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Multi-Fractal Analysis for Feature Extraction from DNA Sequences

Multi-Fractal Analysis for Feature Extraction from DNA Sequences

Witold Kinsner, Hong Zhang
Copyright: © 2010 |Volume: 2 |Issue: 2 |Pages: 18
ISSN: 1942-9045|EISSN: 1942-9037|EISBN13: 9781609604196|DOI: 10.4018/jssci.2010040101
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MLA

Kinsner, Witold, and Hong Zhang. "Multi-Fractal Analysis for Feature Extraction from DNA Sequences." IJSSCI vol.2, no.2 2010: pp.1-18. http://doi.org/10.4018/jssci.2010040101

APA

Kinsner, W. & Zhang, H. (2010). Multi-Fractal Analysis for Feature Extraction from DNA Sequences. International Journal of Software Science and Computational Intelligence (IJSSCI), 2(2), 1-18. http://doi.org/10.4018/jssci.2010040101

Chicago

Kinsner, Witold, and Hong Zhang. "Multi-Fractal Analysis for Feature Extraction from DNA Sequences," International Journal of Software Science and Computational Intelligence (IJSSCI) 2, no.2: 1-18. http://doi.org/10.4018/jssci.2010040101

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Abstract

This paper presents estimations of multi-scale (multi-fractal) measures for feature extraction from deoxyribonucleic acid (DNA) sequences, and demonstrates the intriguing possibility of identifying biological functionality using information contained within the DNA sequence. We have developed a technique that seeks patterns or correlations in the DNA sequence at a higher level than the local base-pair structure. The technique has three main steps: (i) transforms the DNA sequence symbols into a modified Lévy walk, (ii) transforms the Lévy walk into a signal spectrum, and (iii) breaks the spectrum into sub-spectra and treats each of these as an attractor from which the multi-fractal dimension spectrum is estimated. An optimal minimum window size and volume element size are found for estimation of the multi-fractal measures. Experimental results show that DNA is multi-fractal, and that the multi-fractality changes depending upon the location (coding or non-coding region) in the sequence.

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