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MethEvo: an accurate evolutionary information-based methylation site predictor

  • S.I.: Improving Healthcare outcomes using Multimedia Big Data Analytics
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Abstract

Post Translational Modification (PTM) plays an essential role in the biological and molecular mechanisms. They are also considered as a vital element in cell signaling and networking pathways. Among different PTMs, Methylation is regarded as one of the most important types. Methylation plays a crucial role in maintaining the dynamic balance, stability, and remodeling of chromatins. Methylation also leads to different abnormalities in cells and is responsible for many serious diseases. Methylation can be detected by experimental approaches such as methylation-specific antibodies, mass spectrometry, or characterizing methylation sites using the radioactive labeling method. However, these approaches are time-consuming and costly. Therefore, there is a demand for fast and accurate computational techniques to solve these issues. This study proposes a novel machine learning approach called MethEvo to predict methylation sites in proteins. To build this model, we use an evolutionary-based bi-gram profile approach to extract features. We also use SVM as our classification technique to build MethEvo. Our results demonstrate that MethEvo achieves 98.7%, 98.8%, 98.4%, and 0.974 in terms of accuracy, specificity, sensitivity, and Matthews Correlation Coefficient (MCC). MethEvo and its source code are publicly available at: https://github.com/islamsadia88/MethEvo.

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Data availability

MethEvo and its source code are publicly available at: https://github.com/islamsadia88/MethEvo.

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Funding

This research received no external funding Rutgers, The State University of New Jersey, p321243, Abdollah Dehzangi.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, United International University, Dhaka, Bangladesh

    Sadia Islam, Shafayat Bin Shabbir Mugdha & Swakkhar Shatabda

  2. Department of Computer Science, University of Maryland Baltimore County, Baltimore, MD, 21250, USA

    Shubhashis Roy Dipta

  3. Institute of Information Technology, Jahangirnagar University, Savar, Dhaka, Bangladesh

    MD. Easin Arafat

  4. BioMedical Machine Learning Lab (BML), The Graduate School of Biomedical Engineering, UNSW SYDNEY, Sydney, NSW, 2052, Australia

    Hamid Alinejad-Rokny

  5. UNSW Data Science Hub, The University of New South Wales (UNSW Sydney), Sydney, NSW, 2052, Australia

    Hamid Alinejad-Rokny

  6. AI-Enabled Processes (AIP) Research Centre, Macquarie University, Sydney, 2109, Australia

    Hamid Alinejad-Rokny

  7. Department of Computer Science, Rutgers University, Camden, NJ, 08102, USA

    Iman Dehzangi

  8. Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, 08102, USA

    Iman Dehzangi

Authors
  1. Sadia Islam
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Contributions

Conceptualization, I.D., H.A.R and S.S.; methodology, S.I, S.B.S.M, S.R.D, and MD.E.A software, S.I, R.R.D; validation, S.B.S.M, S.I. and S.S.; formal analysis, S.I.; investigation, I.D; resources, S.S., and I.D; data curation, I.D.; writing—original draft preparation, S.I., I.D, AND S.S; writing—review and editing, I.D, AND H.A.R; supervision, S.S., H.A.R, AND I.D.

Corresponding authors

Correspondence to Swakkhar Shatabda, Hamid Alinejad-Rokny or Iman Dehzangi.

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Islam, S., Mugdha, S.B.S., Dipta, S.R. et al. MethEvo: an accurate evolutionary information-based methylation site predictor. Neural Comput & Applic 36, 201–212 (2024). https://doi.org/10.1007/s00521-022-07738-9

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  • DOI: https://doi.org/10.1007/s00521-022-07738-9

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