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R new unit tests included. Jun 29, 2018
inst/extdata Major Changes in MLSeq, version moved to 2.x.y from 1.x.y May 18, 2018
man Bibliography error fixed for vignette. Jun 5, 2018
tests new unit tests included. Jun 29, 2018
.gitignore removed all and added with git ignore (revised) May 21, 2018
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README.Rmd NEWS & README added. May 18, 2018


## Introduction
[![Build Status](](


MLSeq is an R/BIOCONDUCTOR package, which provides over 90 algorithms including support vector machines (SVM),random forest (RF), classification and regression trees (CART), Poisson and Negative Binomial Linear Discriminant Analysis (PLDA, NBLDA) and voom-based classifiers (voomDLDA, voomNSC, etc.) for the classification of sequencing data. MLSeq requires a count table as an input 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. MLSeq includes both normalization (e.g deseq median ratio, trimmed mean of M values) and transformation (variance stabiliation transformation, regularized logarithmic transformation, etc.) techniques which can be performed through classification process. Although the main purpose of MLSeq is to classify samples using a count matrix from RNA-Sequencing data, some of the classifiers which are called sparse classifiers such as PLDA and voomNSC can be used to detect significant features. 

To install the MLSeq package in R:

```{r, eval = FALSE, message=FALSE, warning=FALSE}

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

> Gokmen Zararsiz, Dincer Goksuluk, Selcuk Korkmaz, Vahap Eldem, Izzet Parug Duru, Ahmet Ozturk and Ahmet Ergun Karaagaoglu (2018). MLSeq: Machine Learning Interface for RNA-Seq Data. R package version 2.1.0.

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

```{r, eval = FALSE}


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

## News:

#### Major changes in version 2.x.y

* Functions are reconstructed using S4 systems and new classes such as `MLSeq`, `MLSeqMetaData` and `MLSeqModelInfo`.
* New classifiers from [caret]( package are now available for MLSeq. These functions can be used for transformed continuous data using one of transformation techniques which are provided by MLSeq's classification algorithms.
* A complete list of available classifiers can be viewed using `availableMethods()` and `printAvailableMethods()`.
* New setter and getter functions are included.
* Predictions are now evaluated usin generic function `predict(...)`. The older function `predictClassify(...)` can also be used for predictions.
* For more details see package manuals.