Package: MLSeq 2.31.0

Gokmen Zararsiz

MLSeq: Machine Learning Interface for RNA-Seq Data

This package applies several machine learning methods, including SVM, bagSVM, Random Forest and CART to RNA-Seq data.

Authors:Gokmen Zararsiz [aut, cre], Dincer Goksuluk [aut], Selcuk Korkmaz [aut], Vahap Eldem [aut], Izzet Parug Duru [ctb], Ahmet Ozturk [aut], Ahmet Ergun Karaagaoglu [aut, ths]

MLSeq_2.31.0.tar.gz
MLSeq_2.31.0.zip(r-4.7)MLSeq_2.31.0.zip(r-4.6)MLSeq_2.31.0.zip(r-4.5)
MLSeq_2.31.0.tgz(r-4.6-any)MLSeq_2.31.0.tgz(r-4.5-any)
MLSeq_2.31.0.tar.gz(r-4.7-any)MLSeq_2.31.0.tar.gz(r-4.6-any)
MLSeq_2.31.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
MLSeq/json (API)
NEWS

# Install 'MLSeq' in R:

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

Datasets:

cervical - Cervical cancer data

On BioConductor:MLSeq-2.31.0(bioc 3.24)MLSeq-2.30.0(bioc 3.23)

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

immunooncology sequencing rnaseq classification clustering

4.96 score 1 packages 38 scripts 470 downloads 4 mentions 32 exports 123 dependencies

Last updated from:f69a70062c. Checks:1 ERROR, 7 NOTE, 2 OK. Indexed: yes.

Target	Result	Time
bioc-checks	ERROR	197
linux-devel-x86_64	NOTE	373
source / vignettes	OK	337
linux-release-x86_64	NOTE	345
macos-release-arm64	NOTE	202
macos-oldrel-arm64	NOTE	201
windows-devel	NOTE	266
windows-release	NOTE	265
windows-oldrel	NOTE	243
wasm-release	OK	151

Exports:availableMethods classify confusionMat control control<-discreteControl input isModified isModified<-isUpdated isUpdated<-metaData method method<-modelInfo normalization normalization<-plot predict predictClassify preProcessing preProcessing<-print printAvailableMethods ref ref<-selectedGenes trained trainParameters transformation update voomControl

Dependencies:abind BH Biobase BiocGenerics BiocParallel bitops brio callr caret caTools class cli clock cluster codetools cpp11 crayon data.table DelayedArray desc DESeq2 diagram diffobj digest dplyr e1071 edgeR evaluate farver foreach formatR fs futile.logger futile.options future future.apply generics GenomicRanges ggplot2 globals glue gower gtable hardhat ipred IRanges isoband iterators jsonlite KernSmooth labeling lambda.r lattice lava lifecycle limma listenv locfit lubridate magrittr MASS Matrix MatrixGenerics matrixStats ModelMetrics nlme nnet numDeriv pamr parallelly pillar pkgbuild pkgconfig pkgload plyr praise pROC processx prodlim progressr proxy ps purrr R6 RColorBrewer Rcpp RcppArmadillo recipes reshape2 rlang rpart rprojroot S4Arrays S4Vectors S7 scales Seqinfo shape snow SparseArray sparsevctrs SQUAREM sSeq statmod stringi stringr SummarizedExperiment survival testthat tibble tidyr tidyselect timechange timeDate tzdb utf8 vctrs VennDiagram viridisLite waldo withr xtable XVector

Beginner's guide to the "MLSeq" package

Rendered fromMLSeq.Rnwusingknitr::knitron May 30 2026.

Last update: 2021-08-14
Started: 2014-03-20

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Machine learning interface for RNA-Seq data	MLSeq-package
Available classification/regression methods in 'MLSeq'	Available-classifiers availableMethods printAvailableMethods
Cervical cancer data	cervical
Fitting classification models to sequencing data	classify
Accessors for the 'confusionMat' slot.	confusionMat confusionMat,MLSeq-method confusionMat,MLSeqModelInfo-method
Accessors for the 'control' slot.	control control,MLSeq-method control<- control<-,MLSeq,list-method
'discrete.train' object	discrete.train-class
Define controlling parameters for discrete classifiers (NBLDA and PLDA)	discreteControl
Accessors for the 'inputObject' slot of an 'MLSeq' object	input input,MLSeq-method
Checks if MLSeq object is updated/modified or not.	isModified isModified,MLSeq-method isModified<- isModified<-,MLSeq,logical-method isUpdated isUpdated,MLSeq-method isUpdated<- isUpdated<-,MLSeq,logical-method
Accessors for the 'metaData' slot of an 'MLSeq' object	metaData metaData,MLSeq-method
Accessors for the 'method'.	method method,MLSeq-method method,MLSeqModelInfo-method method<- method<-,MLSeq,character-method
'MLSeq' object	MLSeq-class
'MLSeqMetaData' object	MLSeqMetaData-class
'MLSeqModelInfo' object	MLSeqModelInfo-class
Accessors for the 'modelInfo' slot of an 'MLSeq' object	modelInfo modelInfo,MLSeq-method
Accessors for the 'normalization' slot.	normalization normalization,MLSeq-method normalization,MLSeqModelInfo-method normalization<- normalization<-,MLSeq,character-method
Plot accuracy results from 'MLSeq' object	plot plot,MLSeq,ANY-method plot.MLSeq
Extract predictions from 'classify()' object	predict predict,MLSeq-method predict.MLSeq predictClassify
Accessors for the 'preProcessing' slot of an 'MLSeq' object	preProcessing preProcessing,MLSeq-method preProcessing<- preProcessing<-,MLSeq,character-method
Print method for confusion matrix	print,confMat-method print.confMat
Accessors for the 'ref' slot.	ref ref,MLSeq-method ref,MLSeqModelInfo-method ref<- ref<-,MLSeq,character-method
Accessors for the 'selectedGenes'.	selectedGenes selectedGenes,MLSeq-method
Show method for MLSeq objects	show show,discrete.train-method show,MLSeq-method show,MLSeqMetaData-method show,MLSeqModelInfo-method show,voom.train-method show.MLSeq
Accessors for the 'trainedModel' slot.	trained trained,MLSeq-method trained,MLSeqModelInfo-method
Accessors for the 'trainParameters' slot.	trainParameters trainParameters,MLSeq-method trainParameters,MLSeqModelInfo-method
Accessors for the 'transformation' slot.	transformation transformation,MLSeq-method transformation,MLSeqModelInfo-method
Update 'MLSeq' objects returnd from 'classify()'	update update,MLSeq-method update.MLSeq
'voom.train' object	voom.train-class
Define controlling parameters for voom-based classifiers	voomControl