A Markov chain-based feature extraction method for classification and identification of cancerous DNA sequences
Introduction: In recent decades, the growing rate of cancer incidence is a big concern for most societies. Due to the genetic origins of cancer disease, its internal structure is necessary for the study of this disease. Methods: In this research, cancer data are analyzed based on DNA sequences. The...
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| Format: | Article |
| Language: | English |
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Tabriz University of Medical Sciences
2021-03-01
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| Series: | BioImpacts |
| Subjects: | |
| Online Access: | https://bi.tbzmed.ac.ir/PDF/bi-11-87.pdf |
| _version_ | 1818926019481960448 |
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| author | Amin Khodaei Mohammad-Reza Feizi-Derakhshi Behzad Mozaffari-Tazehkand |
| author_facet | Amin Khodaei Mohammad-Reza Feizi-Derakhshi Behzad Mozaffari-Tazehkand |
| author_sort | Amin Khodaei |
| collection | DOAJ |
| description | Introduction: In recent decades, the growing rate of cancer incidence is a big concern for most societies. Due to the genetic origins of cancer disease, its internal structure is necessary for the study of this disease. Methods: In this research, cancer data are analyzed based on DNA sequences. The transition probability of occurring two pairs of nucleotides in DNA sequences has Markovian property. This property inspires the idea of feature dimension reduction of DNA sequence for overcoming the high computational overhead of genes analysis. This idea is utilized in this research based on the Markovian property of DNA sequences. This mapping decreases feature dimensions and conserves basic properties for discrimination of cancerous and non-cancerous genes. Results: The results showed that a non-linear support vector machine (SVM) classifier with RBF and polynomial kernel functions can discriminate selected cancerous samples from non-cancerous ones. Experimental results based on the 10-fold cross-validation and accuracy metrics verified that the proposed method has low computational overhead and high accuracy. Conclusion: The proposed algorithm was successfully tested on related research case studies. In general, a combination of proposed Markovian-based feature reduction and non-linear SVM classifier can be considered as one of the best methods for discrimination of cancerous and non-cancerous genes. |
| first_indexed | 2024-12-20T02:50:28Z |
| format | Article |
| id | doaj.art-47ac149be6434ba79cc72e4714e59ead |
| institution | Directory Open Access Journal |
| issn | 2228-5660 2228-5652 |
| language | English |
| last_indexed | 2024-12-20T02:50:28Z |
| publishDate | 2021-03-01 |
| publisher | Tabriz University of Medical Sciences |
| record_format | Article |
| series | BioImpacts |
| spelling | doaj.art-47ac149be6434ba79cc72e4714e59ead2022-12-21T19:56:03ZengTabriz University of Medical SciencesBioImpacts2228-56602228-56522021-03-01112879910.34172/bi.2021.16bi-21850A Markov chain-based feature extraction method for classification and identification of cancerous DNA sequencesAmin Khodaei0Mohammad-Reza Feizi-Derakhshi1Behzad Mozaffari-Tazehkand2Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, IranFaculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, IranFaculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, IranIntroduction: In recent decades, the growing rate of cancer incidence is a big concern for most societies. Due to the genetic origins of cancer disease, its internal structure is necessary for the study of this disease. Methods: In this research, cancer data are analyzed based on DNA sequences. The transition probability of occurring two pairs of nucleotides in DNA sequences has Markovian property. This property inspires the idea of feature dimension reduction of DNA sequence for overcoming the high computational overhead of genes analysis. This idea is utilized in this research based on the Markovian property of DNA sequences. This mapping decreases feature dimensions and conserves basic properties for discrimination of cancerous and non-cancerous genes. Results: The results showed that a non-linear support vector machine (SVM) classifier with RBF and polynomial kernel functions can discriminate selected cancerous samples from non-cancerous ones. Experimental results based on the 10-fold cross-validation and accuracy metrics verified that the proposed method has low computational overhead and high accuracy. Conclusion: The proposed algorithm was successfully tested on related research case studies. In general, a combination of proposed Markovian-based feature reduction and non-linear SVM classifier can be considered as one of the best methods for discrimination of cancerous and non-cancerous genes.https://bi.tbzmed.ac.ir/PDF/bi-11-87.pdfdna sequencecancerclassificationmarkov chainsupport vector machine |
| spellingShingle | Amin Khodaei Mohammad-Reza Feizi-Derakhshi Behzad Mozaffari-Tazehkand A Markov chain-based feature extraction method for classification and identification of cancerous DNA sequences BioImpacts dna sequence cancer classification markov chain support vector machine |
| title | A Markov chain-based feature extraction method for classification and identification of cancerous DNA sequences |
| title_full | A Markov chain-based feature extraction method for classification and identification of cancerous DNA sequences |
| title_fullStr | A Markov chain-based feature extraction method for classification and identification of cancerous DNA sequences |
| title_full_unstemmed | A Markov chain-based feature extraction method for classification and identification of cancerous DNA sequences |
| title_short | A Markov chain-based feature extraction method for classification and identification of cancerous DNA sequences |
| title_sort | markov chain based feature extraction method for classification and identification of cancerous dna sequences |
| topic | dna sequence cancer classification markov chain support vector machine |
| url | https://bi.tbzmed.ac.ir/PDF/bi-11-87.pdf |
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