"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] cACP: Classifying anticancer peptides using discriminative intelligent model via Chou's 5-step rules and general pseudo components
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[3] JOURNAL OF MATHEMATICS, STATISTICS AND COMPUTING
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[4] An alignment-free measure based on physicochemical properties of amino acids for protein sequence comparison
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[5] VGSC2: Second generation vector graph toolkit of genome synteny and collinearity
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[6] Advance in Predicting Subcellular Localization of Multi-label Proteins and its Implication for Developing Multi-target Drugs
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[7] iRSpot-SPI: Deep learning-based recombination spots prediction by incorporating secondary sequence information coupled with physio-chemical properties via …
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[8] Identification and characterization of WD40 superfamily genes in peach
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[9] Bioimage-based Prediction of Protein Subcellular Location in Human Tissue with Ensemble Features and Deep Networks
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[10] 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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[11] Established and In-trial GPCR Families in Clinical Trials: A Review for Target Selection
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[12] 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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[13] Progresses in predicting post-translational modification
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[14] 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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[15] 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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[16] Glioma stages prediction based on machine learning algorithm combined with protein-protein interaction networks
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[17] Proposing Pseudo Amino Acid Components is an Important Milestone for Proteome and Genome Analyses
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[18] Identifying DNase I hypersensitive sites using multi-features fusion and F-score features selection via Chou's 5-steps rule
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[19] MsDBP: Exploring DNA-Binding Proteins by Integrating Multiscale Sequence Information via Chou's Five-Step Rule
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[20] Advances in Electrochemistry for Monitoring Cellular Chemical Flux
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[21] Physicochemical n‐Grams Tool: A tool for protein physicochemical descriptor generation via Chou's 5‐steps rule
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[22] 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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[23] Identifying FL11 subtype by characterizing tumor immune microenvironment in prostate adenocarcinoma via Chou's 5-steps rule
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[24] 19F-NMR in Target-based Drug Discovery
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[25] MsDBP: Exploring DNA-binding Proteins by Integrating Multi-scale Sequence Information via Chou's 5-steps Rule
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[26] Impacts of pseudo amino acid components and 5-steps rule to proteomics and proteome analysis
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[27] Artificial intelligence (AI) tools constructed via the 5-steps rule for predicting post-translational modifications
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[28] A Study for Therapeutic Treatment against Parkinson's Disease via Chou's 5-steps Rule
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[29] 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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[30] An insightful 20-year recollection since the birth of pseudo amino acid components
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[31] Calcium Pattern Assessment in Patients with Severe Aortic Stenosis Via the Chou's 5-Steps Rule
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[32] iMethylK-PseAAC: Improving Accuracy of Lysine Methylation Sites Identification by Incorporating Statistical Moments and Position Relative Features into General …
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[33] iSulfoTyr-PseAAC: Identify Tyrosine Sulfation Sites by Incorporating Statistical Moments via Chou's 5-steps Rule and Pseudo Components
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[34] Biological Production of (S)-acetoin: A State-of-the-Art Review
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[35] Using Chou's general PseAAC to analyze the evolutionary relationship of receptor associated proteins (RAP) with various folding patterns of protein domains
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[36] iRSpot-DTS: Predict recombination spots by incorporating the dinucleotide-based spare-cross covariance information into Chou's pseudo components
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[37] iRNA (m6A)-PseDNC: identifying N6-methyladenosine sites using pseudo dinucleotide composition
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[38] In silico identification of lipid-binding α helices of uncoupling protein 1
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[39] Large-scale frequent stem pattern mining in RNA families
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[40] Prediction of therapeutic peptides by incorporating q-Wiener index into Chou's general PseAAC
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[41] iRNA-PseColl: Identifying the Occurrence Sites of Different RNA Modifications by Incorporating Collective Effects of Nucleotides into PseKNC
Molecular Therapy - Nucleic Acids, 2017
[42] A novel alignment-free method to classify protein folding types by combining spectral graph clustering with Chou's pseudo amino acid composition
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[43] Gene expression and in silico analysis of snakehead murrel interleukin 8 and antimicrobial activity of C-terminal derived peptide WS12
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[44] Sequence-based discrimination of protein-RNA interacting residues using a probabilistic approach
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[45] iRNAm5C-PseDNC: identifying RNA 5-methylcytosine sites by incorporating physical-chemical properties into pseudo dinucleotide composition
Oncotarget, 2017
[46] Metaheuristic Optimization for Parameter Estimation in Kinetic Models of Biological Systems-Recent Development and Future Direction
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[47] HIV-1 Nucleotide Sequence Comprehensive Analysis: A Computational Approach
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[48] iPreny-PseAAC: identify C-terminal cysteine prenylation sites in proteins by incorporating two tiers of sequence couplings into PseAAC
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[49] An unprecedented revolution in medicinal chemistry driven by the progress of biological science
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[50] Role of dopamine signaling in drug addiction
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[51] iKCR-PseENs: Identify lysine crotonylation sites in histone proteins with pseudo components and ensemble classifier
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[52] Microbial routes to (2R, 3R)-2, 3-butanediol: recent advances and future prospects
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[53] Computational prediction of therapeutic peptides based on graph index
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[54] Effects of ADAMTS14 genetic polymorphism and cigarette smoking on the clinicopathologic development of hepatocellular carcinoma
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[55] 2L-piRNA: A Two-Layer Ensemble Classifier for Identifying Piwi-Interacting RNAs and Their Function
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[56] iROS-gPseKNC: predicting replication origin sites in DNA by incorporating dinucleotide position-specific propensity into general pseudo nucleotide …
Oncotarget, 2016
[57] Classify vertebrate hemoglobin proteins by incorporating the evolutionary information into the general PseAAC with the hybrid approach
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[58] iACP: a sequence-based tool for identifying anticancer peptides
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[59] iCar-PseCp: identify carbonylation sites in proteins by Monte Carlo sampling and incorporating sequence coupled effects into general PseAAC
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[60] iOri-Human: identify human origin of replication by incorporating dinucleotide physicochemical properties into pseudo nucleotide composition.
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[61] iPhos-PseEn: identifying phosphorylation sites in proteins by fusing different pseudo components into an ensemble classifier
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[62] iRNA-PseU: Identifying RNA pseudouridine sites
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[63] 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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[64] tRNAfeature: An Algorithm for tRNA Features to Identify tRNA Genes in DNA Sequences
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[65] Integration of multiple biological features yields high confidence human protein interactome
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[66] gDNA-Prot: Predict DNA-binding proteins by employing support vector machine and a novel numerical characterization of protein sequence
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[67] Protein sequence analysis by incorporating modified chaos game and physicochemical properties into Chou's general pseudo amino acid composition
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[68] Predicting protein structural classes based on complex networks and recurrence analysis
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[69] Characterization of BioPlex network by topological properties
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[70] Interplay between Catalysts and Substrates for Activity of Class Ib Aminoacyl-tRNA Synthetases and Implications for Pharmacology
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[71] Modulation of cytokine network in the comorbidity of schizophrenia and tuberculosis
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[72] Recent Progresses in Studying Helix-Helix Interactions in Proteins by Incorporating the Wenxiang Diagram into the NMR Spectroscopy
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[73] pSuc-Lys: Predict lysine succinylation sites in proteins with PseAAC and ensemble random forest approach
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[74] pRNAm-PC: Predicting N6-methyladenosine sites in RNA sequences via physical–chemical properties
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[75] iSuc-PseOpt: identifying lysine succinylation sites in proteins by incorporating sequence-coupling effects into pseudo components and optimizing imbalanced training …
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[76] iOri-Human: identify human origin of replication by incorporating dinucleotide physicochemical properties into pseudo nucleotide composition
Oncotarget, 2016
[77] pRNAm-PC: Predicting N 6-methyladenosine sites in RNA sequences via physical–chemical properties
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[78] 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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[79] fabp4 is central to eight obesity associated genes: A functional gene network-based polymorphic study
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[80] Identification of Real MicroRNA Precursors with a Pseudo Structure Status Composition Approach.
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[81] Novel 3D Bio-Macromolecular Bilinear Descriptors for Protein Science: Predicting Protein Structural Classes
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[82] Editorial (Thematic Issue: Current Progress in Structural Bioinformatics of Protein-Biomolecule Interactions)
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[83] Impacts of bioinformatics to medicinal chemistry
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[84] iPPI-Esml: An ensemble classifier for identifying the interactions of proteins by incorporating their physicochemical properties and wavelet transforms into PseAAC
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[85] Some Remarks on Prediction of Protein-Protein Interaction with Machine Learning
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[86] Editorial: current progress in structural bioinformatics of protein-biomolecule interactions.
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[88] Advances in Protein Contact Map Prediction Based on Machine Learning
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[89] Recent Development of Peptide Drugs and Advance on Theory and Methodology of Peptide Inhibitor Design
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[90] Identification of real microRNA precursors with a pseudo structure status composition approach
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[91] current progress in structural bioinformatics of protein-biomolecule interactions.
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[93] A novel k-word relative measure for sequence comparison
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[94] Apolipoprotein E Gene Variants of Alzheimer's Disease and Vascular Dementia Patients in a Community Population of Nanking
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[95] Research/Review: Insights into the Mutation-Induced Dysfunction of Arachidonic Acid Metabolism from Modeling of Human CYP2J2
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[96] Research/Review: Structure and Linkage Disequilibrium Analysis of Adamantane Resistant Mutations in Influenza Virus M2 Proton Channel
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[101] The similarity/dissimilarity analysis of protein sequence based on nucleotide triplet codon.
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[102] Characterization of a DNA exit gate in the human cohesin ring
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[103] Review and Research Analysis of Computational Target Methods Using BioRuby and In silico Screening of Herbal Lead Compounds Against Pancreatic Cancer Using R Programming
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[108] < i> k-Partite cliques of protein interactions: A novel subgraph topology for functional coherence analysis on PPI networks
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[109] Graphic mapping of protein-coding DNA sequence in four-dimensional space and its application
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[111] k-Partite cliques of protein interactions: A novel subgraph topology for functional coherence analysis on PPI networks
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[112] 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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[113] The similarity/dissimilarity analysis of protein sequence based on nucleotide triplet codon
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[114] A simple k-word interval method for phylogenetic analysis of DNA sequences
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[115] Linear regression model of short k-word: a similarity distance suitable for biological sequences with various lengths
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[116] Peptide Design by Nature‐Inspired Algorithms
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[117] Feature identification and reduction for improved generalization accuracy in secondary-structure prediction
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[125] iAMP-2L: a two-level multi-label classifier for identifying antimicrobial peptides and their functional types
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[126] Signal propagation in protein interaction network during colorectal cancer progression
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[130] The protein–protein interaction network of the human Sirtuin family
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[139] Protein-Protein Networks Construction and Their Relevance Measurement Based on Multi-Epitope-Ligand-Kartographie and Gene Ontology Data of T-Cell Surface Proteins for Polymyositis
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