iPSW(2L)-PseKNC: A two-layer predictor for identifying promoters and their strength by hybrid features via pseudo K-tuple nucleotide composition.
暂无分享,去创建一个
K. Chou | X. Xiao | Zhaochun Xu | Wangren Qiu | Peng Wang | Hui Ge | Xuan Xiao
[1] Chou Kuo-Chen,et al. GRAPH THEORY OF ENZYME KINETICS I.STEADY-STATE REACTION SYSTEMS , 1979 .
[2] S. Forsén,et al. Graphical rules for enzyme-catalysed rate laws. , 1980, The Biochemical journal.
[3] G. Zhou,et al. An extension of Chou's graphic rules for deriving enzyme kinetic equations to systems involving parallel reaction pathways. , 1984, The Biochemical journal.
[4] K. Chou,et al. Low-frequency vibrations of DNA molecules. , 1984, The Biochemical journal.
[5] K. Chou,et al. Graphic rules in steady and non-steady state enzyme kinetics. , 1989, The Journal of biological chemistry.
[6] K. Chou,et al. A correlation-coefficient method to predicting protein-structural classes from amino acid compositions. , 1992, European journal of biochemistry.
[7] K. Chou,et al. An optimization approach to predicting protein structural class from amino acid composition , 1992, Protein science : a publication of the Protein Society.
[8] J. Chou,et al. Kinetic studies with the non-nucleoside HIV-1 reverse transcriptase inhibitor U-88204E. , 1993, Biochemistry.
[9] C. Zhang,et al. A joint prediction of the folding types of 1490 human proteins from their genetic codons. , 1993, Journal of theoretical biology.
[10] L. Resnick,et al. The quinoline U-78036 is a potent inhibitor of HIV-1 reverse transcriptase. , 1993, The Journal of biological chemistry.
[11] J. Chou,et al. A formulation for correlating properties of peptides and its application to predicting human immunodeficiency virus protease‐cleavable sites in proteins , 1993, Biopolymers.
[12] J. Chou,et al. Predicting cleavability of peptide sequences by HIV protease via correlation-angle approach , 1993, Journal of protein chemistry.
[13] K. Chou,et al. Prediction of protein structural classes. , 1995, Critical reviews in biochemistry and molecular biology.
[14] K. Chou. Prediction of protein cellular attributes using pseudo‐amino acid composition , 2001, Proteins.
[15] K. Chou. Prediction of signal peptides using scaled window , 2001, Peptides.
[16] Martin G. Reese,et al. Application of a Time-delay Neural Network to Promoter Annotation in the Drosophila Melanogaster Genome , 2001, Comput. Chem..
[17] K. Chou,et al. Using Functional Domain Composition and Support Vector Machines for Prediction of Protein Subcellular Location* , 2002, The Journal of Biological Chemistry.
[18] K. Chou,et al. Bioinformatical analysis of G-protein-coupled receptors. , 2002, Journal of proteome research.
[19] K. Chou,et al. Support vector machines for predicting membrane protein types by using functional domain composition. , 2003, Biophysical journal.
[20] Kuo-Chen Chou,et al. Nearest neighbour algorithm for predicting protein subcellular location by combining functional domain composition and pseudo-amino acid composition. , 2003, Biochemical and biophysical research communications.
[21] Kuo-Chen Chou,et al. Prediction and classification of protein subcellular location—sequence‐order effect and pseudo amino acid composition , 2003, Journal of cellular biochemistry.
[22] K.-C. Chou,et al. Using cellular automata to generate image representation for biological sequences , 2005, Amino Acids.
[23] Yvan Saeys,et al. Large-scale structural analysis of the core promoter in mammalian and plant genomes , 2005, Nucleic acids research.
[24] Kuo-Chen Chou,et al. Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes , 2005, Bioinform..
[25] R. Zhang,et al. Improving promoter prediction for the NNPP 2 . 2 algorithm : a case study using Escherichia coli DNA sequences , 2004 .
[26] K. Chou,et al. Prediction of linear B-cell epitopes using amino acid pair antigenicity scale , 2007, Amino Acids.
[27] K. Chou,et al. Using LogitBoost classifier to predict protein structural classes. , 2006, Journal of theoretical biology.
[28] Mark Goadrich,et al. The relationship between Precision-Recall and ROC curves , 2006, ICML.
[29] Hao Lin,et al. The recognition and prediction of σ70 promoters in Escherichia coli K-12 , 2006 .
[30] Tom Fawcett,et al. An introduction to ROC analysis , 2006, Pattern Recognit. Lett..
[31] Loris Nanni,et al. Genetic programming for creating Chou’s pseudo amino acid based features for submitochondria localization , 2008, Amino Acids.
[32] 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.
[33] Bhaskar D. Kulkarni,et al. Using pseudo amino acid composition to predict protein subnuclear localization: Approached with PSSM , 2007, Pattern Recognit. Lett..
[34] 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.
[35] K. Chou,et al. Recent progress in protein subcellular location prediction. , 2007, Analytical biochemistry.
[36] K. Chou,et al. Cell-PLoc: a package of Web servers for predicting subcellular localization of proteins in various organisms , 2008, Nature Protocols.
[37] Kuo-Chen Chou,et al. HIVcleave: a web-server for predicting human immunodeficiency virus protease cleavage sites in proteins. , 2008, Analytical biochemistry.
[38] Manju Bansal,et al. Relative stability of DNA as a generic criterion for promoter prediction: whole genome annotation of microbial genomes with varying nucleotide base composition. , 2009, Molecular bioSystems.
[39] Vivek K. Mutalik,et al. Promoter Strength Properties of the Complete Sigma E Regulon of Escherichia coli and Salmonella enterica , 2009, Journal of bacteriology.
[40] J. Nieto,et al. Use of fuzzy clustering technique and matrices to classify amino acids and its impact to Chou's pseudo amino acid composition. , 2009, Journal of theoretical biology.
[41] Lior Pachter,et al. Sequence Analysis , 2020, Definitions.
[42] K. Chou,et al. REVIEW : Recent advances in developing web-servers for predicting protein attributes , 2009 .
[43] K. Chou. Pseudo Amino Acid Composition and its Applications in Bioinformatics, Proteomics and System Biology , 2009 .
[44] K. Chou,et al. 2D-MH: A web-server for generating graphic representation of protein sequences based on the physicochemical properties of their constituent amino acids. , 2010, Journal of theoretical biology.
[45] Menglong Li,et al. SecretP: identifying bacterial secreted proteins by fusing new features into Chou's pseudo-amino acid composition. , 2010, Journal of theoretical biology.
[46] K. Chou,et al. Cell-PLoc 2.0: an improved package of web-servers for predicting subcellular localization of proteins in various organisms , 2010 .
[47] Loris Nanni,et al. Wavelet images and Chou’s pseudo amino acid composition for protein classification , 2011, Amino Acids.
[48] K. Chou. Some remarks on protein attribute prediction and pseudo amino acid composition , 2010, Journal of Theoretical Biology.
[49] Dongsheng Zou,et al. Supersecondary structure prediction using Chou's pseudo amino acid composition , 2011, J. Comput. Chem..
[50] A. Esmaeili,et al. Prediction of GABAA receptor proteins using the concept of Chou's pseudo-amino acid composition and support vector machine. , 2011, Journal of theoretical biology.
[51] Kuo-Chen Chou,et al. Predicting Functions of Proteins in Mouse Based on Weighted Protein-Protein Interaction Network and Protein Hybrid Properties , 2011, PloS one.
[52] T. Furey. ChIP – seq and beyond : new and improved methodologies to detect and characterize protein – DNA interactions , 2012 .
[53] Suyu Mei,et al. Multi-kernel transfer learning based on Chou's PseAAC formulation for protein submitochondria localization. , 2012, Journal of theoretical biology.
[54] Zhengwei Zhu,et al. CD-HIT: accelerated for clustering the next-generation sequencing data , 2012, Bioinform..
[55] Xin Wang,et al. PseAAC-Builder: a cross-platform stand-alone program for generating various special Chou's pseudo-amino acid compositions. , 2012, Analytical biochemistry.
[56] K. Song. Recognition of prokaryotic promoters based on a novel variable-window Z-curve method , 2011, Nucleic acids research.
[57] R. Aggarwal,et al. Prediction of essential proteins in prokaryotes by incorporating various physico-chemical features into the general form of Chou's pseudo amino acid composition. , 2013, Protein and peptide letters.
[58] Kuo-Chen Chou,et al. Some remarks on predicting multi-label attributes in molecular biosystems. , 2013, Molecular bioSystems.
[59] Jingqi Yuan,et al. A Multilabel Model Based on Chou’s Pseudo–Amino Acid Composition for Identifying Membrane Proteins with Both Single and Multiple Functional Types , 2013, The Journal of Membrane Biology.
[60] Wei Chen,et al. iRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition , 2013, Nucleic acids research.
[61] Dong-Sheng Cao,et al. propy: a tool to generate various modes of Chou's PseAAC , 2013, Bioinform..
[62] K. Chou,et al. iSNO-AAPair: incorporating amino acid pairwise coupling into PseAAC for predicting cysteine S-nitrosylation sites in proteins , 2013, PeerJ.
[63] K. Chou,et al. iHSP-PseRAAAC: Identifying the heat shock protein families using pseudo reduced amino acid alphabet composition. , 2013, Analytical biochemistry.
[64] Sukanta Mondal,et al. Chou's pseudo amino acid composition improves sequence-based antifreeze protein prediction. , 2014, Journal of theoretical biology.
[65] K. Chou,et al. iSS-PseDNC: Identifying Splicing Sites Using Pseudo Dinucleotide Composition , 2014, BioMed research international.
[66] K. Chou,et al. PseKNC: a flexible web server for generating pseudo K-tuple nucleotide composition. , 2014, Analytical biochemistry.
[67] Wei Chen,et al. iTIS-PseTNC: a sequence-based predictor for identifying translation initiation site in human genes using pseudo trinucleotide composition. , 2014, Analytical biochemistry.
[68] K. Chou,et al. iCTX-Type: A Sequence-Based Predictor for Identifying the Types of Conotoxins in Targeting Ion Channels , 2014, BioMed research international.
[69] K. Chou,et al. iMethyl-PseAAC: Identification of Protein Methylation Sites via a Pseudo Amino Acid Composition Approach , 2014, BioMed research international.
[70] Wei Chen,et al. iPro54-PseKNC: a sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition , 2014, Nucleic acids research.
[71] K. Chou,et al. iRSpot-TNCPseAAC: Identify Recombination Spots with Trinucleotide Composition and Pseudo Amino Acid Components , 2014, International journal of molecular sciences.
[72] Ujjwal Maulik,et al. Prediction of protein subcellular localization by incorporating multiobjective PSO-based feature subset selection into the general form of Chou’s PseAAC , 2015, Medical & Biological Engineering & Computing.
[73] K. Chou,et al. iHyd-PseAAC: Predicting Hydroxyproline and Hydroxylysine in Proteins by Incorporating Dipeptide Position-Specific Propensity into Pseudo Amino Acid Composition , 2014, International journal of molecular sciences.
[74] Maqsood Hayat,et al. Discriminating protein structure classes by incorporating Pseudo Average Chemical Shift to Chou's general PseAAC and Support Vector Machine , 2014, Comput. Methods Programs Biomed..
[75] Wei Chen,et al. iNuc-PseKNC: a sequence-based predictor for predicting nucleosome positioning in genomes with pseudo k-tuple nucleotide composition , 2014, Bioinform..
[76] Tahila Andrighetti,et al. DNA duplex stability as discriminative characteristic for Escherichia coli σ(54)- and σ(28)- dependent promoter sequences. , 2014, Biologicals : journal of the International Association of Biological Standardization.
[77] Kuo-Chen Chou,et al. iNR-Drug: Predicting the Interaction of Drugs with Nuclear Receptors in Cellular Networking , 2014, International journal of molecular sciences.
[78] K. Chou,et al. iRNA-Methyl: Identifying N(6)-methyladenosine sites using pseudo nucleotide composition. , 2015, Analytical biochemistry.
[79] Junjie Chen,et al. Pse-in-One: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences , 2015, Nucleic Acids Res..
[80] Xiaolong Wang,et al. repRNA: a web server for generating various feature vectors of RNA sequences , 2015, Molecular Genetics and Genomics.
[81] Wei Chen,et al. PseKNC-General: a cross-platform package for generating various modes of pseudo nucleotide compositions , 2015, Bioinform..
[82] K. Chou,et al. iUbiq-Lys: prediction of lysine ubiquitination sites in proteins by extracting sequence evolution information via a gray system model , 2015, Journal of biomolecular structure & dynamics.
[83] Xiang Cheng,et al. iDrug-Target: predicting the interactions between drug compounds and target proteins in cellular networking via benchmark dataset optimization approach , 2015, Journal of biomolecular structure & dynamics.
[84] B. Liu,et al. Identification of microRNA precursor with the degenerate K-tuple or Kmer strategy. , 2015, Journal of theoretical biology.
[85] Xiaolong Wang,et al. Identification of DNA-binding proteins by incorporating evolutionary information into pseudo amino acid composition via the top-n-gram approach , 2015, Journal of biomolecular structure & dynamics.
[86] K. Chou. Impacts of bioinformatics to medicinal chemistry. , 2015, Medicinal chemistry (Shariqah (United Arab Emirates)).
[87] Maqsood Hayat,et al. Prediction of Protein Submitochondrial Locations by Incorporating Dipeptide Composition into Chou’s General Pseudo Amino Acid Composition , 2016, The Journal of Membrane Biology.
[88] K. Chou,et al. iDNA-Methyl: identifying DNA methylation sites via pseudo trinucleotide composition. , 2015, Analytical biochemistry.
[89] K. Chou,et al. Pseudo nucleotide composition or PseKNC: an effective formulation for analyzing genomic sequences. , 2015, Molecular bioSystems.
[90] Xiaolong Wang,et al. repDNA: a Python package to generate various modes of feature vectors for DNA sequences by incorporating user-defined physicochemical properties and sequence-order effects , 2015, Bioinform..
[91] Kuo-Chen Chou,et al. iPPI-Esml: An ensemble classifier for identifying the interactions of proteins by incorporating their physicochemical properties and wavelet transforms into PseAAC. , 2015, Journal of theoretical biology.
[92] Wei Chen,et al. iRNA-PseU: Identifying RNA pseudouridine sites , 2016, Molecular therapy. Nucleic acids.
[93] Kuo-Chen Chou,et al. iHyd-PseCp: Identify hydroxyproline and hydroxylysine in proteins by incorporating sequence-coupled effects into general PseAAC , 2016, Oncotarget.
[94] H. Mohabatkar,et al. Analysis and comparison of lignin peroxidases between fungi and bacteria using three different modes of Chou's general pseudo amino acid composition. , 2016, Journal of theoretical biology.
[95] Ren Long,et al. iDHS-EL: identifying DNase I hypersensitive sites by fusing three different modes of pseudo nucleotide composition into an ensemble learning framework , 2016, Bioinform..
[96] K. Chou,et al. pRNAm-PC: Predicting N(6)-methyladenosine sites in RNA sequences via physical-chemical properties. , 2016, Analytical biochemistry.
[97] Kuo-Chen Chou,et al. iPTM-mLys: identifying multiple lysine PTM sites and their different types , 2016, Bioinform..
[98] Wei Chen,et al. iOri-Human: identify human origin of replication by incorporating dinucleotide physicochemical properties into pseudo nucleotide composition , 2016, Oncotarget.
[99] Kuo-Chen Chou,et al. iPPBS-Opt: A Sequence-Based Ensemble Classifier for Identifying Protein-Protein Binding Sites by Optimizing Imbalanced Training Datasets , 2016, Molecules.
[100] Fabio Rinaldi,et al. RegulonDB version 9.0: high-level integration of gene regulation, coexpression, motif clustering and beyond , 2015, Nucleic Acids Res..
[101] Wei Chen,et al. Identifying 2'-O-methylationation sites by integrating nucleotide chemical properties and nucleotide compositions. , 2016, Genomics.
[102] Ren Long,et al. iEnhancer-2L: a two-layer predictor for identifying enhancers and their strength by pseudo k-tuple nucleotide composition , 2016, Bioinform..
[103] Kuo-Chen Chou,et al. iSuc-PseOpt: Identifying lysine succinylation sites in proteins by incorporating sequence-coupling effects into pseudo components and optimizing imbalanced training dataset. , 2016, Analytical biochemistry.
[104] Hui Ding,et al. Using deformation energy to analyze nucleosome positioning in genomes. , 2016, Genomics.
[105] Kuo-Chen Chou,et al. Identification of protein-protein binding sites by incorporating the physicochemical properties and stationary wavelet transforms into pseudo amino acid composition , 2016, Journal of biomolecular structure & dynamics.
[106] Kuo-Chen Chou,et al. pSuc-Lys: Predict lysine succinylation sites in proteins with PseAAC and ensemble random forest approach. , 2016, Journal of theoretical biology.
[107] K. Chou,et al. iCar-PseCp: identify carbonylation sites in proteins by Monte Carlo sampling and incorporating sequence coupled effects into general PseAAC , 2016, Oncotarget.
[108] Kuo-Chen Chou,et al. pSumo-CD: predicting sumoylation sites in proteins with covariance discriminant algorithm by incorporating sequence-coupled effects into general PseAAC , 2016, Bioinform..
[109] K. Chou,et al. iACP: a sequence-based tool for identifying anticancer peptides , 2016, Oncotarget.
[110] Pooja Tripathi,et al. A novel alignment-free method to classify protein folding types by combining spectral graph clustering with Chou's pseudo amino acid composition. , 2017, Journal of theoretical biology.
[111] Kuo-Chen Chou,et al. 2L-piRNA: A Two-Layer Ensemble Classifier for Identifying Piwi-Interacting RNAs and Their Function , 2017, Molecular therapy. Nucleic acids.
[112] Kuo-Chen Chou,et al. iPreny-PseAAC: Identify C-terminal Cysteine Prenylation Sites in Proteins by Incorporating Two Tiers of Sequence Couplings into PseAAC. , 2017, Medicinal chemistry (Shariqah (United Arab Emirates)).
[113] Kuo-Chen Chou,et al. An Unprecedented Revolution in Medicinal Chemistry Driven by the Progress of Biological Science. , 2017, Current topics in medicinal chemistry.
[114] Kuo-Chen Chou,et al. iRNAm5C-PseDNC: identifying RNA 5-methylcytosine sites by incorporating physical-chemical properties into pseudo dinucleotide composition , 2017, Oncotarget.
[115] Ren Long,et al. iRSpot-EL: identify recombination spots with an ensemble learning approach , 2017, Bioinform..
[116] Prabina Kumar Meher,et al. Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou’s general PseAAC , 2017, Scientific Reports.
[117] S. Khan,et al. Unb-DPC: Identify mycobacterial membrane protein types by incorporating un-biased dipeptide composition into Chou's general PseAAC. , 2017, Journal of theoretical biology.
[118] Geoffrey I. Webb,et al. POSSUM: a bioinformatics toolkit for generating numerical sequence feature descriptors based on PSSM profiles , 2017, Bioinform..
[119] Kuo-Chen Chou,et al. iATC-mISF: a multi-label classifier for predicting the classes of anatomical therapeutic chemicals , 2017, Bioinform..
[120] Kuo-Chen Chou,et al. pLoc-mGneg: Predict subcellular localization of Gram-negative bacterial proteins by deep gene ontology learning via general PseAAC. , 2017, Genomics.
[121] Vladimir B. Bajic,et al. bTSSfinder: a novel tool for the prediction of promoters in cyanobacteria and Escherichia coli , 2016, Bioinform..
[122] Bin Liu,et al. Pse-in-One 2.0: An Improved Package of Web Servers for Generating Various Modes of Pseudo Components of DNA, RNA, and Protein Sequences , 2017 .
[123] Kuo-Chen Chou,et al. pLoc-mGpos: Incorporate Key Gene Ontology Information into General PseAAC for Predicting Subcellular Localization of Gram-Positive Bacterial Proteins , 2017 .
[124] Muhammad Tahir,et al. Sequence based predictor for discrimination of enhancer and their types by applying general form of Chou's trinucleotide composition , 2017, Comput. Methods Programs Biomed..
[125] K. Chou,et al. iPGK-PseAAC: Identify Lysine Phosphoglycerylation Sites in Proteins by Incorporating Four Different Tiers of Amino Acid Pairwise Coupling Information into the General PseAAC. , 2017, Medicinal chemistry (Shariqah (United Arab Emirates)).
[126] Kuo-Chen Chou,et al. pLoc‐mAnimal: predict subcellular localization of animal proteins with both single and multiple sites , 2017, Bioinform..
[127] Wei Chen,et al. iRNA-AI: identifying the adenosine to inosine editing sites in RNA sequences , 2016, Oncotarget.
[128] Kuo-Chen Chou,et al. pLoc-mPlant: predict subcellular localization of multi-location plant proteins by incorporating the optimal GO information into general PseAAC. , 2017, Molecular bioSystems.
[129] Kuo-Chen Chou,et al. iRNA-2methyl: Identify RNA 2'-O-methylation Sites by Incorporating Sequence-Coupled Effects into General PseKNC and Ensemble Classifier. , 2017, Medicinal chemistry (Shariqah (United Arab Emirates)).
[130] M. Bakhtiarizadeh,et al. OOgenesis_Pred: A sequence-based method for predicting oogenesis proteins by six different modes of Chou's pseudo amino acid composition. , 2017, Journal of theoretical biology.
[131] K. Chou,et al. iRNA-PseColl: Identifying the Occurrence Sites of Different RNA Modifications by Incorporating Collective Effects of Nucleotides into PseKNC , 2017, Molecular therapy. Nucleic acids.
[132] Kuo-Chen Chou,et al. pLoc-mVirus: Predict subcellular localization of multi-location virus proteins via incorporating the optimal GO information into general PseAAC. , 2017, Gene.
[133] Kuo-Chen Chou,et al. iATC-mHyb: a hybrid multi-label classifier for predicting the classification of anatomical therapeutic chemicals , 2017, Oncotarget.
[134] Liang Kong,et al. iRSpot-ADPM: Identify recombination spots by incorporating the associated dinucleotide product model into Chou's pseudo components. , 2018, Journal of theoretical biology.
[135] E Siva Sankari,et al. Predicting membrane protein types by incorporating a novel feature set into Chou's general PseAAC. , 2018, Journal of theoretical biology.
[136] Kuo-Chen Chou,et al. pLoc_bal-mGpos: Predict subcellular localization of Gram-positive bacterial proteins by quasi-balancing training dataset and PseAAC. , 2019, Genomics.
[137] Shahid Akbar,et al. iMethyl-STTNC: Identification of N6-methyladenosine sites by extending the idea of SAAC into Chou's PseAAC to formulate RNA sequences. , 2018, Journal of theoretical biology.
[138] Ernesto Contreras-Torres,et al. Predicting structural classes of proteins by incorporating their global and local physicochemical and conformational properties into general Chou's PseAAC. , 2018, Journal of theoretical biology.
[139] Gholamreza Haffari,et al. PREvaIL, an integrative approach for inferring catalytic residues using sequence, structural, and network features in a machine-learning framework. , 2018, Journal of theoretical biology.
[140] Shengli Zhang,et al. Identify Gram-negative bacterial secreted protein types by incorporating different modes of PSSM into Chou's general PseAAC via Kullback-Leibler divergence. , 2018, Journal of theoretical biology.
[141] Gholamreza Haffari,et al. PROSPERous: high-throughput prediction of substrate cleavage sites for 90 proteases with improved accuracy , 2018, Bioinform..
[142] Mohammad Sohel Rahman,et al. DPP-PseAAC: A DNA-binding protein prediction model using Chou's general PseAAC. , 2018, Journal of theoretical biology.
[143] K. Chou,et al. iRNA-3typeA: Identifying Three Types of Modification at RNA’s Adenosine Sites , 2018, Molecular therapy. Nucleic acids.
[144] Swakkhar Shatabda,et al. iRSpot-SF: Prediction of recombination hotspots by incorporating sequence based features into Chou's Pseudo components. , 2019, Genomics.
[145] Zahoor Jan,et al. iMem-2LSAAC: A two-level model for discrimination of membrane proteins and their types by extending the notion of SAAC into chou's pseudo amino acid composition. , 2018, Journal of theoretical biology.
[146] Jiangning Song,et al. Quokka: a comprehensive tool for rapid and accurate prediction of kinase family‐specific phosphorylation sites in the human proteome , 2018, Bioinform..
[147] Hui Ding,et al. iRNA(m6A)-PseDNC: Identifying N6-methyladenosine sites using pseudo dinucleotide composition. , 2018, Analytical biochemistry.
[148] Juan Mei,et al. Prediction of HIV-1 and HIV-2 proteins by using Chou’s pseudo amino acid compositions and different classifiers , 2018, Scientific Reports.
[149] Yi Fu,et al. Analysis and prediction of ion channel inhibitors by using feature selection and Chou's general pseudo amino acid composition. , 2018, Journal of theoretical biology.
[150] Kuo-Chen Chou,et al. iRSpot-Pse6NC: Identifying recombination spots in Saccharomyces cerevisiae by incorporating hexamer composition into general PseKNC , 2018, International journal of biological sciences.
[151] Shengli Zhang,et al. Prediction of protein subcellular localization with oversampling approach and Chou's general PseAAC. , 2018, Journal of theoretical biology.
[152] Juan Mei,et al. Analysis and prediction of presynaptic and postsynaptic neurotoxins by Chou's general pseudo amino acid composition and motif features. , 2018, Journal of theoretical biology.
[153] Maqsood Hayat,et al. Predicting subcellular localization of multi-label proteins by incorporating the sequence features into Chou's PseAAC. , 2019, Genomics.
[154] Kuo-Chen Chou,et al. pLoc_bal-mHum: Predict subcellular localization of human proteins by PseAAC and quasi-balancing training dataset. , 2019, Genomics.
[155] Geoffrey I. Webb,et al. iFeature: a Python package and web server for features extraction and selection from protein and peptide sequences , 2018, Bioinform..
[156] Kuo-Chen Chou,et al. pLoc_bal-mGneg: Predict subcellular localization of Gram-negative bacterial proteins by quasi-balancing training dataset and general PseAAC. , 2018, Journal of theoretical biology.
[157] S. Muthu Krishnan,et al. Using Chou's general PseAAC to analyze the evolutionary relationship of receptor associated proteins (RAP) with various folding patterns of protein domains. , 2018 .
[158] Dong Wang,et al. iLoc‐lncRNA: predict the subcellular location of lncRNAs by incorporating octamer composition into general PseKNC , 2018, Bioinform..
[159] Sher Afzal Khan,et al. iNuc-ext-PseTNC: an efficient ensemble model for identification of nucleosome positioning by extending the concept of Chou’s PseAAC to pseudo-tri-nucleotide composition , 2018, Molecular Genetics and Genomics.
[160] K. Chou,et al. pLoc-mEuk: Predict subcellular localization of multi-label eukaryotic proteins by extracting the key GO information into general PseAAC. , 2018, Genomics.
[161] De-Shuang Huang,et al. iRO-3wPseKNC: identify DNA replication origins by three-window-based PseKNC , 2018, Bioinform..
[162] Mukhtaj Khan,et al. Identifying 5-methylcytosine sites in RNA sequence using composite encoding feature into Chou's PseKNC. , 2018, Journal of theoretical biology.
[163] Fan Yang,et al. iPromoter-2L: a two-layer predictor for identifying promoters and their types by multi-window-based PseKNC , 2018, Bioinform..
[164] Jiangning Song,et al. Bastion6: a bioinformatics approach for accurate prediction of type VI secreted effectors , 2018, Bioinform..
[165] Kuo-Chen Chou,et al. pLoc-mHum: predict subcellular localization of multi-location human proteins via general PseAAC to winnow out the crucial GO information , 2018, Bioinform..
[166] Fuquan Zhang,et al. Implications of Newly Identified Brain eQTL Genes and Their Interactors in Schizophrenia , 2018, Molecular therapy. Nucleic acids.
[167] K. Chou,et al. iDNA6mA-PseKNC: Identifying DNA N6-methyladenosine sites by incorporating nucleotide physicochemical properties into PseKNC. , 2018, Genomics.
[168] Swakkhar Shatabda,et al. Effective DNA binding protein prediction by using key features via Chou's general PseAAC. , 2019, Journal of theoretical biology.
[169] Gholamreza Haffari,et al. Twenty years of bioinformatics research for protease-specific substrate and cleavage site prediction: a comprehensive revisit and benchmarking of existing methods , 2018, Briefings Bioinform..
[170] Liang Kong,et al. iRSpot-PDI: Identification of recombination spots by incorporating dinucleotide property diversity information into Chou's pseudo components. , 2019, Genomics.
[171] Geoffrey I. Webb,et al. Computational analysis and prediction of lysine malonylation sites by exploiting informative features in an integrative machine-learning framework , 2018, Briefings Bioinform..
[172] Kuo-Chen Chou,et al. iPPI-PseAAC(CGR): Identify protein-protein interactions by incorporating chaos game representation into PseAAC. , 2019, Journal of theoretical biology.
[173] Geoffrey I. Webb,et al. iProt-Sub: a comprehensive package for accurately mapping and predicting protease-specific substrates and cleavage sites , 2018, Briefings Bioinform..
[174] Kuo-Chen Chou,et al. pLoc_bal‐mAnimal: predict subcellular localization of animal proteins by balancing training dataset and PseAAC , 2018, Bioinform..