BioImpacts. 2021;11(2): 87-99.
doi: 10.34172/bi.2021.16
PMID: 33842279
PMCID: PMC8022238
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Original Research

A Markov chain-based feature extraction method for classification and identification of cancerous DNA sequences

Amin Khodaei 1 ORCID logo, Mohammad-Reza Feizi-Derakhshi 1* ORCID logo, Behzad Mozaffari-Tazehkand 1 ORCID logo

1 Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
Corresponding author: Mohammad-Reza Feizi-Derakhshi, Email: mfeizi@tabrizu.ac.ir mfeizi@tabrizu.ac.ir


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.
Keywords: DNA sequence, Cancer, Classification, Markov chain, Support vector machine
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Submitted: 23 Jul 2019
Revision: 06 Jan 2020
Accepted: 21 Jan 2020
ePublished: 24 Mar 2020
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