## Introduction
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<br>

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:

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

```{r, eval = FALSE}
citation("MLSeq")
```

<br>

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

  gokmenzararsiz@hotmail.com

## 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(...)`