Vol. 5 No. 1 (2025): Issue 5
Articles

DNA methylation patterns Analysis through K-Nearest Neighbors-based Method

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  • Liam Hawthorne,
  • Sarah Ngata,
  • Jorrit van Veen
Liam Hawthorne
Molecular Genetics Research Institute, Waikato University, Hamilton, 3240, New Zealand
Sarah Ngata
Genome and Biomarker Lab, Southern Institute of Technology, Invercargill, 9810, New Zealand
Jorrit van Veen
Epigenetics and Computational Biology Center, Massey University, Palmerston North, 4442, New Zealand
DOI: https://doi.org/10.71070/jcbm.v5i1.87

Published 2025-02-25

Keywords

  • DNA Methylation,
  • Gene Regulation,
  • Personalized Medicine,
  • Machine Learning,
  • Epigenetic Modifications

How to Cite

Hawthorne, L., Ngata, S., & van Veen, J. (2025). DNA methylation patterns Analysis through K-Nearest Neighbors-based Method. Journal of Computational Biology and Medicine, 5(1). https://doi.org/10.71070/jcbm.v5i1.87

Abstract

DNA methylation patterns play a crucial role in gene regulation and disease development. Understanding these patterns is essential for advancing personalized medicine and disease diagnosis. Current research in DNA methylation analysis faces challenges such as high dimensionality and computational complexity. This paper proposes a novel approach utilizing a K-Nearest Neighbors-based method for analyzing DNA methylation patterns. The innovative method aims to improve accuracy and efficiency in identifying methylation patterns associated with specific biological processes or diseases. By integrating machine learning techniques with DNA methylation data, this research contributes to the development of more effective tools for studying epigenetic modifications and their implications in human health.

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