The impact of the ELIXIR community in machine learning
Year: 2024
Type: other
Abstract: With the continuous generation, processing, and transformation of biological data, the application of Machine Learning (ML) has become one of the most effective approaches for extracting insights from this data and supporting decision-making processes. The ELIXIR Machine Learning Focus Group launched in October 2019, specifically to capture the emerging needs in ML expertise across the community, and has been consistently producing high-impact outputs over the past 5 years. Its overarching goals include all aspects of ML, from standards and reproducibility, to benchmarking and training. One key output of the FG is the publication of the DOME recommendations (Nat Methods, July 2021), a set of community-wide recommendations for reporting supervised ML-based analyses of biological studies. Broad adoption of these recommendations help improve ML assessment and reproducibility. They have been effectively used in a more ambitious Strategic Implementation Study, leading to a coherent registry around DOME and a community-led publication annotation effort. Another key activity of the FG aims to tackle challenges of synthetic data, creating frameworks and best practices for the generation, evaluation, and application of synthetic data. Achievements include a synthetic data catalogue, a FAIR metadata model, a data registry, an evaluation metrics review, and a community survey. Finally, as AI-ready data are a necessity in ML, a comprehensive effort around gold standard datasets is in place, collecting both paper candidates with datasets on selected domains (e.g. metabolomics/omics), as well as collected datasets that can be applied in ML in Life Sciences, specifically for supervised learning, around omics.
Authors: Fotis Psomopoulos, Emidio Capriotti, Núria Queralt Rosinach, Davide Cirillo, Leyla Jael Castro, Silvio Tosatto, The ELIXIR ML Focus Group members
Language: English
Subfield: Information Systems and Management
Field: Decision Sciences
Domain: Social Sciences
Open Access status: gold
OpenAlex ID: W4416504688
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