Natural Science

Natural Science

ISSN Print: 2150-4091
ISSN Online: 2150-4105
www.scirp.org/journal/ns
E-mail: ns@scirp.org
"Wenxiang: a web-server for drawing wenxiang diagrams"
written by Kuo-Chen Chou, Wei-Zhong Lin, Xuan Xiao,
published by Natural Science, Vol.3 No.10, 2011
has been cited by the following article(s):
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[2] The NMR Studies of the Interactions between Sialyllactoses and Polysialytransferase Domain for the Polysialylation Inhibition
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[3] The graphical studies of the major molecular interactions for neural cell adhesion molecule (NCAM) polysialylation by incorporating Wenxiang Diagram into …
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[4] Controlling oncogenic KRAS signaling pathways with a Palladium-responsive peptide
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[5] Wenxiang 3.0: Evolutionary Visualization of α, π, and 3/10 Helices
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[6] The significant and profound impacts of Gordon life science institute
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[7] Recent progresses for computationally identifying N6-methyladenosine sites in Saccharomyces cerevisiae
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[8] Helixvis: Visualize α-Helical Peptides in Python
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[9] Using Chou's 5-steps rule to predict O-linked serine glycosylation sites by blending position relative features and statistical moment
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[10] Use Chou's 5-steps rule with different word embedding types to boost performance of electron transport protein prediction model
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[11] The divination of things by things
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[12] RETRACTED ARTICLE: An insightful 20-year recollection since the birth of pseudo amino acid components
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[13] Some illuminating remarks on molecular genetics and genomics as well as drug development
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[14] The Development of Gordon Life Science Institute: Its Driving Force and Accomplishments
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[15] Use Chou's 5-steps rule to predict remote homology proteins by merging grey incidence analysis and domain similarity analysis
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[16] Noah's Ark and Internet Institutes: When and Why?
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[17] Gordon Life Science Institute and Its Impacts on Computational Biology and Drug Development
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[18] The Implication of “I Am the Alpha and the Omega” to Internet Institutes
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[19] The Pandemic Pestilences and Internet Institutes
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[20] The speed of FtsZ treadmilling is tightly regulated by membrane binding
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[21] cACP: Classifying anticancer peptides using discriminative intelligent model via Chou's 5-step rules and general pseudo components
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[22] Advances in predicting subcellular localization of multi-label proteins and its implication for developing multi-target drugs
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[23] An Insightful 10-year Recollection Since the Emergence of the 5-steps Rule.
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[24] The cradle of Gordon Life Science Institute and its development and driving force
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[25] An alignment-free measure based on physicochemical properties of amino acids for protein sequence comparison
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[26] VGSC2: Second generation vector graph toolkit of genome synteny and collinearity
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[27] Advance in Predicting Subcellular Localization of Multi-label Proteins and its Implication for Developing Multi-target Drugs
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[28] iRSpot-SPI: Deep learning-based recombination spots prediction by incorporating secondary sequence information coupled with physio-chemical properties via …
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[29] Identification and characterization of WD40 superfamily genes in peach
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[30] Bioimage-based Prediction of Protein Subcellular Location in Human Tissue with Ensemble Features and Deep Networks
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[31] Studying Calcium Ion-Dependent Effect on the Inter-subunit Interaction Between the cTnC N-terminal Domain and cTnI C-terminal Switch Peptide of Human Cardiac …
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[32] Established and In-trial GPCR Families in Clinical Trials: A Review for Target Selection
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[33] iHyd-PseAAC (EPSV): Identifying Hydroxylation Sites in Proteins by Extracting Enhanced Position and Sequence Variant Feature via Chou's 5-Step Rule and General …
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[34] Progresses in predicting post-translational modification
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[35] iHyd-PseAAC (EPSV): Identifying Hydroxylation Sites in Proteins by Extracting Enhanced Position and Sequence Variant Feature via Chou's 5-Step Rule and …
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[36] A two-level computation model based on deep learning algorithm for identification of piRNA and their functions via Chou's 5-steps rule
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[37] Glioma stages prediction based on machine learning algorithm combined with protein-protein interaction networks
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[38] Proposing Pseudo Amino Acid Components is an Important Milestone for Proteome and Genome Analyses
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[39] Identifying DNase I hypersensitive sites using multi-features fusion and F-score features selection via Chou's 5-steps rule
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[40] MsDBP: Exploring DNA-Binding Proteins by Integrating Multiscale Sequence Information via Chou's Five-Step Rule
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[41] Advances in Electrochemistry for Monitoring Cellular Chemical Flux
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[42] Physicochemical n‐Grams Tool: A tool for protein physicochemical descriptor generation via Chou's 5‐steps rule
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[43] Prediction of lysine formylation sites using the composition of k-spaced amino acid pairs via Chou's 5-steps rule and general pseudo components
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[44] Identifying FL11 subtype by characterizing tumor immune microenvironment in prostate adenocarcinoma via Chou's 5-steps rule
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[45] 19F-NMR in Target-based Drug Discovery
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[46] MsDBP: Exploring DNA-binding Proteins by Integrating Multi-scale Sequence Information via Chou's 5-steps Rule
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[47] Impacts of pseudo amino acid components and 5-steps rule to proteomics and proteome analysis
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[48] Artificial intelligence (AI) tools constructed via the 5-steps rule for predicting post-translational modifications
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[49] A Study for Therapeutic Treatment against Parkinson's Disease via Chou's 5-steps Rule
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[50] Using Chou's general pseudo amino acid composition to classify laccases from bacterial and fungal sources via Chou's five-step rule
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[51] An insightful 20-year recollection since the birth of pseudo amino acid components
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[52] Calcium Pattern Assessment in Patients with Severe Aortic Stenosis Via the Chou's 5-Steps Rule
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[53] iMethylK-PseAAC: Improving Accuracy of Lysine Methylation Sites Identification by Incorporating Statistical Moments and Position Relative Features into General …
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[54] iSulfoTyr-PseAAC: Identify Tyrosine Sulfation Sites by Incorporating Statistical Moments via Chou's 5-steps Rule and Pseudo Components
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[55] Biological Production of (S)-acetoin: A State-of-the-Art Review
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[56] Using Chou's general PseAAC to analyze the evolutionary relationship of receptor associated proteins (RAP) with various folding patterns of protein domains
Journal of Theoretical Biology, 2018
[57] iRSpot-DTS: Predict recombination spots by incorporating the dinucleotide-based spare-cross covariance information into Chou's pseudo components
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[58] iRNA (m6A)-PseDNC: identifying N6-methyladenosine sites using pseudo dinucleotide composition
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[59] In silico identification of lipid-binding α helices of uncoupling protein 1
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[60] Large-scale frequent stem pattern mining in RNA families
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[61] Prediction of therapeutic peptides by incorporating q-Wiener index into Chou's general PseAAC
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[62] iRNA-PseColl: Identifying the Occurrence Sites of Different RNA Modifications by Incorporating Collective Effects of Nucleotides into PseKNC
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[63] A novel alignment-free method to classify protein folding types by combining spectral graph clustering with Chou's pseudo amino acid composition
Journal of Theoretical Biology, 2017
[64] Gene expression and in silico analysis of snakehead murrel interleukin 8 and antimicrobial activity of C-terminal derived peptide WS12
Veterinary Immunology and Immunopathology, 2017
[65] Sequence-based discrimination of protein-RNA interacting residues using a probabilistic approach
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[66] iRNAm5C-PseDNC: identifying RNA 5-methylcytosine sites by incorporating physical-chemical properties into pseudo dinucleotide composition
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[67] Metaheuristic Optimization for Parameter Estimation in Kinetic Models of Biological Systems-Recent Development and Future Direction
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[68] HIV-1 Nucleotide Sequence Comprehensive Analysis: A Computational Approach
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[69] iPreny-PseAAC: identify C-terminal cysteine prenylation sites in proteins by incorporating two tiers of sequence couplings into PseAAC
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[70] An unprecedented revolution in medicinal chemistry driven by the progress of biological science
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[71] Role of dopamine signaling in drug addiction
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[72] iKCR-PseENs: Identify lysine crotonylation sites in histone proteins with pseudo components and ensemble classifier
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[73] Microbial routes to (2R, 3R)-2, 3-butanediol: recent advances and future prospects
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[74] Computational prediction of therapeutic peptides based on graph index
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[75] Effects of ADAMTS14 genetic polymorphism and cigarette smoking on the clinicopathologic development of hepatocellular carcinoma
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[76] 2L-piRNA: A Two-Layer Ensemble Classifier for Identifying Piwi-Interacting RNAs and Their Function
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[77] iROS-gPseKNC: predicting replication origin sites in DNA by incorporating dinucleotide position-specific propensity into general pseudo nucleotide …
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[78] Classify vertebrate hemoglobin proteins by incorporating the evolutionary information into the general PseAAC with the hybrid approach
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[79] iACP: a sequence-based tool for identifying anticancer peptides
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[80] iCar-PseCp: identify carbonylation sites in proteins by Monte Carlo sampling and incorporating sequence coupled effects into general PseAAC
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[81] iOri-Human: identify human origin of replication by incorporating dinucleotide physicochemical properties into pseudo nucleotide composition.
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[82] iPhos-PseEn: identifying phosphorylation sites in proteins by fusing different pseudo components into an ensemble classifier
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[83] iRNA-PseU: Identifying RNA pseudouridine sites
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[84] An In Silico Approach to Investigation of the Source of Controversial Interpretations about Phenotypic Results of Human AhR-gene G1661A Polymorphism
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[86] Integration of multiple biological features yields high confidence human protein interactome
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[87] gDNA-Prot: Predict DNA-binding proteins by employing support vector machine and a novel numerical characterization of protein sequence
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[88] Protein sequence analysis by incorporating modified chaos game and physicochemical properties into Chou's general pseudo amino acid composition
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[89] Predicting protein structural classes based on complex networks and recurrence analysis
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[90] Characterization of BioPlex network by topological properties
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[91] Interplay between Catalysts and Substrates for Activity of Class Ib Aminoacyl-tRNA Synthetases and Implications for Pharmacology
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[92] Modulation of cytokine network in the comorbidity of schizophrenia and tuberculosis
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[93] Recent Progresses in Studying Helix-Helix Interactions in Proteins by Incorporating the Wenxiang Diagram into the NMR Spectroscopy
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[94] pSuc-Lys: Predict lysine succinylation sites in proteins with PseAAC and ensemble random forest approach
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[95] pRNAm-PC: Predicting N6-methyladenosine sites in RNA sequences via physical–chemical properties
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[96] iSuc-PseOpt: identifying lysine succinylation sites in proteins by incorporating sequence-coupling effects into pseudo components and optimizing imbalanced training …
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[97] iOri-Human: identify human origin of replication by incorporating dinucleotide physicochemical properties into pseudo nucleotide composition
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[98] Current progress in structural bioinformatics of protein-biomolecule interactions
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[100] iSuc-PseOpt: Identifying lysine succinylation sites in proteins by incorporating sequence-coupling effects into pseudo components and optimizing imbalanced training dataset
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[108] Editorial: current progress in structural bioinformatics of protein-biomolecule interactions.
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[113] current progress in structural bioinformatics of protein-biomolecule interactions.
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[130] < i> k-Partite cliques of protein interactions: A novel subgraph topology for functional coherence analysis on PPI networks
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[131] Graphic mapping of protein-coding DNA sequence in four-dimensional space and its application
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[132] A QSPR-like model for multilocus genotype networks of Fasciola hepatica in Northwest Spain
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[134] Review and research analysis of computational target methods using BioRuby and in silico screening of herbal lead compounds against pancreatic cancer using R …
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[135] The similarity/dissimilarity analysis of protein sequence based on nucleotide triplet codon
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[138] Peptide Design by Nature‐Inspired Algorithms
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[148] Signal propagation in protein interaction network during colorectal cancer progression
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[152] The protein–protein interaction network of the human Sirtuin family
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