DNA N
6-methyladenosine (6
mA) modification
carries significant epigenetic information and plays a pivotal role
in biological functions, thereby profoundly impacting human development.
Precise and reliable detection of 6 mA sites is integral to understanding
the mechanisms underpinning DNA modification. The present methods,
primarily experimental, used to identify specific molecular sites
are often time-intensive and costly. Consequently, the rise of computer-based
methods aimed at identifying 6 mA sites provides a welcome alternative.
Our research introduces a novel model to discern DNA 6 mA sites in
cross-species genomes. This model, developed through machine learning,
utilizes extracted sequence information. Hyperparameter tuning was
employed to ascertain the most effective feature combination and model
implementation, thereby garnering vital information from sequences.
Our model demonstrated superior accuracy compared to the existing
models when tested using five-fold cross-validation. Thus, our study
substantiates the reliability and efficiency of our model as a valuable
tool for supplementing experimental research.