Multifractal characterization of complete genomes

Anh, Vo; Lau, Ka–Sing; Yu, Zu-Guo

doi:10.1088/0305-4470/34/36/301

Cited by 28 publications

(37 citation statements)

References 20 publications

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“…As can be seen in Figure 2, our earlier work, 28 and that of Yu et al, 12 the multifractal technique has some promise in classifying bacteria and generating phylogenetic trees but has difficulties distinguishing between closely related bacteria. This is due to the fact that it ignores information differences in structure between different genomes and also the features of the genomes such as genes and repeat sequences.…”

Section: Evalution Of Multifractal Methodsmentioning

confidence: 84%

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Stochastic evolution and multifractal classification of prokaryotes

Berryman

Allison

Abbott

2003

Fluctuations and Noise in Biological, Biophysical, and Biomedical Systems

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We introduce a model for simulating mutation of prokaryote DNA sequences. Using that model we can then evaluated traditional techniques like parsimony and maximum likelihood methods for computing phylogenetic relationships. We also use the model to mimic large scale genomic changes, and use this to evaluate multifractal and related information theory techniques which take into account these large changes in determining phylogenetic relationships.

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Section: Evalution Of Multifractal Methodsmentioning

confidence: 84%

“…11 Other techniques which we have explored in this paper include a multifractal approach 12 and related information theory approaches.…”

mentioning

confidence: 99%

Stochastic evolution and multifractal classification of prokaryotes

Berryman

Allison

Abbott

2003

Fluctuations and Noise in Biological, Biophysical, and Biomedical Systems

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“…Generalizing these methods, MFA has proved to be useful to characterize both theoretical and experimental heterogeneous spatial patterns [Grassberger and Procaccia, 1983;Halsey et al, 1986]. MFA has been successfully applied in financial modeling [e.g., Canessa, 2000;Anh et al, 2000], biological systems including DNA and protein sequences [e.g., Anh et al, 2001Anh et al, , 2002Yu et al, 2001Yu et al, , 2003Yu et al, , 2004Yu et al, , 2006, and geophysical systems including rain and clouds YU ET AL.…”

Section: Journal Of Geophysical Research: Space Physicsmentioning

confidence: 99%

Underlying scaling relationships between solar activity and geomagnetic activity revealed by multifractal analyses

Anh

Eastes

2014

JGR Space Physics

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This paper identifies some scaling relationships between solar activity and geomagnetic activity. We examine the scaling properties of hourly data for two geomagnetic indices (a p and AE), two solar indices (solar X-rays X l and solar flux F10.7), and two inner heliospheric indices (ion density Ni and flow speed Vs) over the period 1995-2001 by the universal multifractal approach and the traditional multifractal analysis. We found that the universal multifractal model (UMM) provides a good fit to the empirical K(q) and (q) curves of these time series. The estimated values of the Lévy index in the UMM indicate that multifractality exists in the time series for a p , AE, X l , and Ni, while those for F10.7 and Vs are monofractal.The estimated values of the nonconservation parameter H of this model confirm that these time series are conservative which indicate that the mean value of the process is constant for varying resolution. Additionally, the multifractal K(q) and (q) curves, and the estimated values of the sparseness parameter C 1 of the UMM indicate that there are three pairs of indices displaying similar scaling properties, namely a p and X l , AE and Ni, and F10.7 and Vs. The similarity in the scaling properties of pairs (a p , X l ) and (AE, Ni) suggests that a p and X l , AE and Ni are better correlated-in terms of scaling-than previous thought, respectively. But our results still cannot be used to advance forecasting of a p and AE by X l and Ni, respectively, due to some reasons.

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“…In this paper we adopt a method which is similar to that proposed in Anh et al (2001): If we denote K T (q) the K(q) function defined by Eq. (2), and K d (q) the empirical K(q) function.…”

Section: Universal Multifractal Approachmentioning

confidence: 99%

“…Multifractal analysis was initially proposed to treat turbulence data and is a useful way to characterise the spatial heterogeneity of both theoretical and experimental fractal patterns (Grassberger and Procaccia, 1983;Halsy et al, 1986). It has been applied successfully in many different fields including financial modelling (e.g., Anh et al, 2000;Canessa, 2000), biological systems (e.g., Yu et al, 2001Yu et al, , 2003Yu et al, , 2004Yu et al, , 2006Anh et al, 2001Anh et al, , 2002Zhou et al, 2005), geophysical systems (e.g., Schertzer and Lovejoy, 1987;Schmitt et al, 1992;Tessier et al, 1993Tessier et al, , 1996Olsson, 1995;Olsson and Niemczynowicz, 1996;Harris et al, 1996;Lovejoy et al, 1996;Deidda, 2000;Lilley et al, 2006;Kantelhardt et al, 2006;Veneziano et al, 2006;Venugopal et al, 2006;Schertzer, 2006, 2010a, b;Garcia-Marin et al, 2008;Serinaldi, 2010) and high energy physics (e.g., Ratti et al, 1994). Fractal and multifractal approaches have been quite successful in extracting salient features of physical processes responsible for the near-Earth magnetospheric phenomena (Lui, 2002).…”

Section: Introductionmentioning

confidence: 99%

Multifractal analysis of solar flare indices and their horizontal visibility graphs

Anh

Eastes

et al. 2012

Nonlin. Processes Geophys.

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Abstract. The multifractal properties of the daily solar Xray brightness, X l and X s , during the period from 1 January 1986 to 31 December 2007 which includes two solar cycles are examined using the universal multifractal approach and multifractal detrended fluctuation analysis. Then we convert these time series into networks using the horizontal visibility graph technique. Multifractal analysis of the resulting networks is performed using an algorithm proposed by us. The results from the multifractal analysis show that multifractality exists in both raw daily time series of X-ray brightness and their horizontal visibility graphs. It is also found that the empirical K(q) curves of raw time series can be fitted by the universal multifractal model. The numerical results on the raw data show that the Solar Cycle 23 is weaker than the Solar Cycle 22 in multifractality. The values of h(2) from multifractal detrended fluctuation analysis for these time series indicate that they are stationary and persistent, and the correlations in the time series of Solar Cycle 23 are stronger than those for Solar Cycle 22. Furthermore, the multifractal scaling for the networks of the time series can reflect some properties which cannot be picked up by using the same analysis on the original time series. This suggests a potentially useful method to explore geophysical data.

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Multifractal characterization of complete genomes

Cited by 28 publications

References 20 publications

Stochastic evolution and multifractal classification of prokaryotes

Stochastic evolution and multifractal classification of prokaryotes

Underlying scaling relationships between solar activity and geomagnetic activity revealed by multifractal analyses

Multifractal analysis of solar flare indices and their horizontal visibility graphs

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