Package: BioM2 1.0.10

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_1.0.10.tar.gz
BioM2_1.0.10.tar.gz(r-4.5-noble)BioM2_1.0.10.tar.gz(r-4.4-noble)
BioM2_1.0.10.tgz(r-4.4-emscripten)BioM2_1.0.10.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'))

Datasets:

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.

14 exports 1.08 score 265 dependencies 11 scripts 590 downloads

Last updated 28 days agofrom:b644d0524e. Checks:OK: 2. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Aug 21 2024
R-4.5-linux	OK	Aug 21 2024

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

Dependencies:abind afex AnnotationDbi askpass backports base64enc BayesFactor bayestestR bbotk BH Biobase BiocGenerics Biostrings bit bit64 bitops blob boot broom bslib BWStest cachem callr car carData caret caTools checkmate class cli clock clue cluster CMplot coda codetools colorspace contfrac correlation corrplot cowplot cpp11 crayon curl data.table datawizard DBI DEoptimR Deriv deSolve diagram digest diptest doBy doParallel dplyr dqrng dynamicTreeCut e1071 effectsize elliptic evaluate fansi farver fastcluster fastmap flexmix FNN fontawesome foreach foreign Formula fpc fs future future.apply generics GenomeInfoDb GenomeInfoDbData ggcorrplot ggforce ggnetwork ggplot2 ggpubr ggrepel ggsci ggside ggsignif ggstatsplot ggthemes globals glue gmp GO.db gower gplots gridExtra gtable gtools hardhat highr Hmisc htmlTable htmltools htmlwidgets httr hypergeo igraph impute insight ipred IRanges irlba isoband iterators jiebaR jiebaRD jquerylib jsonlite KEGGREST kernlab KernSmooth knitr kSamples labeling lattice lava lgr lifecycle listenv lme4 lmerTest lubridate magrittr MASS Matrix MatrixModels matrixStats mclust memoise mgcv microbenchmark mime minqa mlbench mlr3 mlr3cluster mlr3data mlr3filters mlr3fselect mlr3hyperband mlr3learners mlr3mbo mlr3measures mlr3misc mlr3pipelines mlr3tuning mlr3tuningspaces mlr3verse mlr3viz ModelMetrics modelr modeltools multcompView munsell mvtnorm network nlme nloptr nnet numDeriv openssl paletteer palmerpenguins paradox parallelly parameters patchwork pbapply pbkrtest performance pillar pkgconfig plogr plyr PMCMRplus png polyclip polynom prabclus preprocessCore prismatic pROC processx prodlim progressr proxy PRROC ps purrr quantreg R6 rappdirs RColorBrewer Rcpp RcppAnnoy RcppEigen RcppProgress recipes rematch2 reshape reshape2 rlang rmarkdown Rmpfr robustbase ROCR rpart RSpectra RSQLite rstatix rstudioapi S4Vectors sass scales shape sitmo sna spacefillr SparseM SQUAREM stabm statnet.common statsExpressions stringi stringr SuppDists survival sys systemfonts tibble tidyr tidyselect timechange timeDate tinytex tweenr tzdb UCSC.utils utf8 uuid uwot vctrs viridis viridisLite webshot WGCNA withr wordcloud2 WRS2 xfun XVector yaml zeallot zlibbioc

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 1.0.10

BioM2: Biologically Explainable Machine Learning Framework

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Help Manual

Usage by other packages (reverse dependencies)