Predicting DNA Sequence Similarity Across Species Using Machine Learning: a K-mer Based Approach

Authors

  • Vinodkumar R. Patil Department Computer Engineering, GES’s R. H. Sapat College of Engineering, Management Studies and Research, Nashik, INDIA. https://orcid.org/0000-0001-8803-9724
  • Archana S. Vaidya Department Computer Engineering, GES’s R. H. Sapat College of Engineering, Management Studies and Research, Nashik, INDIA.
  • Manisha S. Patil Department of Computer Science and Engineering (Data Science), R. C. Patel Institute of Technology, Shirpur, INDIA.

DOI:

https://doi.org/10.22452/

Keywords:

DNA classification, machine learning, K-mer, naive bayes

Abstract

This study examines the effectiveness of machine learning methods for categorizing DNA sequences across human, chimpanzee, and dog samples. We employed k-mer encoding to renovate DNA structures into numerical representations suitable for machine learning models. Four classifiers, such as naive bayes and weighted naive bayes, random forest, K-nearest neighbors, and decision tree, were applied and evaluated using accuracy, precision, recall, and F1-score. The naive bayes classifier consistently outperformed the others across all three datasets, achieving the highest accuracy in classifying human DNA (98.4%), followed by chimpanzee DNA (91.4%), and exhibiting significantly lower accuracy for dog DNA (68.9%). This performance disparity is attributed to the increasing evolutionary distance from human DNA. Additionally, a weighted naive bayes model that was trained on human data showed very high accuracy in predicting chimpanzee (99.3%) and dog (92.6%) DNA sequences. The results presented here show the significance of taking into account evolutionary relations and dataset features whenever developing and training classification models for genetic sequence analysis. The research extends the present research by evaluating the performance of several different algorithms on separate DNA databases, identifying strengths and weaknesses, and suggesting avenues for future research focusing on advanced feature engineering and algorithm selection for improved cross-species classification.

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Published

30-06-2026

Issue

Vol 45 No 2 (2026): June 2026

Section

Original Articles

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