1983
DOI: 10.1016/s0021-9258(18)33196-x
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H curves, a novel method of representation of nucleotide series especially suited for long DNA sequences.

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Cited by 288 publications
(32 citation statements)
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“…A number of studies have been devoted to examining the structure of nucleic acids sequences subjected to a variety of mathematical transforms, in order to uncover pattern irregularities in the DNA, that often result from constraints and are therefore frequently associated with function [12][13][14][15][16][17][18][19], also using graphical approaches [5,6]. By our approach, ancient informational polymers, old bacterial tRNAs [20], present significant lower values of LZ complexity, Entropy and Hurst indexes than random sequence data (white noise).…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…A number of studies have been devoted to examining the structure of nucleic acids sequences subjected to a variety of mathematical transforms, in order to uncover pattern irregularities in the DNA, that often result from constraints and are therefore frequently associated with function [12][13][14][15][16][17][18][19], also using graphical approaches [5,6]. By our approach, ancient informational polymers, old bacterial tRNAs [20], present significant lower values of LZ complexity, Entropy and Hurst indexes than random sequence data (white noise).…”
Section: Discussionmentioning
confidence: 99%
“…Random data (white noise) were obtained from the algorithm by Press and Teukolsky [4] and their orbit walks were obtained generating an uniformly and randomly distributed data points over the unit interval (0 to 1). Based on the graphical approaches by Hamori and Ruskin and Mizrahi & Ninio [5,6], we have analyzed nucleotide sequences of nonintronic tRNAs and of computer-generated random data describing them as random walks [7] by means of softwares developed in Visual Basic language by the first Author of the paper (Figures 1 and 2).…”
Section: Random Datamentioning
confidence: 99%
“…The most frequently used similarity measures for analyzing differences or similarities between DNA sequences based their corresponding graphical representation are Euclidean distances or correlation angles. For papers that adopt graphical representation for DNA similarity analysis, the reader is referred to references [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21].…”
Section: Introductionmentioning
confidence: 99%
“…1 The first three-dimensional (3D) geometric representation for DNA sequences was presented by Hamori and Ruskin. 14 Later, 2D, [15][16][17][18][19][20] 3D, [21][22][23][24][25] 4D, 26 5D, 27 and 6D 28 representations of DNA sequences were developed. These methodologies represent DNA as matrices that were associated with the selected geometrical objects, as well as vectors that were composed of invariants of matrices that were used to compare DNA sequences.…”
Section: Introductionmentioning
confidence: 99%