Published in GigaBlog

Watch a DOME Webinar on Machine Learning Best Practices & Recommendations on 24th September 2024 In recent years, there has been a substantial increase in scientific publications in journals publishing computational research such as ours utilising Machine Learning (ML). This represents a significant challenge for disseminating and assessing scientific research as the black box and […]

The post Machine Learning Standards in the Wild. DOME Webinar on ML Recommendations and Applications appeared first on GigaBlog.

References

DOME: recommendations for supervised machine learning validation in biology

Published in Nature Methods
Authors Ian Walsh, Dmytro Fishman, Dario Garcia-Gasulla, Tiina Titma, Gianluca Pollastri, ELIXIR Machine Learning Focus Group, Emidio Capriotti, Rita Casadio, Salvador Capella-Gutierrez, Davide Cirillo, Alessio Del Conte, Alexandros C. Dimopoulos, Victoria Dominguez Del Angel, Joaquin Dopazo, Piero Fariselli, José Maria Fernández, Florian Huber, Anna Kreshuk, Tom Lenaerts, Pier Luigi Martelli, Arcadi Navarro, Pilib Ó Broin, Janet Piñero, Damiano Piovesan, Martin Reczko, Francesco Ronzano, Venkata Satagopam, Castrense Savojardo, Vojtech Spiwok, Marco Antonio Tangaro, Giacomo Tartari, David Salgado, Alfonso Valencia, Federico Zambelli, Jennifer Harrow, Fotis E. Psomopoulos, Silvio C. E. Tosatto
Machine learningPublishingData Standards

Trust and Transparency in Reporting Machine Learning: The DOME-GigaScience Press Trial

Published

Machine learning is increasingly applied to biological and biomedical data, and there is a need for sufficient detail to enable a researcher to understand the machine learning approach used in a research study. This is even more challenging due to Machine Learning studies being inherently difficult to interpret (the so-called “black box” effect).  To throw light on these methods, GigaScience Press (https://www.gigasciencepress.org/) has partnered with the DOME Consortium with the goal of encouraging authors to follow the DOME (Data, Optimisation, Model, Evaluation) recommendations.The role of the GigaScience DataBase (GigaDB) Data Curation team is to ensure the Data Submission process runs as smoothly as possible. The DOME Consortium has generated the DOME Data Stewardship Wizard which enables researchers to submit their DOME annotations to a central repository and share them with reviewers. The GigaDB team scans submitted manuscripts for Machine Learning content, and performs checks to ensure that DOME annotations in support of GigaScience and GigaByte manuscripts are sufficiently complete.To increase the visibility of the supporting DOME annotation, a link to DOME annotation is included in the GigaDB dataset that accompanies a GigaScience or GigaByte manuscript. The DOME annotations are a great asset to peer review, providing the necessary high-level overview to properly understand a machine learning study. We recommend that other journals follow our example in encouraging DOME annotations to be submitted early in the publication process and prior to peer-review.