Machine Learning (ML) enables computers to assist humans in making sense of large and complex data sets. With the fall in the cost of high-throughput technologies, large amounts of omics data are being generated and made accessible to researchers. Analyzing these complex high-volume data is not trivial, and the use of classical statistics can not 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 medicine and improve health care. The ELIXIR Machine Learning Focus Group was initiated in October 2019, in order to capture the emerging need in Machine Learning expertise across the network.
Goals of the group
Standards for Machine Learning
This includes controlled terminology/ontology and services for ML model description and sharing, alignment to the ELIXIR Tools and Interoperability Platforms, as well as defining best practices for Machine Learning-related reviewing.
Machine Learning and reproducibility
This area focuses on the definition of the best practices for developing, sharing and reusing Machine Learning approaches (including, but not limited to, Machine Learning models, algorithms, frameworks and protocols). It involves using the existing approaches in the ELIXIR Tools Platform.
Benchmarking of Machine Learning tools
To facilitate clear and objective comparison of ML-based tools, it is important to establish a benchmarking protocol. This may include datasets, protocols and services offered by the ELIXIR Tools Platform.
Training for Machine Learning
Machine Learning has been identified by the ELIXIR Training Platform gap analysis task as an existing need (missing and/or needs to be scaled up, 68% responses, n=181). As a result, this group will design and produce training resources for supporting the ELIXIR community, based on the standards and approaches established by the ELIXIR Training Platform.
Register for the Machine Learning training: Machine Learning using Galaxy Webinar / workshop series
Integration across ELIXIR Communities
Machine Learning is a key competency that is relevant to a large number of activities, as well as being aligned to funding opportunities. As such, a persistent activity of this group is to align and coordinate these efforts across all relevant ELIXIR groups, such as the Federated Human Data Community and the Data Platform.