Research Article
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fCGR yöntemi ile DNA dizi benzerliği için transfer öğrenme modellerinin kullanılması

Year 2025, Volume: 14 Issue: 2, 1 - 1

Emre Delibaş

Abstract

DNA dizilerinin benzerlik analizi, evrimsel ilişkilerin anlaşılması ve genetik mutasyonların belirlenmesi açısından kritik bir konudur. Geleneksel hizalama tabanlı yöntemler yüksek hesaplama maliyetine sahip olduğundan, bu çalışmada hizalamadan bağımsız DNA benzerlik analizi için transfer öğrenme modellerinin uygulanabilirliği incelenmiştir. DNA dizileri, Frequency Chaos Game Representation (fCGR) yöntemiyle görselleştirilmiş ve ResNet50, EfficientNetB0, MobileNet modelleriyle özellik çıkarımı yapılmıştır. Cosine similarity, Euclidean distance ve correlation gibi üç benzerlik metriği ve dört farklı hiyerarşik kümeleme yöntemi karşılaştırılmıştır. Sonuçlar, cosine similarity metriğinin genetik benzerlikleri daha iyi yansıttığını göstermektedir. MobileNet, hafif yapısı ve verimli özellik çıkarımıyla en yüksek doğruluk oranını sunmuştur. PCA ile görselleştirilen özellik vektörleri güçlü kümelenme eğilimleri sergilemiş ve referans filogenetik ağaçlarla uyum göstermiştir. Çalışma, transfer öğrenmenin genetik analizlerde uygulanabilirliğini ortaya koyarak ölçeklenebilir ve biyolojik olarak anlamlı analizler yapılabileceğini göstermektedir.

Keywords

DNA dizi benzerliği Transfer öğrenme Otomatik özellik çıkarımı fCGR

References

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Using transfer learning models for DNA sequence similarity via fCGR method

Year 2025, Volume: 14 Issue: 2, 1 - 1

Emre Delibaş

Abstract

Similarity analysis of DNA sequences is a critical issue for understanding evolutionary relationships and identifying genetic mutations. Since traditional alignment-based methods have high computational costs, this study investigated the applicability of transfer learning models for alignment-independent DNA similarity analysis. DNA sequences were visualized with the Frequency Chaos Game Representation (fCGR) method and feature extraction was performed with ResNet50, EfficientNetB0, and MobileNet models. Three similarity metrics such as cosine similarity, Euclidean distance, and correlation and four different hierarchical clustering methods were compared. The results show that cosine similarity metric reflects genetic similarities better. MobileNet provided the highest accuracy rate with its lightweight structure and efficient feature extraction. Feature vectors visualized with PCA exhibited strong clustering tendencies and were in agreement with reference phylogenetic trees. The study demonstrates the applicability of transfer learning in genetic analyses and shows that scalable and biologically meaningful analyses can be performed.

Keywords

DNA sequence similarity Transfer learning Automatic feature extraction fCGR

References

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  • E. Delibaş and A. Arslan, DNA sequence similarity analysis using image texture analysis based on first-order statistics, J Mol Graph Model, 99, p. 107603, Sep. 2020. https://doi.org/10.1016/j.jmgm.2020.107603.
  • W. Chen, B. Liao, and W. Li, Use of image texture analysis to find DNA sequence similarities, J Theor Biol, 455, 1–6, Oct. 2018. https://doi.org/10.1016/J.JTBI.2018.07.001.
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  • E. Delibaş and A. Arslan, A new feature vector model for alignment-free DNA sequence similarity analysis, Sigma Journal of Engineering and Natural Sciences, 40, 3, 610–619, Oct. 2022. https://doi.org/10.14744/sigma.2022.00065.
  • J. P. Bao and R. Y. Yuan, A wavelet-based feature vector model for DNA clustering, Genet Mol Res, 14, 4, 19163–19172, Dec. 2015. https://doi.org/10.4238/2015.DECEMBER.29.26.
  • G. Mendizabal-Ruiz, I. Román-Godínez, S. Torres-Ramos, R. A. Salido-Ruiz, H. Vélez-Pérez, and J. A. Morales, Genomic signal processing for DNA sequence clustering, PeerJ, 2018, 1, p. e4264, Jan. 2018. https://doi.org/10.7717/PEERJ.4264/SUPP-2.
  • S. Dey, P. Ghosh, and S. Das, Positional difference and Frequency (PdF) based alignment-free technique for genome sequence comparison, J Biomol Struct Dyn, Oct. 2023. https://doi.org/10.1080/07391102.2023.2272748.
  • S. Akbari Rokn Abadi, A. Mohammadi, and S. Koohi, A new profiling approach for DNA sequences based on the nucleotides’ physicochemical features for accurate analysis of SARS-CoV-2 genomes, BMC Genomics, 24, 1, Dec. 2023. https://doi.org/10.1186/S12864-023-09373-7.
  • M. K. Ganapathiraju, A. D. Mitchell, M. Thahir, K. Motwani, and S. Ananthasubramanian, Suite of tools for statistical N-gram language modeling for pattern mining in whole genome sequences, J Bioinform Comput Biol, 10, 6, Dec. 2012. https://doi.org/10.1142/S0219720012500163.
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  • M. R. Kantorovitz, G. E. Robinson, and S. Sinha, A statistical method for alignment-free comparison of regulatory sequences, Bioinformatics, 23, 13, i249–i255, Jul. 2007. https://doi.org/10.1093/BIOINFORMATICS/BTM211.
  • K. Song, J. Ren, G. Reinert, M. Deng, M. S. Waterman, and F. Sun, New developments of alignment-free sequence comparison: measures, statistics and next-generation sequencing, Brief Bioinform, 15, 3, 343–353, May 2014. https://doi.org/10.1093/BIB/BBT067.
  • H.-H. Huang and C. Yu, Clustering DNA sequences using the out-of-place measure with reduced n-grams, J Theor Biol, 406, 61–72, 2016. https://doi.org/https://doi.org/10.1016/j.jtbi.2016.06.029.
  • M. S. Nawaz, P. Fournier-Viger, M. Aslam, W. Li, Y. He, and X. Niu, Using alignment-free and pattern mining methods for SARS-CoV-2 genome analysis, Applied Intelligence, 53, 19, 21920–21943, Oct. 2023. https://doi.org/10.1007/S10489-023-04618-0/TABLES/13.
  • T. Wang, Z. G. Yu, and J. Li, CGRWDL: alignment-free phylogeny reconstruction method for viruses based on chaos game representation weighted by dynamical language model, Front Microbiol, 15, p. 1339156, Mar. 2024. https://doi.org/10.3389/FMICB.2024.1339156/BIBTEX.
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There are 57 citations in total.

Details

Primary Language English
Subjects Deep Learning, Data Engineering and Data Science
Journal Section Articles
Authors

Emre Delibaş 0000-0001-7564-5020

Early Pub Date March 17, 2025
Publication Date
Submission Date October 29, 2024
Acceptance Date February 5, 2025
Published in Issue Year 2025 Volume: 14 Issue: 2

Cite

APA Delibaş, E. (2025). Using transfer learning models for DNA sequence similarity via fCGR method. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, 14(2), 1-1. https://doi.org/10.28948/ngumuh.1575701
AMA Delibaş E. Using transfer learning models for DNA sequence similarity via fCGR method. NOHU J. Eng. Sci. March 2025;14(2):1-1. doi:10.28948/ngumuh.1575701
Chicago Delibaş, Emre. “Using Transfer Learning Models for DNA Sequence Similarity via FCGR Method”. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi 14, no. 2 (March 2025): 1-1. https://doi.org/10.28948/ngumuh.1575701.
EndNote Delibaş E (March 1, 2025) Using transfer learning models for DNA sequence similarity via fCGR method. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi 14 2 1–1.
IEEE E. Delibaş, “Using transfer learning models for DNA sequence similarity via fCGR method”, NOHU J. Eng. Sci., vol. 14, no. 2, pp. 1–1, 2025, doi: 10.28948/ngumuh.1575701.
ISNAD Delibaş, Emre. “Using Transfer Learning Models for DNA Sequence Similarity via FCGR Method”. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi 14/2 (March 2025), 1-1. https://doi.org/10.28948/ngumuh.1575701.
JAMA Delibaş E. Using transfer learning models for DNA sequence similarity via fCGR method. NOHU J. Eng. Sci. 2025;14:1–1.
MLA Delibaş, Emre. “Using Transfer Learning Models for DNA Sequence Similarity via FCGR Method”. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, vol. 14, no. 2, 2025, pp. 1-1, doi:10.28948/ngumuh.1575701.
Vancouver Delibaş E. Using transfer learning models for DNA sequence similarity via fCGR method. NOHU J. Eng. Sci. 2025;14(2):1-.

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