In gokmenzararsiz/MLSeq: Machine Learning Interface for RNA-Seq Data

Introduction

MLSeq is an R/BIOCONDUCTOR package, which provides several algorithms including support vector machines (SVM), bagging support vector machines (bagSVM), random forest (RF) and classification and regression trees (CART) to classify sequencing data. To achieve this, MLSeq package requires a count table, which contains the number of reads mapped to each transcript for each sample. This kind of count data can be obtained from RNA-Seq experiments, also from other sequencing experiments such as DNA or ChIP-sequencing.

To install the MLSeq package in R:

source("http://bioconductor.org/biocLite.R")
biocLite("MLSeq")

If you use MLSeq package in your research, please cite it as below:

Gokmen Zararsiz, Dincer Goksuluk, Selcuk Korkmaz, Vahap Eldem, Izzet Parug Duru, Turgay Unver and Ahmet Ozturk (2016). MLSeq: Machine Learning Interface for RNA-Seq Data. R package version 1.15.3.

To get BibTeX entry for LaTeX users, type the following:

citation("MLSeq")

Please contact us, if you have any questions or suggestions:

[e-mail]

News:

Version 1.15.3

• Model training options are now determined using the control arguement within classify(...) function.
• control arguement is a wrapper of trainControl(...) function from caret package. Any option from trainControl(...) can be used while fitting the classifier using classify function.
• Other arguements from train(...) can be used within wrapper functions classify(...) and predictClassify(...)

gokmenzararsiz/MLSeq documentation built on May 17, 2019, 7:41 a.m.