Predicting Golgi-resident protein types using pseudo amino acid compositions: Approaches with positional specific physicochemical properties.

[1]  K. Chou,et al.  Prediction of protein structural classes. , 1995, Critical reviews in biochemistry and molecular biology.

[2]  Thomas L. Madden,et al.  Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. , 1997, Nucleic acids research.

[3]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

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

[5]  K. Chou.  Using subsite coupling to predict signal peptides. , 2001, Protein engineering.

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

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

[8]  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.

[9]  H.-B. Shen,et al.  Euk-PLoc: an ensemble classifier for large-scale eukaryotic protein subcellular location prediction , 2007, Amino Acids.

[10]  K. Chou,et al.  Virus-PLoc: a fusion classifier for predicting the subcellular localization of viral proteins within host and virus-infected cells. , 2007, Biopolymers.

[11]  Kuo-Chen Chou,et al.  Nuc-PLoc: a new web-server for predicting protein subnuclear localization by fusing PseAA composition and PsePSSM. , 2007, Protein engineering, design & selection : PEDS.

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

[13]  K. Chou,et al.  Knowledge-based computational intelligence development for predicting protein secondary structures from sequences , 2008, Expert review of proteomics.

[14]  Minoru Kanehisa,et al.  AAindex: amino acid index database, progress report 2008 , 2007, Nucleic Acids Res..

[15]  Cajo J. F. ter Braak,et al.  Predicting sub-Golgi localization of type II membrane proteins , 2008, Bioinform..

[16]  K. Chou,et al.  PseAAC: a flexible web server for generating various kinds of protein pseudo amino acid composition. , 2008, Analytical biochemistry.

[17]  Yanda Li,et al.  SubChlo: predicting protein subchloroplast locations with pseudo-amino acid composition and the evidence-theoretic K-nearest neighbor (ET-KNN) algorithm. , 2009, Journal of theoretical biology.

[18]  K. Chou.  Pseudo Amino Acid Composition and its Applications in Bioinformatics, Proteomics and System Biology , 2009 .

[19]  K. Chou,et al.  iLoc-Euk: A Multi-Label Classifier for Predicting the Subcellular Localization of Singleplex and Multiplex Eukaryotic Proteins , 2011, PloS one.

[20]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[21]  K. Chou.  Some remarks on protein attribute prediction and pseudo amino acid composition , 2010, Journal of Theoretical Biology.

[22]  Xin Wang,et al.  Recent progress in predicting protein sub-subcellular locations , 2011, Expert review of proteomics.

[23]  K. Chou,et al.  iLoc-Virus: a multi-label learning classifier for identifying the subcellular localization of virus proteins with both single and multiple sites. , 2011, Journal of theoretical biology.

[24]  K. Chou,et al.  iLoc-Plant: a multi-label classifier for predicting the subcellular localization of plant proteins with both single and multiple sites. , 2011, Molecular bioSystems.

[25]  Li Zhang,et al.  Identify submitochondria and subchloroplast locations with pseudo amino acid composition: approach from the strategy of discrete wavelet transform feature extraction. , 2011, Biochimica et biophysica acta.

[26]  Hui Ding,et al.  Identify Golgi protein types with modified Mahalanobis discriminant algorithm and pseudo amino acid composition. , 2011, Protein and peptide letters.

[27]  K. Chou,et al.  iLoc-Hum: using the accumulation-label scale to predict subcellular locations of human proteins with both single and multiple sites. , 2012, Molecular bioSystems.

[28]  K. Chou,et al.  iLoc-Gpos: a multi-layer classifier for predicting the subcellular localization of singleplex and multiplex Gram-positive bacterial proteins. , 2012, Protein and peptide letters.

[29]  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.

[30]  Jian Huang,et al.  Prediction of Golgi-resident protein types by using feature selection technique , 2013 .

[31]  Kuo-Chen Chou,et al.  Some remarks on predicting multi-label attributes in molecular biosystems. , 2013, Molecular bioSystems.

[32]  K. Chou,et al.  iLoc-Animal: a multi-label learning classifier for predicting subcellular localization of animal proteins. , 2013, Molecular bioSystems.

[33]  Wei Chen,et al.  Using Over-Represented Tetrapeptides to Predict Protein Submitochondria Locations , 2013, Acta Biotheoretica.

[34]  Yuan Yu,et al.  SubMito-PSPCP: Predicting Protein Submitochondrial Locations by Hybridizing Positional Specific Physicochemical Properties with Pseudoamino Acid Compositions , 2013, BioMed research international.

[35]  Wei Chen,et al.  iRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition , 2013, Nucleic acids research.

[36]  Dong-Sheng Cao,et al.  propy: a tool to generate various modes of Chou's PseAAC , 2013, Bioinform..

[37]  Thanaruk Theeramunkong,et al.  Predict Subcellular Locations of Singleplex and Multiplex Proteins by Semi-Supervised Learning and Dimension-Reducing General Mode of Chou's PseAAC , 2013, IEEE Transactions on NanoBioscience.

[38]  K. Chou,et al.  iSNO-AAPair: incorporating amino acid pairwise coupling into PseAAC for predicting cysteine S-nitrosylation sites in proteins , 2013, PeerJ.

[39]  K. Chou,et al.  iAMP-2L: a two-level multi-label classifier for identifying antimicrobial peptides and their functional types. , 2013, Analytical biochemistry.

[40]  Pufeng Du,et al.  Predicting multisite protein subcellular locations: progress and challenges , 2013, Expert review of proteomics.

[41]  K. Chou,et al.  PseKNC: a flexible web server for generating pseudo K-tuple nucleotide composition. , 2014, Analytical biochemistry.

[42]  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.

[43]  K. Chou,et al.  iCTX-Type: A Sequence-Based Predictor for Identifying the Types of Conotoxins in Targeting Ion Channels , 2014, BioMed research international.

[44]  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.

[45]  B. Liu,et al.  iDNA-Prot|dis: Identifying DNA-Binding Proteins by Incorporating Amino Acid Distance-Pairs and Reduced Alphabet Profile into the General Pseudo Amino Acid Composition , 2014, PloS one.

[46]  Wei Chen,et al.  iNuc-PseKNC: a sequence-based predictor for predicting nucleosome positioning in genomes with pseudo k-tuple nucleotide composition , 2014, Bioinform..

[47]  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.

[48]  K. Chou,et al.  iRNA-Methyl: Identifying N(6)-methyladenosine sites using pseudo nucleotide composition. , 2015, Analytical biochemistry.

[49]  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..

[50]  Xiaolong Wang,et al.  repRNA: a web server for generating various feature vectors of RNA sequences , 2015, Molecular Genetics and Genomics.

[51]  Wei Chen,et al.  PseKNC-General: a cross-platform package for generating various modes of pseudo nucleotide compositions , 2015, Bioinform..

[52]  B. Liu,et al.  Identification of microRNA precursor with the degenerate K-tuple or Kmer strategy. , 2015, Journal of theoretical biology.

[53]  B. Liu,et al.  Identification of Real MicroRNA Precursors with a Pseudo Structure Status Composition Approach , 2015, PloS one.

[54]  K. Chou.  Impacts of bioinformatics to medicinal chemistry. , 2015, Medicinal chemistry (Shariqah (United Arab Emirates)).

[55]  K. Chou,et al.  iDNA-Methyl: identifying DNA methylation sites via pseudo trinucleotide composition. , 2015, Analytical biochemistry.

[56]  Maqsood Hayat,et al.  Author ' s Accepted Manuscript Classification of membrane protein types using Voting feature interval in combination with Chou ' s pseudo amino acid composition , 2015 .

[57]  Qiuwen Zhang,et al.  MultiP-SChlo: Multi-label protein subchloroplast localization prediction , 2014, 2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[58]  K. Chou,et al.  Pseudo nucleotide composition or PseKNC: an effective formulation for analyzing genomic sequences. , 2015, Molecular bioSystems.

[59]  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..

[60]  James G. Lyons,et al.  Gram-positive and Gram-negative protein subcellular localization by incorporating evolutionary-based descriptors into Chou׳s general PseAAC. , 2015, Journal of theoretical biology.

[61]  B. Liu,et al.  PseDNA‐Pro: DNA‐Binding Protein Identification by Combining Chou’s PseAAC and Physicochemical Distance Transformation , 2015, Molecular informatics.

[62]  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.

[63]  Manish Kumar,et al.  Prediction of β-lactamase and its class by Chou's pseudo-amino acid composition and support vector machine. , 2015, Journal of theoretical biology.

[64]  Xiaolong Wang,et al.  iMiRNA-PseDPC: microRNA precursor identification with a pseudo distance-pair composition approach , 2016, Journal of biomolecular structure & dynamics.

[65]  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.