An extension of fuzzy topological approach for comparison of genetic sequences

[1]  Kuo-Chen Chou,et al.  Using optimized evidence-theoretic K-nearest neighbor classifier and pseudo-amino acid composition to predict membrane protein types. , 2005, Biochemical and biophysical research communications.

[2]  Ujjwal Maulik,et al.  Fuzzy clustering of physicochemical and biochemical properties of amino Acids , 2011, Amino Acids.

[3]  K. Chou,et al.  iHSP-PseRAAAC: Identifying the heat shock protein families using pseudo reduced amino acid alphabet composition. , 2013, Analytical biochemistry.

[4]  Giulia Menconi,et al.  Sublinear growth of information in DNA sequences , 2004, Bulletin of mathematical biology.

[5]  Kuo-Chen Chou,et al.  Predicting enzyme family class in a hybridization space , 2004, Protein science : a publication of the Protein Society.

[6]  Bin Ma,et al.  A General Edit Distance between RNA Structures , 2002, J. Comput. Biol..

[7]  I. Nookaew,et al.  Insights from 20 years of bacterial genome sequencing , 2015, Functional & Integrative Genomics.

[8]  Juan J. Nieto,et al.  A metric space to study differences between polynucleotides , 2003, Appl. Math. Lett..

[9]  Kuo-Chen Chou,et al.  Boosting classifier for predicting protein domain structural class. , 2005, Biochemical and biophysical research communications.

[10]  K. Chou,et al.  Recent progress in protein subcellular location prediction. , 2007, Analytical biochemistry.

[11]  Michael G Sadovsky,et al.  The method to compare nucleotide sequences based on the minimum entropy principle , 2003, Bulletin of mathematical biology.

[12]  K. Chou.  Prediction of protein cellular attributes using pseudo‐amino acid composition , 2001, Proteins.

[13]  Xin Chen,et al.  An information-based sequence distance and its application to whole mitochondrial genome phylogeny , 2001, Bioinform..

[14]  Bhaskar D. Kulkarni,et al.  Using pseudo amino acid composition to predict protein subnuclear localization: Approached with PSSM , 2007, Pattern Recognit. Lett..

[15]  Kuo-Chen Chou,et al.  Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes , 2005, Bioinform..

[16]  T E Karakasidis,et al.  Fuzzy polynucleotide spaces and metrics , 2006, Bulletin of mathematical biology.

[17]  Qianzhong Li,et al.  Using pseudo amino acid composition to predict protein structural class: Approached by incorporating 400 dipeptide components , 2007, J. Comput. Chem..

[18]  Mercè Llabrés,et al.  A new family of metrics for biopolymer contact structures , 2004, Comput. Biol. Chem..

[19]  B. Tang,et al.  Evaluation of some DNA cloning strategies , 2000 .

[20]  Rui Zhao,et al.  An Overview of the Prediction of Protein DNA-Binding Sites , 2015, International journal of molecular sciences.

[21]  Kuo-Chen Chou,et al.  Prediction of protease types in a hybridization space. , 2006, Biochemical and biophysical research communications.

[22]  Juan J. Nieto,et al.  Midpoints for fuzzy sets and their application in medicine , 2003, Artif. Intell. Medicine.

[23]  K. Chou,et al.  Euk-mPLoc: a fusion classifier for large-scale eukaryotic protein subcellular location prediction by incorporating multiple sites. , 2007, Journal of proteome research.

[24]  Bernhard O. Palsson,et al.  Dynamic simulation of the human red blood cell metabolic network , 2001, Bioinform..

[25]  Stephen C. J. Parker,et al.  Accurate and comprehensive sequencing of personal genomes. , 2011, Genome research.

[26]  Juan J. Nieto,et al.  Fuzzy Logic in Medicine and Bioinformatics , 2006, Journal of biomedicine & biotechnology.

[27]  Juan J. Nieto,et al.  The fuzzy polynucleotide space: basic properties , 2003, Bioinform..

[28]  C. E. SHANNON,et al.  A mathematical theory of communication , 1948, MOCO.

[29]  Pufeng Du,et al.  PseAAC-General: Fast Building Various Modes of General Form of Chou’s Pseudo-Amino Acid Composition for Large-Scale Protein Datasets , 2014, International journal of molecular sciences.

[30]  Qian-zhong Li,et al.  Predict mycobacterial proteins subcellular locations by incorporating pseudo-average chemical shift into the general form of Chou's pseudo amino acid composition. , 2012, Journal of theoretical biology.

[31]  Hao Lin,et al.  Predicting conotoxin superfamily and family by using pseudo amino acid composition and modified Mahalanobis discriminant. , 2007, Biochemical and biophysical research communications.

[32]  Burkhard Morgenstern,et al.  A simple and space-efficient fragment-chaining algorithm for alignment of DNA and protein sequences , 2002, Appl. Math. Lett..

[33]  Kuo-Chen Chou,et al.  Fuzzy KNN for predicting membrane protein types from pseudo-amino acid composition. , 2006, Journal of theoretical biology.

[34]  Sukanta Mondal,et al.  Pseudo amino acid composition and multi-class support vector machines approach for conotoxin superfamily classification. , 2006, Journal of theoretical biology.

[35]  K. Chou,et al.  EzyPred: a top-down approach for predicting enzyme functional classes and subclasses. , 2007, Biochemical and biophysical research communications.

[36]  M. Wang,et al.  Low-frequency Fourier spectrum for predicting membrane protein types. , 2005, Biochemical and biophysical research communications.

[37]  Peixiang Cai,et al.  Predicting protein structural class with pseudo-amino acid composition and support vector machine fusion network. , 2006, Analytical biochemistry.

[38]  Vladimir D. Gusev,et al.  On the complexity measures of genetic sequences , 1999, Bioinform..

[39]  Kuo-Chen Chou,et al.  Prediction of protein structure classes with pseudo amino acid composition and fuzzy support vector machine network. , 2007, Protein and peptide letters.

[40]  Tao Jiang,et al.  On the Complexity and Approximation of Syntenic Distance , 1998, Discret. Appl. Math..

[41]  K. Chou,et al.  iRSpot-TNCPseAAC: Identify Recombination Spots with Trinucleotide Composition and Pseudo Amino Acid Components , 2014, International journal of molecular sciences.

[42]  Kuo-Chen Chou,et al.  Using stacked generalization to predict membrane protein types based on pseudo-amino acid composition. , 2006, Journal of theoretical biology.

[43]  Jiu-Lun Fan,et al.  Some new fuzzy entropy formulas , 2002, Fuzzy Sets Syst..

[44]  Kuo-Chen Chou,et al.  Signal-CF: a subsite-coupled and window-fusing approach for predicting signal peptides. , 2007, Biochemical and biophysical research communications.

[45]  Wei Chen,et al.  iNuc-PhysChem: A Sequence-Based Predictor for Identifying Nucleosomes via Physicochemical Properties , 2012, PloS one.

[46]  Mike A. Steel,et al.  Metrics on RNA Secondary Structures , 2000, J. Comput. Biol..

[47]  Kuo-Chen Chou,et al.  MemType-2L: a web server for predicting membrane proteins and their types by incorporating evolution information through Pse-PSSM. , 2007, Biochemical and biophysical research communications.

[48]  T. Urban,et al.  Whole-genome sequencing in pharmacogenetics. , 2013, Pharmacogenomics.

[49]  J. Lupski,et al.  Human genome sequencing in health and disease. , 2012, Annual review of medicine.

[50]  Settimo Termini,et al.  A Definition of a Nonprobabilistic Entropy in the Setting of Fuzzy Sets Theory , 1972, Inf. Control..

[51]  Michael Zaus.  Crisp and Soft Computing with Hypercubical Calculus , 1999 .

[52]  Yanda Li,et al.  Prediction of protein submitochondria locations by hybridizing pseudo-amino acid composition with various physicochemical features of segmented sequence , 2006, BMC Bioinformatics.

[53]  Kuo-Chen Chou,et al.  Predicting protein subnuclear location with optimized evidence-theoretic K-nearest classifier and pseudo amino acid composition. , 2005, Biochemical and biophysical research communications.

[54]  Ying-Li Chen,et al.  Prediction of the subcellular location of apoptosis proteins. , 2007, Journal of theoretical biology.

[55]  K. Chou,et al.  iNitro-Tyr: Prediction of Nitrotyrosine Sites in Proteins with General Pseudo Amino Acid Composition , 2014, PloS one.

[56]  David Liben-Nowell.  On the Structure of Syntenic Distance , 2001, J. Comput. Biol..

[57]  Yufang Qin,et al.  Locating apoptosis proteins by incorporating the signal peptide cleavage sites into the general form of Chou's Pseudo amino acid composition , 2013 .

[58]  K. Chou,et al.  Cell-PLoc: a package of Web servers for predicting subcellular localization of proteins in various organisms , 2008, Nature Protocols.

[59]  Kuo-Chen Chou,et al.  Using pseudo amino acid composition to predict protein structural classes: Approached with complexity measure factor , 2006, J. Comput. Chem..

[60]  Rongguo Fu,et al.  Advances in the Techniques for the Prediction of microRNA Targets , 2013, International journal of molecular sciences.

[61]  T. Ramakrishnan,et al.  Multi-Fuzzy Sets , 2013 .

[62]  Kuo-Chen Chou,et al.  Predicting eukaryotic protein subcellular location by fusing optimized evidence-theoretic K-Nearest Neighbor classifiers. , 2006, Journal of proteome research.

[63]  P. Suganthan,et al.  AFP-Pred: A random forest approach for predicting antifreeze proteins from sequence-derived properties. , 2011, Journal of theoretical biology.

[64]  Tatiana Lokot,et al.  A new scale-invariant geometry on L1 spaces , 2004, Appl. Math. Lett..

[65]  K. Chou,et al.  Hum-mPLoc: an ensemble classifier for large-scale human protein subcellular location prediction by incorporating samples with multiple sites. , 2007, Biochemical and biophysical research communications.

[66]  K. Chou,et al.  Signal-3L: A 3-layer approach for predicting signal peptides. , 2007, Biochemical and biophysical research communications.

[67]  Kuo-Chen Chou,et al.  Ensemble classifier for protein fold pattern recognition , 2006, Bioinform..

[68]  Kazem Sadegh-Zadeh,et al.  Fundamentals of clinical methodology: 2. Etiology , 1998, Artif. Intell. Medicine.

[69]  Xiaoyong Zou,et al.  Using pseudo-amino acid composition and support vector machine to predict protein structural class. , 2006, Journal of theoretical biology.

[70]  Tatiana Lokot,et al.  A simple proof of the triangle inequality for the NTV metric , 2003, Appl. Math. Lett..

[71]  T. V. Ramakrishnan,et al.  Multi-fuzzy sets: An extension of fuzzy sets , 2011 .

[72]  T. V. Ramakrishnan,et al.  Multi-Fuzzy Extensions of Functions , 2011, Adv. Data Sci. Adapt. Anal..

[73]  Kuo-Chen Chou,et al.  GPCR-2L: predicting G protein-coupled receptors and their types by hybridizing two different modes of pseudo amino acid compositions. , 2011, Molecular bioSystems.

[74]  Cathy M. Helgason,et al.  The fuzzy cube and causal efficacy: representation of concomitant mechanisms in stroke , 1998, Neural Networks.

[75]  Kuo-Chen Chou,et al.  Large‐scale plant protein subcellular location prediction , 2007, Journal of cellular biochemistry.

[76]  Zhanchao Li,et al.  Using Chou's amphiphilic pseudo-amino acid composition and support vector machine for prediction of enzyme subfamily classes. , 2007, Journal of theoretical biology.

[77]  Kuo-Bin Li,et al.  Predicting membrane protein types by incorporating protein topology, domains, signal peptides, and physicochemical properties into the general form of Chou's pseudo amino acid composition. , 2013, Journal of theoretical biology.

[78]  Mark J. Forster,et al.  Application of distance geometry to 3D visualization of sequence relationships , 1999, Bioinform..

[79]  Ying-Li Chen,et al.  Prediction of apoptosis protein subcellular location using improved hybrid approach and pseudo-amino acid composition. , 2007, Journal of theoretical biology.

[80]  Peter D. Karp,et al.  The comprehensive updated regulatory network of Escherichia coli K-12 , 2006, BMC Bioinformatics.

[81]  K. Chou,et al.  Predicting protein quaternary structure by pseudo amino acid composition , 2003, Proteins.