**3.4 Classification and clustering algorithms of machine learning for RNA-seq data**

Classification in machine learning is a supervised learning approach in which the algorithm learns from the data given to it and makes new observations, then applies the conclusions to new data. Clustering on the other hand is an unsupervised learning problem for grouping unlabeled features. The learning algorithm that learns the model from the training data and maps the input data to a specific class is called classifier, in the following section, we briefly present three widely used classifiers for grouping RNA-seq data.


To test these algorithms, we used MLSeq (Machine learning interface for RNAsequencing data) which is an R package including more than 80 machine learning algorithms and a pipeline to classify RNA-seq data including normalization, filtering and transformation steps [18].
