Prediction of protein structural classes by recurrence quantification analysis based on chaos game representation

Peng, Zhenling; Yu, Zu-Guo; Zhang, Rui-Jie; Anh, Vo; Wang, Desheng

doi:10.1016/j.jtbi.2008.12.027

Cited by 109 publications

(87 citation statements)

References 78 publications

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“…To evaluate our proposed approach, we employe three benchmarks with low sequence identity including 25PDB(the homology-range between 22 and 45%) (Yang et al, 2009), 1189 (less than 40% sequence similarities) (Yang et al, 2009) and 640 (with 25% sequence identity) (Yang et al, 2010), respectively. The data sets in this study and the number of proteins belonging to four structural classes are shown in Table 1.…”

Section: Data Setsmentioning

confidence: 99%

“…The method of feature extraction contains several aspects. Such as physicochemical based information (Dehzangi et al, 2013a;Sharma et al, 2013), structural based information (Yang et al, 2009;Zhang et al, 2013;Liu and Jia, 2010;Zhang et al, 2011;Ding et al, 2012;Han et al, 2014;Dehzangi et al, 2013b;Wang et al, 2014). Yu et al (2017) use Chous pseudo amino acid composition and wavelet denoising to prediction structural class.…”

Section: Introductionmentioning

confidence: 99%

“…From 2014 to now, several papers (Dehzangi et al, 2014;Wang et al, 2014;Jones, 1999;Faraggi et al, 2012) show that the protein secondary structure is significanc to predict protein structural classes. Firstly the features are extracted, secondly all kinds of algorithms can be used to implement the classification prediction, such as Fisher's linear discriminant algorithm (Yang et al, 2009), Support Vector Machine (SVM) (Cai et al, 2003) and so on.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Low-Homology Protein Structural Class Prediction from Secondary Structure Based on Visibility and Horizontal Visibility Network

Zhao¹,

Luo²,

Liu³

2018

American Journal of Biochemistry and Biotechnology

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In this study, based on the predicted secondary structures of proteins, we propose a new approach to predict protein structural classes (α,β,α/β,α+β) for three widely used low-homology data sets. Fist, we obtain two time siries from the chaos game representation of each predicted secondary structure; second, based on two time series, we construct visibility and horizontal visibility network, respectively and generate a set of features using 17 network features; finaly, we predict each protein structure class using support vector machine and Fisher's linear discriminant algorithm, respectively. In order to evaluate our method, the leave one out cross-validating test is employed on three data sets. Results show that our approach has been provided as a effective tool for the prediction of low-homology protein structural classes.

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Section: Data Setsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Low-Homology Protein Structural Class Prediction from Secondary Structure Based on Visibility and Horizontal Visibility Network

Zhao¹,

Luo²,

Liu³

2018

American Journal of Biochemistry and Biotechnology

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“…The chaos game representation image gives local as well as global visual information which can be identified as both qualitative and quantitative expressions of the order, regularity, structure, and complexity of DNA sequences (Zbilut et al 2002;Yang et al, 2009).…”

Section: Similarity Evaluation Derived From Cgrmentioning

confidence: 99%

Similarity analysis for DNA sequences based on chaos game representation. Case study: The albumin

Stan

Cristescu

Scarlat

2010

Journal of Theoretical Biology

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This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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“…In 1997, a similar CGR algorithm was presented by Basu, et al [16] to represent a protein sequence by using a 12-sided regular polygon, each vertex of which represents a class of amino acid residues on the basis of conservative substitutions. Up to present, CGR method has achieved some applications in the studies of bioinformatics [17][18][19][20][21]. Moreover, fractal dimensions (FD) are important features of highly irregular geometries.…”

Section: Introductionmentioning

confidence: 99%

A novel fractal approach for predicting G-protein–coupled receptors and their subfamilies with support vector machines

Nie

Wang

et al. 2015

BME

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Abstract. G-protein-coupled receptors (GPCRs) are seven membrane-spanning proteins and regulate many important physiological processes, such as vision, neurotransmission, immune response and so on. GPCRs-related pathways are the targets of a large number of marketed drugs. Therefore, the design of a reliable computational model for predicting GPCRs from amino acid sequence has long been a significant biomedical problem. Chaos game representation (CGR) reveals the fractal patterns hidden in protein sequences, and then fractal dimension (FD) is an important feature of these highly irregular geometries with concise mathematical expression. Here, in order to extract important features from GPCR protein sequences, CGR algorithm, fractal dimension and amino acid composition (AAC) are employed to formulate the numerical features of protein samples. Four groups of features are considered, and each group is evaluated by support vector machine (SVM) and 10-fold cross-validation test. To test the performance of the present method, a new non-redundant dataset was built based on latest GPCRDB database. Comparing the results of numerical experiments, the group of combined features with AAC and FD gets the best result, the accuracy is 99.22% and Matthew's correlation coefficient (MCC) is 0.9845 for identifying GPCRs from non-GPCRs. Moreover, if it is classified as a GPCR, it will be further put into the second level, which will classify a GPCR into one of the five main subfamilies. At this level, the group of combined features with AAC and FD also gets best accuracy 85.73%. Finally, the proposed predictor is also compared with existing methods and shows better performances.

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Prediction of protein structural classes by recurrence quantification analysis based on chaos game representation

Cited by 109 publications

References 78 publications

Low-Homology Protein Structural Class Prediction from Secondary Structure Based on Visibility and Horizontal Visibility Network

Low-Homology Protein Structural Class Prediction from Secondary Structure Based on Visibility and Horizontal Visibility Network

Similarity analysis for DNA sequences based on chaos game representation. Case study: The albumin

A novel fractal approach for predicting G-protein–coupled receptors and their subfamilies with support vector machines

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