Exploring functional conservation in silico: a new machine learning approach to RNA-editing
Michał Zawisza-Álvarez, Jesús Peñuela-Melero, Esteban Vegas, Ferrán Reverter, Jordi García‐Fernàndez, Carlos Herrera-Úbeda
The paper introduces a machine learning approach to assess RNA-editing functional conservation using random forest and biLSTM networks, predicting editing events and analyzing conservation across species.
Briefings in Bioinformatics · May 23, 2024Peer-reviewedGenetics