  As a prevalent and dynamically regulated epigenetic modification,
5-formylcytidine (f5C) is crucial in various biological processes. However,
traditional experimental methods for f5C detection are often laborious and
time-consuming, limiting their ability to map f5C sites across the
transcriptome comprehensively. While computational approaches offer a
cost-effective and high-throughput alternative, no recognition model for f5C
has been developed to date. Drawing inspiration from language models in natural
language processing, this study presents f5C-finder, an ensemble neural
network-based model utilizing multi-head attention for the identification of
f5C. Five distinct feature extraction methods were employed to construct five
individual artificial neural networks, and these networks were subsequently
integrated through ensemble learning to create f5C-finder. 10-fold
cross-validation and independent tests demonstrate that f5C-finder achieves
state-of-the-art (SOTA) performance with AUC of 0.807 and 0.827, respectively.
The result highlights the effectiveness of biological language model in
capturing both the order (sequential) and functional meaning (semantics) within
genomes. Furthermore, the built-in interpretability allows us to understand
what the model is learning, creating a bridge between identifying key
sequential elements and a deeper exploration of their biological functions.
