Galaxy Machine Learning Community

The Galaxy Machine Learning workbench is a comprehensive set of data preprocessing, machine learning, deep learning and visualisation tools, consolidated workflows for end-to-end machine learning analysis and training materials to showcase the usage of these tools. The workbench is available on the Galaxy framework, which guarantees simple access, easy extension, flexible adaption to personal and security needs, and sophisticated machine learning analyses independent of command-line knowledge.

The workbench provides you with a Swiss Army knife of scikit-learn, Keras (a deep learning library based on TensorFlow) and various other tools to transform, learn and predict and plot your data.

The workbench is currently developed by the Goecks Lab and the European Galaxy project. The German Network for Bioinformatics Infrastructure (de.NBI), which runs the German ELIXIR Node, provides the necessary compute clusters with CPUs and GPU resources.

The project is a community effort, please jump in, ask questions, and contribute to the development of new tools, workflows or trainings!


We are passionate about training. So we are working in close collaboration with the Galaxy Training Network (GTN) to develop training materials of data analyses based on Galaxy. These materials hosted on the GTN GitHub repository are available online at

Want to learn more about machine learning? Take one of our guided tours or check out the following hands-on tutorials, developed together with the GTN community.

Lesson Slides Hands-on Input dataset Workflows Galaxy tour Galaxy History
Basics of machine learning
Age prediction using machine learning
Introduction to deep learning

Available tools

In this section we list the most important tools that have been integrated into the Machine Learning workbench. There are many more tools available so please have a more detailed look at the tool panel at All tools follow the IUC best practise guidelines for Galaxy tool development and are available under and For better readability, we have listed the most powerful tools below and divided them into categories.


Identifying which category an object belongs to.

Tool Description Reference
SVM Classifier Support vector machines (SVMs) for classification Pedregosa et al. 2011
NN Classifier Nearest Neighbors Classification Pedregosa et al. 2011
Ensemble classification Ensemble methods for classification and regression Pedregosa et al. 2011
Discriminant Classifier Linear and Quadratic Discriminant Analysis Pedregosa et al. 2011
Generalized linear Generalized linear models for classification and regression Pedregosa et al. 2011
CLF Metrics Calculate metrics for classification performance Pedregosa et al. 2011


Predicting a continuous-valued attribute associated with an object.

Tool Description Reference
Ensemble regression Ensemble methods for classification and regression Pedregosa et al. 2011
Generalized linear Generalized linear models for classification and regression Pedregosa et al. 2011
Regression metrics Calculate metrics for regression performance Pedregosa et al. 2011


Automatic grouping of similar objects into sets.

Tool Description Reference
Numeric clustering Different numerical clustering algorithms Pedregosa et al. 2011

Model building

Building general machine learning models.

Tool Description Reference
Estimator Attributes Estimator attributes to get all attributes from an estimator or scikit object Pedregosa et al. 2011
Stacking Ensemble Models Stacking Ensembles to build stacking, voting ensemble models with numerous base options Pedregosa et al. 2011
Search CV Hyperparameter Search performs hyperparameter optimization using various SearchCVs Pedregosa et al. 2011
Build Pipeline Pipeline Builder as an all-in-one platform to build pipeline, single estimator, preprocessor and custom wrappers Pedregosa et al. 2011

Model evaluation

Evaluation, validating and choosing parameters and models.

Tool Description Reference
Model validation Model Validation includes cross_validate, cross_val_predict, learning_curve, and more Pedregosa et al. 2011
Pairwise Metrics Evaluate pairwise distances or compute affinity or kernel for sets of samples Pedregosa et al. 2011
Train/Test evaluation Train, Test and Evaluation to fit a model using part of dataset and evaluate using the rest Pedregosa et al. 2011
Model Prediction Model Prediction predicts on new data using a preffited model Chollet et al. 2011
Fitted model evaluation Evaluate a Fitted Model using a new batch of labeled data Pedregosa et al. 2011
Model fitting Fit a Pipeline, Ensemble or other models using a labeled dataset Pedregosa et al. 2011

Preprocessing and feature selection

Feature selection and preprocessing.

Tool Description Reference
Data preprocessing Preprocess raw feature vectors into standardized datasets Pedregosa et al. 2011
Feature selection Feature Selection module, including univariate filter selection methods and recursive feature elimination algorithm Pedregosa et al. 2011

Deep learning

Build and use deep neural networks.

Tool Description Reference
Batch Models Build Deep learning Batch Training Models with online data generator for Genomic/Protein sequences and images Chollet et al. 2011
Model Builder Create deep learning model with an optimizer, loss function and fit parameters Chollet et al. 2011
Model Config Create a deep learning model architecture using Keras Chollet et al. 2011
Train and evaluation Deep learning training and evaluation either implicitly or explicitly Chollet et al. 2011


Plotting and visualization.

Tool Description Reference
Regression performance plots Plot actual vs predicted curves and residual plots of tabular data
ML performance plots Plot confusion matrix, precision, recall and ROC and AUC curves of tabular data
Visualization Machine Learning Visualization Extension includes several types of plotting for machine learning Chollet et al. 2011


General data and table manipulation tools.

Tool Description Reference
Table compute The power of the pandas data library for manipulating and computing expressions upon tabular data and matrices.
Datamash operations Datamash operations on tabular data
Datamash transpose Transpose rows/columns in a tabular file
Sample Generator Generate random samples with controlled size and complexity Pedregosa et al. 2011
Train/Test splitting Split Dataset into training and test subsets Pedregosa et al. 2011

Interactive Environments

You have done the heavy lifting and now want to use your coding skills inside Jupyter or RStudio? Work on data with the following:

Tool Description Reference
Jupyter Jupyter lab
RStudio RStudio