Predicting genes associated with RNA methylation pathways using machine learning

Georgia Tsagkogeorga; Helena Santos-Rosa; Andrej Alendar; Dan Leggate; Oliver Rausch; Tony Kouzarides; Hendrik Weisser; Namshik Han

doi:10.1038/s42003-022-03821-y

Communications Biology (Aug 2022)

Predicting genes associated with RNA methylation pathways using machine learning

Georgia Tsagkogeorga,
Helena Santos-Rosa,
Andrej Alendar,
Dan Leggate,
Oliver Rausch,
Tony Kouzarides,
Hendrik Weisser,
Namshik Han

Affiliations

Georgia Tsagkogeorga: STORM Therapeutics Ltd, Babraham Research Campus
Helena Santos-Rosa: The Gurdon Institute, University of Cambridge
Andrej Alendar: The Gurdon Institute, University of Cambridge
Dan Leggate: STORM Therapeutics Ltd, Babraham Research Campus
Oliver Rausch: STORM Therapeutics Ltd, Babraham Research Campus
Tony Kouzarides: Milner Therapeutics Institute, University of Cambridge
Hendrik Weisser: STORM Therapeutics Ltd, Babraham Research Campus
Namshik Han: Milner Therapeutics Institute, University of Cambridge

DOI: https://doi.org/10.1038/s42003-022-03821-y
Journal volume & issue: Vol. 5, no. 1
pp. 1 – 10

Abstract

Read online

Machine learning of multi-modal data, including transcriptomic, proteomic, structural and physical interaction, predicts genes and molecular pathways involved in RNA methylation in humans.

Published in Communications Biology

ISSN: 2399-3642 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science: Biology (General)
Website: https://www.nature.com/commsbio/

About the journal