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Predicting genes associated with RNA methylation pathways using machine learning.

Published version
Peer-reviewed

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Authors

Tsagkogeorga, Georgia  ORCID logo  https://orcid.org/0000-0002-9657-453X
Santos-Rosa, Helena 
Alendar, Andrej 
Leggate, Dan 
Rausch, Oliver 

Abstract

RNA methylation plays an important role in functional regulation of RNAs, and has thus attracted an increasing interest in biology and drug discovery. Here, we collected and collated transcriptomic, proteomic, structural and physical interaction data from the Harmonizome database, and applied supervised machine learning to predict novel genes associated with RNA methylation pathways in human. We selected five types of classifiers, which we trained and evaluated using cross-validation on multiple training sets. The best models reached 88% accuracy based on cross-validation, and an average 91% accuracy on the test set. Using protein-protein interaction data, we propose six molecular sub-networks linking model predictions to previously known RNA methylation genes, with roles in mRNA methylation, tRNA processing, rRNA processing, but also protein and chromatin modifications. Our study exemplifies how access to large omics datasets joined by machine learning methods can be used to predict gene function.

Description

Keywords

Article, /631/114, /631/337/176, /45, article

Journal Title

Commun Biol

Conference Name

Journal ISSN

2399-3642
2399-3642

Volume Title

Publisher

Springer Science and Business Media LLC