Entropy-driven machine learning for deciphering mRNA rearrangement by splicing in complex microorganisms
This work identifies key sequence-level features predictive of intron retention in Tetrahymena and could provide a generalizable, explainable ML framework for investigating splicing regulation in other eukaryotes.
A. Mancini, E. Merelli, Marco Piangerelli et al.
· Frontiers in Bioinformatics · 0 citations