 330 downloads 14 exports 243 dependencies

Last updated from:003de40881. Checks:4 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	OK	276
source / vignettes	OK	299
linux-release-x86_64	OK	282
wasm-release	OK	238

Exports:AddUnmapped baseModel BioM2 FindParaModule HyBioM2 PathwaysModule PlotCorModule PlotPathFearture PlotPathInner PlotPathNet ShowModule Stage1_FeartureSelection Stage2_FeartureSelection VisMultiModule

Dependencies:abind afex backports base64enc BayesFactor bayestestR bbotk BH bitops boot broom bslib BWStest cachem callr car carData caret caTools checkmate class cli clock cluster CMplot coda codetools colorspace contfrac correlation corrplot cowplot cpp11 data.table datawizard Deriv desc deSolve diagram digest doBy doParallel dplyr dqrng dynamicTreeCut e1071 effectsize elliptic evaluate farver fastcluster fastmap FNN fontawesome forcats foreach forecast foreign Formula fracdiff fs future future.apply generics ggcorrplot ggforce ggnetwork ggplot2 ggpubr ggrepel ggsci ggside ggsignif ggstatsplot ggthemes globals glue gmp gower gplots gridExtra gtable gtools hardhat highr Hmisc htmlTable htmltools htmlwidgets hypergeo igraph impute insight ipred irlba isoband iterators jquerylib jsonlite KernSmooth knitr kSamples labeling lattice lava lgr lifecycle listenv lme4 lmerTest lmtest lubridate magrittr MASS Matrix MatrixModels matrixStats memoise mgcv mime minqa mirai mlbench mlr3 mlr3cluster mlr3data mlr3filters mlr3fselect mlr3hyperband mlr3inferr mlr3learners mlr3mbo mlr3measures mlr3misc mlr3pipelines mlr3tuning mlr3tuningspaces mlr3verse mlr3viz ModelMetrics modelr moocore multcompView mvtnorm nanonext network nlme nloptr nnet numDeriv otel paletteer palmerpenguins paradox parallelly parameters patchwork pbapply pbkrtest performance pillar pkgconfig plyr PMCMRplus polyclip polynom preprocessCore prismatic pROC processx prodlim progressr proxy PRROC ps purrr quantreg R6 rappdirs rbibutils RColorBrewer Rcpp RcppAnnoy RcppArmadillo RcppEigen RcppParallel RcppProgress Rdpack recipes reformulas rematch2 reshape reshape2 rlang rmarkdown Rmpfr ROCR rpart RSpectra rstantools rstatix rstudioapi S7 sass scales shape sitmo sna spacefillr SparseM sparsevctrs SQUAREM stabm statnet.common statsExpressions stringi stringr SuppDists survival systemfonts tibble tidyr tidyselect timechange timeDate tinytex tweenr tzdb urca utf8 uuid uwot vctrs viridis viridisLite webshot WGCNA withr wordcloud2 WRS2 xfun yaml zoo

Help page	Topics
Add unmapped probe	AddUnmapped
Prediction by Machine Learning	baseModel
Biologically Explainable Machine Learning Framework	BioM2
Find suitable parameters for partitioning pathways modules	FindParaModule
Pathways in the GO database and their Ancestor	GO_Ancestor
Pathways in the GO database and their Ancestor	GO_Ancestor_exact
An example about pathlistDB	GO2ALLEGS_BP
BioM2 Hyperparametric Combination	HyBioM2
An example about FeatureAnno for methylation data	MethylAnno
An example about TrainData/TestData for methylation data	MethylData_Test
Delineate differential pathway modules with high biological interpretability	PathwaysModule
Correlalogram for Biological Differences Modules	PlotCorModule
Visualisation of significant pathway-level features	PlotPathFearture
Visualisation Original features that make up the pathway	PlotPathInner
Network diagram of pathways-level features	PlotPathNet
Display biological information within each pathway module	ShowModule
Stage 1 Fearture Selection	Stage1_FeartureSelection
Stage 2 Fearture Selection	Stage2_FeartureSelection
An example about FeatureAnno for gene expression	TransAnno
An example about TrainData/TestData for gene expression	TransData_Test
Visualisation of the results of the analysis of the pathway modules	VisMultiModule

Package: BioM2 1.1.3

BioM2: Biologically Explainable Machine Learning Framework

Citation

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)