When: 08-09 September 2021, 09:00 – 16:30 BST
Instructors and helpers
- Krzysztof Poterlowicz, University of Bradford
- Fotis Psomopoulos, INAB-CERTH, ELIXIR-GR
- Gabriele Schweikert, University of Dundee
- to be updated
With the rise in high-throughput sequencing technologies, the volume of omics data has grown exponentially in recent times and a major issue is to mine useful knowledge from these data which are also heterogeneous in nature. Machine learning (ML) is a discipline in which computers perform automated learning without being programmed explicitly and assist humans to make sense of large and complex data sets. The analysis of complex high-volume data is not trivial and classical tools cannot be used to explore their full potential. Machine learning can thus be very useful in mining large omics datasets to uncover new insights that can advance the field of bioinformatics.
This 2-day course will introduce participants to the machine learning taxonomy and the applications of common machine learning algorithms to omics data. The course will cover the common methods being used to analyse different omics data sets by providing a practical context through the use of basic but widely used R libraries. The course will comprise a number of hands-on exercises and challenges where the participants will acquire a first understanding of the standard ML processes, as well as the practical skills in applying them on familiar problems and publicly available real-world data sets.
At the end of the course, the participants will be able to:
- Understand the ML taxonomy and the commonly used machine learning algorithms for analysing “omics” data
- Understand differences between ML algorithms categories and to which kind of problem they can be applied
- Understand different applications of ML in different -omics studies
- Use some basic, widely used R packages for ML
- Interpret and visualize the results obtained from ML analyses on omics datasets
- Apply the ML techniques to analyse their own datasets
Audience and requirements
This course is intended for master and PhD students, post-docs and staff scientists familiar with different omics data technologies who are interested in applying machine learning to analyse these data. No prior knowledge of Machine Learning concepts and methods is expected nor required.
Familiarity with any programming language will be required (familiarity with R will be preferable).
This course will be streamed, you are thus required to have your own computer with an internet connection. In order to ensure clear communication between Instructors and participants, we will be using collaborative tools, such as Google Drive and/or Google Docs.
Maximum participants: 30