Low-Homology Protein Structural Class Prediction from Secondary Structure Based on Visibility and Horizontal Visibility Network
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[1] M M Gromiha,et al. Protein secondary structure prediction in different structural classes. , 1998, Protein engineering.
[2] Yu-Dong Cai,et al. Support vector machines for prediction of protein domain structural class. , 2003, Journal of theoretical biology.
[3] Xin Chen,et al. Prediction of protein structural classes for low-homology sequences based on predicted secondary structure , 2010, BMC Bioinformatics.
[4] Shengli Zhang,et al. High-accuracy prediction of protein structural class for low-similarity sequences based on predicted secondary structure. , 2011, Biochimie.
[5] R. Jernigan,et al. Understanding the recognition of protein structural classes by amino acid composition , 1997, Proteins.
[6] Chih-Jen Lin,et al. LIBSVM: A library for support vector machines , 2011, TIST.
[7] B. Luque,et al. Horizontal visibility graphs: exact results for random time series. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.
[8] Cangzhi Jia,et al. A high-accuracy protein structural class prediction algorithm using predicted secondary structural information. , 2010, Journal of theoretical biology.
[9] K. Chou,et al. Analysis and Prediction of the Metabolic Stability of Proteins Based on Their Sequential Features, Subcellular Locations and Interaction Networks , 2010, PloS one.
[10] A. Fiser,et al. Chaos game representation of protein structures. , 1994, Journal of molecular graphics.
[11] Abdollah Dehzangi,et al. A Combination of Feature Extraction Methods with an Ensemble of Different Classifiers for Protein Structural Class Prediction Problem , 2013, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[12] K. Chou,et al. iSNO-PseAAC: Predict Cysteine S-Nitrosylation Sites in Proteins by Incorporating Position Specific Amino Acid Propensity into Pseudo Amino Acid Composition , 2013, PloS one.
[13] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[14] L. Freeman. Centrality in social networks conceptual clarification , 1978 .
[15] Lukasz A. Kurgan,et al. SPINE X: Improving protein secondary structure prediction by multistep learning coupled with prediction of solvent accessible surface area and backbone torsion angles , 2012, J. Comput. Chem..
[16] Minghui Wang,et al. Prediction of protein structural class for low-similarity sequences using Chou's pseudo amino acid composition and wavelet denoising. , 2017, Journal of molecular graphics & modelling.
[17] Kuldip K. Paliwal,et al. Exploring Potential Discriminatory Information Embedded in PSSM to Enhance Protein Structural Class Prediction Accuracy , 2013, PRIB.
[18] C. Chothia,et al. Structural patterns in globular proteins , 1976, Nature.
[19] Yan Li,et al. High-accuracy prediction of protein structural classes using PseAA structural properties and secondary structural patterns. , 2014, Biochimie.
[20] K. Chou. A novel approach to predicting protein structural classes in a (20–1)‐D amino acid composition space , 1995, Proteins.
[21] Shan Chang,et al. Amino acid network and its scoring application in protein-protein docking. , 2008, Biophysical chemistry.
[22] I. Gutman,et al. Laplacian energy of a graph , 2006 .
[23] A G Murzin,et al. SCOP: a structural classification of proteins database for the investigation of sequences and structures. , 1995, Journal of molecular biology.
[24] Kuldip K. Paliwal,et al. Proposing a highly accurate protein structural class predictor using segmentation-based features , 2014, BMC Genomics.
[25] Vladimir Vapnik,et al. The Nature of Statistical Learning , 1995 .
[26] D T Jones,et al. Protein secondary structure prediction based on position-specific scoring matrices. , 1999, Journal of molecular biology.
[27] Gert Sabidussi,et al. The centrality index of a graph , 1966 .
[28] 赵熙强,et al. A protein structural class prediction method based on novel features , 2013 .
[29] M E J Newman. Assortative mixing in networks. , 2002, Physical review letters.
[30] Lucas Lacasa,et al. From time series to complex networks: The visibility graph , 2008, Proceedings of the National Academy of Sciences.
[31] Zu-Guo Yu,et al. Secondary Structure Element Alignment Kernel Method for Prediction of Protein Structural Classes , 2014 .
[32] Kuldip K. Paliwal,et al. A strategy to select suitable physicochemical attributes of amino acids for protein fold recognition , 2013, BMC Bioinformatics.
[33] Yang Li,et al. A novel protein structural classes prediction method based on predicted secondary structure. , 2012, Biochimie.
[34] Shigeo Abe DrEng. Pattern Classification , 2001, Springer London.
[35] Zu-Guo Yu,et al. Topological properties and fractal analysis of a recurrence network constructed from fractional Brownian motions. , 2014, Physical review. E, Statistical, nonlinear, and soft matter physics.
[36] Zu-Guo Yu,et al. Prediction of protein structural classes by recurrence quantification analysis based on chaos game representation. , 2009 .
[37] G M Maggiora,et al. A heuristic approach to predicting the tertiary structure of bovine somatotropin. , 1991, Biochemistry.
[38] I. A. Emerson,et al. Network analysis of transmembrane protein structures , 2012 .