m1A-Ensem: accurate identification of 1-methyladenosine sites through ensemble models

Muhammad Taseer Suleman; Fahad Alturise; Tamim Alkhalifah; Yaser Daanial Khan

doi:10.1186/s13040-023-00353-x

m1A-Ensem: accurate identification of 1-methyladenosine sites through ensemble models

BioData Min. 2024 Feb 15;17(1):4. doi: 10.1186/s13040-023-00353-x.

Authors

Muhammad Taseer Suleman¹, Fahad Alturise², Tamim Alkhalifah³, Yaser Daanial Khan¹

Affiliations

¹ Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore, 54770, Pakistan.
² Department of Computer, College of Science and Arts in Ar Rass, Qassim University, Ar Rass, Qassim, Saudi Arabia. falturise@qu.edu.sa.
³ Department of Computer, College of Science and Arts in Ar Rass, Qassim University, Ar Rass, Qassim, Saudi Arabia.

Abstract

Background: 1-methyladenosine (m1A) is a variant of methyladenosine that holds a methyl substituent in the 1st position having a prominent role in RNA stability and human metabolites.

Objective: Traditional approaches, such as mass spectrometry and site-directed mutagenesis, proved to be time-consuming and complicated.

Methodology: The present research focused on the identification of m1A sites within RNA sequences using novel feature development mechanisms. The obtained features were used to train the ensemble models, including blending, boosting, and bagging. Independent testing and k-fold cross validation were then performed on the trained ensemble models.

Results: The proposed model outperformed the preexisting predictors and revealed optimized scores based on major accuracy metrics.

Conclusion: For research purpose, a user-friendly webserver of the proposed model can be accessed through https://taseersuleman-m1a-ensem1.streamlit.app/ .

Keywords: Artificial Intelligence; Computational Biology; Computational Model; Decision Trees; Genetics; Nucleotide Sequence; RNA; Respiratory Disease; Sequence Analysis; Statistical Model.