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Multi-Fractal Analysis for Feature Extraction from DNA Sequences
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Author(s): Witold Kinsner (University of Manitoba, Canada)and Hong Zhang (University of Manitoba, Canada)
Copyright: 2012
Pages: 19
Source title:
Breakthroughs in Software Science and Computational Intelligence
Source Author(s)/Editor(s): Yingxu Wang (University of Calgary, Canada)
DOI: 10.4018/978-1-4666-0264-9.ch007
PurchaseView on the publisher's website for pricing and purchasing information.
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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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