WEBINAR: DOME - Machine learning best practices & recommendations скачать в хорошем качестве

WEBINAR: DOME - Machine learning best practices & recommendations

As the adoption of Artificial Intelligence (AI) and Machine Learning (ML) accelerates across life science research, the demand for standardised practices has become crucial to ensure transparency, reproducibility, and adherence to FAIR principles. In response to these needs, DOME (Data Optimization Model Evaluation) has been developed as a key solution - a set of community-wide recommendations designed to guide supervised ML analysis reporting in biological studies. DOME offers broad, field-agnostic guidelines to enhance the impact of ML applications while ensuring reproducibility. This framework not only supports robust model evaluation but also serves as a valuable resource for training and capacity building in life sciences. Don’t miss this opportunity to learn how to elevate the standard of ML evaluation in your research and join us in setting a new benchmark for best practices in this critical area! Speaker: Dr Fotis Psomopoulos, Senior Researcher, Institute of Applied Biosciences (INAB), Center for Research and Technology Hellas (CERTH) Who the webinar is for: This webinar is for researchers, publishers, funders and policy makers who are committed to advancing best practices in machine learning. Reference: Walsh, I., Fishman, D., Garcia-Gasulla, D. et al. DOME: recommendations for supervised machine learning validation in biology. Nat Methods 18, 1122–1127 (2021) Captions are automatically generated by Otter.ai and edited for accuracy by the BioCommons team.