Deep-m6Am: a deep learning model for identifying N6, 2′-O-Dimethyladenosine (m6Am) sites using hybrid features

Islam Uddin; Salman A. AlQahtani; Sumaiya Noor; Salman Khan; Islam Uddin; Salman A. AlQahtani; Sumaiya Noor; Salman Khan

doi:10.3934/bioeng.2025006

AIMS Bioengineering

2025, Volume 12, Issue 1: 145-161. doi: 10.3934/bioeng.2025006

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Research article

Deep-m6Am: a deep learning model for identifying N6, 2′-O-Dimethyladenosine (m6Am) sites using hybrid features

1.
Department of Computer Science, Abdul Wali Khan University Mardan, KPK, Pakistan
2.
New Emerging Technologies and 5G Network and Beyond Research Chair, Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
3.
Business and Management Sciences Department, Purdue University, West Lafayette, IN, USA

Received: 06 January 2025 Revised: 06 February 2025 Accepted: 12 February 2025 Published: 04 March 2025

N6,2′-O-dimethyladenosine (m6Am) is a crucial RNA modification that plays a pivotal role in regulating gene expression and maintaining RNA stability. Given its dynamic involvement in various biological processes and diseases, accurately identifying m6Am is essential for understanding cellular mechanisms and pathogenesis. Furthermore, detecting m6Am modifications is key to deciphering regulatory pathways and elucidating disease mechanisms. In this study, we propose Deep-m6Am, a deep learning–based model for precisely identifying m6Am sites in RNA sequences. The proposed framework employs a comprehensive feature extraction process, i.e., integrating pseudo single nucleotide composition (PseSNC), pseudo dinucleotide composition (PseDNC), and pseudo trinucleotide composition (PseTNC) to capture complex sequence patterns. To enhance computational efficiency and eliminate noisy or redundant features, a supervised SHAP (SHapley Additive exPlanations) algorithm is utilized, ensuring the selection of the most informative features. Finally, a multilayer deep neural network (DNN) is used as a classification algorithm for identifying m6Am sites. The performance of the proposed model was evaluated in comparison with traditional machine learning (ML) algorithms and existing models. Experimental results demonstrate that Deep-m6Am outperforms previous approaches by 6.67% and traditional ML algorithms by 7.39%.
These findings highlight Deep-m6Am as a promising tool for advancing drug discovery and improving the diagnosis of diseases associated with m6Am modifications.
- machine learning,
- deep neural network,
- hybrid sequential model,
- deep-m6Am model,
- N6, 2′O-dimethyl adenosine,
- RNA modification
Citation: Islam Uddin, Salman A. AlQahtani, Sumaiya Noor, Salman Khan. Deep-m6Am: a deep learning model for identifying N6, 2′-O-Dimethyladenosine (m6Am) sites using hybrid features[J]. AIMS Bioengineering, 2025, 12(1): 145-161. doi: 10.3934/bioeng.2025006

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Abstract

N6,2′-O-dimethyladenosine (m6Am) is a crucial RNA modification that plays a pivotal role in regulating gene expression and maintaining RNA stability. Given its dynamic involvement in various biological processes and diseases, accurately identifying m6Am is essential for understanding cellular mechanisms and pathogenesis. Furthermore, detecting m6Am modifications is key to deciphering regulatory pathways and elucidating disease mechanisms. In this study, we propose Deep-m6Am, a deep learning–based model for precisely identifying m6Am sites in RNA sequences. The proposed framework employs a comprehensive feature extraction process, i.e., integrating pseudo single nucleotide composition (PseSNC), pseudo dinucleotide composition (PseDNC), and pseudo trinucleotide composition (PseTNC) to capture complex sequence patterns. To enhance computational efficiency and eliminate noisy or redundant features, a supervised SHAP (SHapley Additive exPlanations) algorithm is utilized, ensuring the selection of the most informative features. Finally, a multilayer deep neural network (DNN) is used as a classification algorithm for identifying m6Am sites. The performance of the proposed model was evaluated in comparison with traditional machine learning (ML) algorithms and existing models. Experimental results demonstrate that Deep-m6Am outperforms previous approaches by 6.67% and traditional ML algorithms by 7.39%.

These findings highlight Deep-m6Am as a promising tool for advancing drug discovery and improving the diagnosis of diseases associated with m6Am modifications.

Acknowledgments

This work was supported by Research Supporting Project Number (RSPD2025R585), King Saud University, Riyadh, Saudi Arabia.

Data Availability

The datasets used and analyzed during the current study are available on the GitHub link: https://github.com/islamuddinw1/Deep-m6Am1.git.

Conflict of interest

The authors declare no conflict of interest.

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