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Machine Learning & Omics data at ECCB2020

Machine Learning & Omics data: Opportunities for advancing biomedical data analysis in Galaxy

31th August 2020, 13:30-16:30 CEST

Description: Machine learning (ML) has emerged as a discipline that enables computers to assist humans in making sense of large and complex data sets. With the drop-in cost of sequencing technologies, large amounts of omics data are being generated and made accessible to researchers. Analyzing these complex large data is not trivial and the use of classical tools cannot explore their full potential. Machine learning algorithms can thus be very useful in mining large omics datasets to uncover new insights that can advance the field of medicine and improve health care. There is an increasing interest in the potential of ML to create predictive models and to identify complex patterns from omics datasets. The aim of this tutorial is to introduce participants to the Machine learning and taxonomy of machine learning algorithms and common machine learning algorithms, through the Galaxy-ML environment. Galaxy-ML extends Galaxy, a user-friendly, web-based computational workbench used by tens of thousands of scientists across the world, with a machine learning tool suite that supports end-to-end analysis. Galaxy-ML uses the Galaxy framework to make machine learning tools and pipelines widely accessible. As Galaxy records all parameters and tools used, all analyses including those for machine learning are completely reproducible. The tutorial will cover the methods being used to analyze different omics data sets by providing a practical context through the use of basic but widely used modules in Galaxy. It will comprise a number of hands on exercises and challenges, where the participants will acquire a first understanding of the theory behind ML algorithms as well as the practical skills in applying them on familiar problems and publicly available real-world data sets.

Learning objectives: By the end of the workshop attendees will be able to: Introducing the basic concepts in ML and understanding the taxonomy of ML algorithms Understanding differences between supervised and unsupervised ML algorithms categories and which kind of problem they can be applied to Introducing the typically used machine learning algorithms for analyzing “omics” data Understanding different applications of ML in different -omics studies Learning how to tune the parameters of ML algorithms Learning how to use Galaxy’s machine learning tools

Target audience: This tutorial is aimed towards scientists active in Life Sciences (graduate students and researchers) familiar with different omics data analysis, that are interested in applying machine learning to analyze them using Galaxy.



  • Anup Kumar, Ph.D. student at the University of Freiburg and member of the European Galaxy team.
  • Alireza Khanteymoori, Postdoc Researcher at the University of Freiburg and member of the European Galaxy team.
  • Björn Grüning, Postdoc Researcher at the University of Freiburg and Head of the European Galaxy team.
  • Fotis Psomopoulos, Principal Investigator at the Institute of Applied Biosciences (INAB) Centre for Research and Technology Hellas (CERTH).


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