Hybrid representation learning for human m⁶A modifications with chromosome-level generalizability.

ABSTRACT:

Motivation

N6-methyladenosine ( m6A ) is the most abundant internal modification in eukaryotic mRNA and plays essential roles in post-transcriptional gene regulation. While several deep learning approaches have been proposed to predict m6A sites, most suffer from limited chromosome-level generalizability due to evaluation on randomly split datasets.

Results

In this study, we propose two novel hybrid deep learning models-Hybrid Model and Hybrid Deep Model-that integrate local sequence features (k-mers) and contextual embeddings via convolutional neural networks to improve predictive performance and generalization. We evaluate these models using both a Random-Split strategy and a more biologically realistic Leave-One-Chromosome-Out setting to ensure robustness across ge

SUBMITTER: Tahir M 

PROVIDER: S-EPMC12288952 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature