DOME recommendations with a Galaxy tutorial as use case
| DOME recommendations are a product of the ELIXIR Europe Machine Learning Focus Group |
More information about this project at the DOME website and the DOME paper
Galaxy is an open-source platform for FAIR data analysis. In this DOME tutorial we are using a Galaxy tutorial on Convolutional Neural Networks as a use case.
As stated in the DOME website: DOME-ML (or simply DOME) is an acronym standing for Data, Optimization, Model and Evaluation in Machine Learning. DOME is a set of community-wide guidelines, recommendations and checklists spanning these four areas aiming to help establish standards of supervised machine learning validation in biology. The recommendations are formulated as questions to anyone wishing to pursue implementation of a machine learning algorithm. Answers to these questions can be easily included in the supplementary material of published papers.
Learning and using the DOME recommendations will increase awareness among researchers on best practices to share Machine Learning approaches so they include minimum metadata for other researchers to get a quick and clear picture of the their Machine Learning approach is about and how it compares to others.
Evarybody can use this material. Please give proper attribution to authors and contributors.
License: This material is distributed under CC BY-NC
If you reuse these materials or want to reference them, please use the following citation (also provided via a CITATION file):
Castro LJ., Psomopoulos F., Tosatto S., Harrow J. (2022). DOME recommendations with a Galaxy tutorial as use case.
ELIXIR Machine Learning Focus Group. DOME recommendations authors and contributors. NFDI4Data Science (future adopter of the DOME recommendations).