Taxonomic Classification of Bacteria Using Machine Learning ‎Models on DNA Sequences

Sudhir Anakal; Sarange Shreepad Marotrao; Vedavathi G R; Gargi Mishra; Anurag Vijay Agrawal; Archana Bhaskar; Surya D; Anu Swedha Ananthan

doi:10.14419/cse17v46

Article Details

Taxonomic Classification of Bacteria Using Machine Learning ‎Models on DNA Sequences

Authors:

Sudhir Anakal | Sarange Shreepad Marotrao | Vedavathi G R | Gargi Mishra | Anurag Vijay Agrawal | Archana Bhaskar | Surya D | Anu Swedha Ananthan

Publisher:

Science Publishing Corporation

Journal:

International Journal of Basic and Applied Sciences Vol.14 Issue 3, pp.298-310

Document Type:

Article

Publication Date:

7/2025

Document Language:

English

Document ID (Org No.):

2001387264

pISSN:

2227-5053

DOI:

10.14419/cse17v46

Keywords:

Abstract:

 This study investigates different deep learning architectures, especially 1D and 2D convolutional neural networks (CNNs), for DNA se-‎sequence classification using k-mer vector representations. The results show that k-mer vectors, especially those with k = 5, can effectively ‎capture relevant features in DNA sequences and achieve high accuracy, precision, and recall across all taxonomic levels. Among the tested ‎models, 1D CNN outperformed 2D CNN in terms of accuracy and training efficiency. However, the 2D CNN achieved a slightly higher ‎accuracy without the nested layers, suggesting that critical information should be discarded due to the lack of input representation. As ‎expected, the model performance declined at lower taxonomic levels due to sequence feature limitations and class imbalance, but still achieved ‎‎88% accuracy at the genus level. Notably, simple multi-layer neural networks outperformed CNN, indicating the potential of low-‎low-complexity models for genomic data classification. These findings suggest that while CNNs are efficient, simpler architectures can provide ‎competitive performance in terms of information representation and task complexity‎.

Article Details

Taxonomic Classification of Bacteria Using Machine Learning ‎Models on DNA Sequences

Authors:

Sudhir Anakal | Sarange Shreepad Marotrao | Vedavathi G R | Gargi Mishra | Anurag Vijay Agrawal | Archana Bhaskar | Surya D | Anu Swedha Ananthan

Publisher:

Science Publishing Corporation

Journal:

International Journal of Basic and Applied Sciences Vol.14 Issue 3, pp.298-310

Document Type:

Article

Publication Date:

7/2025

Document Language:

English

Document ID (Org No.):

2001387264

pISSN:

2227-5053

DOI:

10.14419/cse17v46

Machine Learning

DNA Sequences

K-Mer Vector

Deep Learning

Issues

Article Details

Taxonomic Classification of Bacteria Using Machine Learning ‎Models on DNA Sequences

Authors: Sudhir Anakal | Sarange Shreepad Marotrao | Vedavathi G R | Gargi Mishra | Anurag Vijay Agrawal | Archana Bhaskar | Surya D | Anu Swedha Ananthan

Publisher: Science Publishing Corporation

Journal: International Journal of Basic and Applied Sciences Vol.14 Issue 3, pp.298-310

Document Type: Article

Publication Date: 7/2025

Document Language: English

Document ID (Org No.): 2001387264

pISSN: 2227-5053

DOI: 10.14419/cse17v46

Machine Learning

DNA Sequences

K-Mer Vector

Deep Learning

Issues

Authors:

Sudhir Anakal | Sarange Shreepad Marotrao | Vedavathi G R | Gargi Mishra | Anurag Vijay Agrawal | Archana Bhaskar | Surya D | Anu Swedha Ananthan

Publisher:

Science Publishing Corporation

Journal:

International Journal of Basic and Applied Sciences Vol.14 Issue 3, pp.298-310

Document Type:

Article

Publication Date:

7/2025

Document Language:

English

Document ID (Org No.):

2001387264

pISSN:

2227-5053

DOI:

10.14419/cse17v46