Relevant bibliographies by topics / CGR (Chaos Game Reprezentece)

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters

Academic literature on the topic 'CGR (Chaos Game Reprezentece)'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "CGR (Chaos Game Reprezentece)"

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SUZUKI, TOMOYA, TOHRU IKEGUCHI, and MASUO SUZUKI. "APPLICATION OF CHAOS GAME REPRESENTATION TO NONLINEAR TIME SERIES ANALYSIS." Fractals 14, no. 01 (2006): 27–35. http://dx.doi.org/10.1142/s0218348x06003064.

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Iterative function systems are often used for investigating fractal structures. The method is also referred as Chaos Game Representation (CGR), and is applied for representing characteristic structures of DNA sequences visually. In this paper, we proposed an original way of plotting CGR to easily confirm the property of the temporal evaluation of a time series. We also showed existence of spurious characteristic structures of time series, if we carelessly applied the CGR to real time series. We revealed that the source of spurious identification came from non-uniformity of the frequency histog
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Jie, Gao, and Xu Zhen-Yuan. "Chaos game representation (CGR)-walk model for DNA sequences." Chinese Physics B 18, no. 1 (2009): 370–76. http://dx.doi.org/10.1088/1674-1056/18/1/060.

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Deng, Wei, and Yihui Luan. "Analysis of Similarity/Dissimilarity of DNA Sequences Based on Chaos Game Representation." Abstract and Applied Analysis 2013 (2013): 1–6. http://dx.doi.org/10.1155/2013/926519.

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The Chaos Game is an algorithm that can allow one to produce pictures of fractal structures. Considering that the four bases A, G, C, and T of DNA sequences can be divided into three classes according to their chemical structure, we propose different kinds of CGR-walk sequences. Based on CGR coordinates of random sequences, we introduce some invariants for the DNA primary sequences. As an application, we can make the examination of similarity/dissimilarity among the first exon ofβ-globin gene of different species. The results indicate that our method is efficient and can get more biological in
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Zheng, Kai, Zhu-Hong You, Jian-Qiang Li, Lei Wang, Zhen-Hao Guo, and Yu-An Huang. "iCDA-CGR: Identification of circRNA-disease associations based on Chaos Game Representation." PLOS Computational Biology 16, no. 5 (2020): e1007872. http://dx.doi.org/10.1371/journal.pcbi.1007872.

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Rekadwad, Bhagwan N., Juan M. Gonzalez, and Chandrahasya N. Khobragade. "Genomic Analysis of a Marine Bacterium: Bioinformatics for Comparison, Evaluation, and Interpretation of DNA Sequences." BioMed Research International 2016 (2016): 1–7. http://dx.doi.org/10.1155/2016/7215379.

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A total of five highly related strains of an unidentified marine bacterium were analyzed through their short genome sequences (AM260709–AM260713). Genome-to-Genome Distance (GGDC) showed high similarity to Pseudoalteromonas haloplanktis (X67024). The generated unique Quick Response (QR) codes indicated no identity to other microbial species or gene sequences. Chaos Game Representation (CGR) showed the number of bases concentrated in the area. Guanine residues were highest in number followed by cytosine. Frequency of Chaos Game Representation (FCGR) indicated that CC and GG blocks have higher f
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Löchel, Hannah F., Dominic Eger, Theodor Sperlea, and Dominik Heider. "Deep learning on chaos game representation for proteins." Bioinformatics 36, no. 1 (2019): 272–79. http://dx.doi.org/10.1093/bioinformatics/btz493.

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AbstractMotivationClassification of protein sequences is one big task in bioinformatics and has many applications. Different machine learning methods exist and are applied on these problems, such as support vector machines (SVM), random forests (RF) and neural networks (NN). All of these methods have in common that protein sequences have to be made machine-readable and comparable in the first step, for which different encodings exist. These encodings are typically based on physical or chemical properties of the sequence. However, due to the outstanding performance of deep neural networks (DNN)
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Jia, Jianhua, Xiaoyan Li, Wangren Qiu, Xuan Xiao, and Kuo-Chen Chou. "iPPI-PseAAC(CGR): Identify protein-protein interactions by incorporating chaos game representation into PseAAC." Journal of Theoretical Biology 460 (January 2019): 195–203. http://dx.doi.org/10.1016/j.jtbi.2018.10.021.

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Anitas, Eugen Mircea. "Small-Angle Scattering and Multifractal Analysis of DNA Sequences." International Journal of Molecular Sciences 21, no. 13 (2020): 4651. http://dx.doi.org/10.3390/ijms21134651.

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The arrangement of A, C, G and T nucleotides in large DNA sequences of many prokaryotic and eukaryotic cells exhibit long-range correlations with fractal properties. Chaos game representation (CGR) of such DNA sequences, followed by a multifractal analysis, is a useful way to analyze the corresponding scaling properties. This approach provides a powerful visualization method to characterize their spatial inhomogeneity, and allows discrimination between mono- and multifractal distributions. However, in some cases, two different arbitrary point distributions, may generate indistinguishable multi
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Zaia, Annamaria, Pierluigi Maponi, Martina Zannotti, and Tiziana Casoli. "Biocomplexity and Fractality in the Search of Biomarkers of Aging and Pathology: Mitochondrial DNA Profiling of Parkinson’s Disease." International Journal of Molecular Sciences 21, no. 5 (2020): 1758. http://dx.doi.org/10.3390/ijms21051758.

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Increasing evidence implicates mitochondrial dysfunction in the etiology of Parkinson’s disease (PD). Mitochondrial DNA (mtDNA) mutations are considered a possible cause and this mechanism might be shared with the aging process and with other age-related neurodegenerative disorders such as Alzheimer’s disease (AD). We have recently proposed a computerized method for mutated mtDNA characterization able to discriminate between AD and aging. The present study deals with mtDNA mutation-based profiling of PD. Peripheral blood mtDNA sequences from late-onset PD patients and age-matched controls were
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Craciunescu, Teddy, Andrea Murari, Ernesto Lerche, and Michela Gelfusa. "Image-Based Methods to Investigate Synchronization between Time Series Relevant for Plasma Fusion Diagnostics." Entropy 22, no. 7 (2020): 775. http://dx.doi.org/10.3390/e22070775.

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Advanced time series analysis and causality detection techniques have been successfully applied to the assessment of synchronization experiments in tokamaks, such as Edge Localized Modes (ELMs) and sawtooth pacing. Lag synchronization is a typical strategy for fusion plasma instability control by pace-making techniques. The major difficulty, in evaluating the efficiency of the pacing methods, is the coexistence of the causal effects with the periodic or quasi-periodic nature of the plasma instabilities. In the present work, a set of methods based on the image representation of time series, are
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Dissertations / Theses on the topic "CGR (Chaos Game Reprezentece)"

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Nedvěd, Jiří. "Zpracování genomických signálů fraktály." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2012. http://www.nusl.cz/ntk/nusl-219634.

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This diploma project is showen possibilities in classification of genomic sequences with CGR and FCGR methods in pictures. From this picture is computed classificator with BCM. Next here is written about the programme and its opportunities for classification. In the end is compared many of sequences computed in different options of programme.
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Book chapters on the topic "CGR (Chaos Game Reprezentece)"

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Nair, Vrinda V., Anita Mallya, Bhavya Sebastian, Indu Elizabeth, and Achuthsankar S. Nair. "Hurst CGR (HCGR) – A Novel Feature Extraction Method from Chaos Game Representation of Genomes." In Advances in Computing and Communications. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22709-7_31.

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