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K.C. Chou, Gordon Life Science Institute: Its philosophy, achievements, and perspective, Annals of Cancer Therapy and Pharmacology https://onomyscience.com/onomy/cancer_archive_volume2_issue2.html 2(2019) 001-026.
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K.C. Chou, Intriguing Story about the Birth of Gordon Life Science Institute and its Development and Driving Force, J Retro Virol Anti Retro Virol, 1 (2019) 180002.
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K.C. Chou, Gordon Life Science Institute and Its Impacts on Computational Biology and Drug Development, Natural Science, 12 (2020) 125-161.
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L. Nanni, A. Lumini, Genetic programming for creating Chou’s pseudo amino acid based features for submitochondria localization, Amino Acids, 34 (2008) 653-660.
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B. Liao, Q. Xiang, D. Li, Incorporating Secondary Features into the General form of Chou’s PseAAC for Predicting Protein Structural Class, Protein & Peptide Letters, 19 (2012) 1133-1138.
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X.W. Zhao, X.T. Li, Z.Q. Ma, M.H. Yin, Identify DNA-Binding Proteins with Optimal Chou’s Amino Acid Composition, Protein & Peptide Letters, 19 (2012) 398-405.
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L. Nanni, A. Lumini, D. Gupta, A. Garg, Identifying bacterial virulent proteins by fusing a set of classifiers based on variants of Chou’s pseudo amino acid composition and on evolutionary information, IEEE-ACM Transaction on Computational Biolology and Bioinformatics, 9 (2012) 467-475.
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Zia-ur-Rehman, A. Khan, Identifying GPCRs and their Types with Chou’s Pseudo Amino Acid Composition: An Approach from Multi-scale Energy Representation and Position Specific Scoring Matrix, Protein & Peptide Letters, 19 (2012) 890-903.
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X.Y. Sun, S.P. Shi, J.D. Qiu, S.B. Suo, S.Y. Huang, R.P. Liang, Identifying protein quaternary structural attributes by incorporating physicochemical properties into the general form of Chou’s PseAAC via discrete wavelet transform, Molecular BioSystems, 8 (2012) 3178-3184.
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C. Chen, Z.B. Shen, X.Y. Zou, Dual-Layer Wavelet SVM for Predicting Protein Structural Class Via the General Form of Chou’s Pseudo Amino Acid Composition, Protein & Peptide Letters, 19 (2012) 422-429.
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S. Wan, M.W. Mak, S.Y. Kung, GOASVM: A subcellular location predictor by incorporating term-frequency gene ontology into the general form of Chou’s pseudo amino acid composition, J. Theor. Biol., 323 (2013) 40-48.
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T.H. Chang, L.C. Wu, T.Y. Lee, S.P. Chen, H.D. Huang, J.T. Horng, EuLoc: a web-server for accurately predict protein subcellular localization in eukaryotes by incorporating various features of sequence segments into the general form of Chou’s PseAAC, Journal of Computer-Aided Molecular Design, 27 (2013) 91-103.
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M.K. Gupta, R. Niyogi, M. Misra, An alignment-free method to find similarity among protein sequences via the general form of Chou’s pseudo amino acid composition, SAR QSAR Environ Res, 24 (2013) 597-609.
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L. Kong, L. Zhang, J. Lv, Accurate prediction of protein structural classes by incorporating predicted secondary structure information into the general form of Chou’s pseudo amino acid composition, J. Theor. Biol., 344 (2014) 12-18.
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M. Hayat, N. Iqbal, Discriminating protein structure classes by incorporating Pseudo Average Chemical Shift to Chou’s general PseAAC and Support Vector Machine, Computer methods and programs in biomedicine, 116 (2014) 184-192.
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S. Ahmad, M. Kabir, M. Hayat, Identification of Heat Shock Protein families and J-protein types by incorporating Dipeptide Composition into Chou’s general PseAAC, Computer methods and programs in biomedicine, 122 (2015) 165-174.
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A. Dehzangi, R. Heffernan, A. Sharma, J. Lyons, K. Paliwal, A. Sattar, Gram-positive and Gram-negative protein subcellular localization by incorporating evolutionary-based descriptors into Chou’s general PseAAC, J. Theor. Biol., 364 (2015) 284-294.
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R. Sharma, A. Dehzangi, J. Lyons, K. Paliwal, T. Tsunoda, A. Sharma, Predict Gram-Positive and Gram-Negative Subcellular Localization via Incorporating Evolutionary Information and Physicochemical Features Into Chou’s General PseAAC, IEEE Trans Nanobioscience, 14 (2015) 915-926.
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M. Zhang, B. Zhao, X. Liu, Predicting industrial polymer melt index via incorporating chaotic characters into Chou’s general PseAAC, Chemometrics and Intelligent Laboratory Systems (CHEMOLAB), 146 (2015) 232-240.
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S.L. Zhang, Accurate prediction of protein structural classes by incorporating PSSS and PSSM into Chou’s general PseAAC, Chemometrics and Intelligent Laboratory Systems (CHEMOLAB), 142 (2015) 28-35.
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G.L. Fan, X.Y. Zhang, Y.L. Liu, Y. Nang, H. Wang, DSPMP: Discriminating secretory proteins of malaria parasite by hybridizing different descriptors of Chou’s pseudo amino acid patterns, J. Comput. Chem., 36 (2015) 2317-2327.
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F. Ali, M. Hayat, Classification of membrane protein types using Voting Feature Interval in combination with Chou’s Pseudo Amino Acid Composition, J. Theor. Biol., 384 (2015) 78-83.
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H. Tang, W. Chen, H. Lin, Identification of immunoglobulins using Chou’s pseudo amino acid composition with feature selection technique, Mol Biosyst, 12 (2016) 1269-1275.
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K. Ahmad, M. Waris, M. Hayat, Prediction of Protein Submitochondrial Locations by Incorporating Dipeptide Composition into Chou’s General Pseudo Amino Acid Composition, J. Membr. Biol., 249 (2016) 293-304.
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M. Behbahani, H. Mohabatkar, M. Nosrati, Analysis and comparison of lignin peroxidases between fungi and bacteria using three different modes of Chou’s general pseudo amino acid composition, J. Theor. Biol., 411 (2016) 1-5.
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G.L. Fan, Y.L. Liu, H. Wang, Identification of thermophilic proteins by incorporating evolutionary and acid dissociation information into Chou’s general pseudo amino acid composition, J. Theor. Biol., 407 (2016) 138-142.
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Z. Ju, J.Z. Cao, H. Gu, Predicting lysine phosphoglycerylation with fuzzy SVM by incorporating k-spaced amino acid pairs into Chou’s general PseAAC, J. Theor. Biol., 397 (2016) 145-150.
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A.K. Tiwari, Prediction of G-protein coupled receptors and their subfamilies by incorporating various sequence features into Chou’s general PseAAC, Computer methods and programs in biomedicine, 134 (2016) 197-213.
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C. Xu, D. Sun, S. Liu, Y. Zhang, Protein Sequence Analysis by Incorporating Modified Chaos Game and Physicochemical Properties into Chou’s General Pseudo Amino Acid Composition, J. Theor. Biol., 406 (2016) 105-115.
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H.L. Zou, X. Xiao, Classifying Multifunctional Enzymes by Incorporating Three Different Models into Chou’s General Pseudo Amino Acid Composition (doi:10.1007/s00232-016-9904-3), J Membr Biol 249 (2016) 561-567.
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B. Yu, S. Li, W.Y. Qiu, C. Chen, R.X. Chen, L. Wang, M.H. Wang, Y. Zhang, Accurate prediction of subcellular location of apoptosis proteins combining Chou’s PseAAC and PsePSSM based on wavelet denoising, Oncotarget, 8 (2017) 107640-107665.
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M. Mousavizadegan, H. Mohabatkar, Computational prediction of antifungal peptides via Chou’s PseAAC and SVM, Journal of bioinformatics and computational biology, (2018) 1850016.
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W. Zhao, L. Wang, T.X. Zhang, Z.N. Zhao, P.F. Du, A brief review on software tools in generating Chou’s pseudo-factor representations for all types of biological sequences, Protein Pept Lett, 25 (2018) 822-829.
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M.F. Sabooh, N. Iqbal, M. Khan, M. Khan, H.F. Maqbool, Identifying 5-methylcytosine sites in RNA sequence using composite encoding feature into Chou’s PseKNC, J. Theor. Biol., 452 (2018) 1-9.
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M. Arif, M. Hayat, Z. Jan, 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, J. Theor. Biol., 442 (2018) 11-21.
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S. Akbar, M. Hayat, iMethyl-STTNC: Identification of N(6)-methyladenosine sites by extending the Idea of SAAC into Chou’s PseAAC to formulate RNA sequences, J. Theor. Biol., 455 (2018) 205-211.
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X. Fu, W. Zhu, B. Liso, L. Cai, L. Peng, J. Yang, Improved DNA-binding protein identification by incorporating evolutionary information into the Chou’s PseAAC, IEEE Access, 18 (2018) 43-66.
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Y. Pan, S. Wang, Q. Zhang, Q. Lu, D. Su, Y. Zuo, L. Yang, Analysis and prediction of animal toxins by various Chou’s pseudo components and reduced amino acid compositions, J. Theor. Biol., 462 (2019) 221-229.
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M. Nosrati, H. Mohabatkar, M. Behbahani, Introducing of an integrated artificial neural network and Chou’s pseudo amino acid composition approach for computational epitope-mapping of Crimean-Congo haemorrhagic fever virus antigens, International Immunopharmacology, 78 (2019) 106020.
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M. Nosrati, H. Mohabatkar, M. Behbahani, Introducing of an integrated artificial neural network and Chou’s pseudo amino acid composition approach for computational epitope-mapping of Crimean-Congo haemorrhagic fever virus antigens, Int Immunopharmacol, 78 (2020) 106020.
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M. Tahir, M. Hayat, S.A. Khan, 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, Molecular genetics and genomics: MGG, 294 (2019) 199-210.
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M. Tahir, M. Hayat, iNuc-STNC: a sequence-based predictor for identification of nucleosome positioning in genomes by extending the concept of SAAC and Chou’s PseAAC, Mol Biosyst 12 (2016) 2587-2593.
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M. Tahir, H. Tayara, K.T. Chong, iRNA-PseKNC(2methyl): Identify RNA 2’-O-methylation sites by convolution neural network and Chou’s pseudo components, J. Theor. Biol., 465 (2019) 1-6.
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L. Zhang, L. Kong, iRSpot-ADPM: Identify recombination spots by incorporating the associated dinucleotide product model into Chou’s pseudo components, J. Theor. Biol., 441 (2018) 1-8.
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Y.S. Jiao, P.F. Du, Predicting protein submitochondrial locations by incorporating the positional-specific physicochemical properties into Chou’s general pseudo-amino acid compositions, J. Theor. Biol., 416 (2017) 81-87.
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Z. Ju, J.J. He, Prediction of lysine crotonylation sites by incorporating the composition of k-spaced amino acid pairs into Chou’s general PseAAC, J Mol Graph Model, 77 (2017) 200-204.
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M. Khan, M. Hayat, S.A. Khan, N. Iqbal, Unb-DPC: Identify mycobacterial membrane protein types by incorporating un-biased dipeptide composition into Chou’s general PseAAC, J. Theor. Biol., 415 (2017) 13-19.
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Y. Liang, S. Zhang, Predict protein structural class by incorporating two different modes of evolutionary information into Chou’s general pseudo amino acid composition, J Mol Graph Model, 78 (2017) 110-117.
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P.K. Meher, T.K. Sahu, V. Saini, A.R. Rao, Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou’s general PseAAC, Sci Rep, 7 (2017) 42362.
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W.R. Qiu, Q.S. Zheng, B.Q. Sun, X. Xiao, Multi-iPPseEvo: A Multi-label Classifier for Identifying Human Phosphorylated Proteins by Incorporating Evolutionary Information into Chou’s General PseAAC via Grey System Theory, Mol Inform, 36 (2017) UNSP 1600085.
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C. Xu, L. Ge, Y. Zhang, M. Dehmer, I. Gutman, Prediction of therapeutic peptides by incorporating q-Wiener index into Chou’s general PseAAC, J Biomed Inform, doi:10.1016/j.jbi.2017.09.011 (2017).
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A.H. Butt, N. Rasool, Y.D. Khan, Predicting membrane proteins and their types by extracting various sequence features into Chou’s general PseAAC, Molecular biology reports, 18 (2018) 39-58.
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A.W. Ghauri, Y.D. Khan, N. Rasool, S.A. Khan, K.C. Chou, pNitro-Tyr-PseAAC: Predict nitrotyrosine sites in proteins by incorporating five features into Chou’s general PseAAC, Curr Pharm Des, 24 (2018) 4034-4043.
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Z. Ju, S.Y. Wang, Prediction of citrullination sites by incorporating k-spaced amino acid pairs into Chou’s general pseudo amino acid composition, Gene, 664 (2018) 78-83.
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M.S. Krishnan, Using Chou’s general PseAAC to analyze the evolutionary relationship of receptor associated proteins (RAP) with various folding patterns of protein domains, J. Theor. Biol., 445 (2018) 62-74.
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Y. Liang, S. Zhang, Identify Gram-negative bacterial secreted protein types by incorporating different modes of PSSM into Chou’s general PseAAC via Kullback-Leibler divergence, J. Theor. Biol., 454 (2018) 22-29.
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J. Mei, Y. Fu, J. Zhao, Analysis and prediction of ion channel inhibitors by using feature selection and Chou’s general pseudo amino acid composition, J. Theor. Biol., 456 (2018) 41-48.
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J. Mei, J. Zhao, Analysis and prediction of presynaptic and postsynaptic neurotoxins by Chou’s general pseudo amino acid composition and motif features, J. Theor. Biol., 427 (2018) 147-153.
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S.M. Rahman, S. Shatabda, S. Saha, M. Kaykobad, M. Sohel Rahman, DPP-PseAAC: A DNA-binding Protein Prediction model using Chou’s general PseAAC, J. Theor. Biol., 452 (2018) 22-34.
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E.S. Sankari, D.D. Manimegalai, Predicting membrane protein types by incorporating a novel feature set into Chou’s general PseAAC, J. Theor. Biol., 455 (2018) 319-328.
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A. Srivastava, R. Kumar, M. Kumar, BlaPred: predicting and classifying beta-lactamase using a 3-tier prediction system via Chou’s general PseAAC, J. Theor. Biol., 457 (2018) 29-36.
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S. Zhang, X. Duan, Prediction of protein subcellular localization with oversampling approach and Chou’s general PseAAC, J. Theor. Biol., 437 (2018) 239-250.
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S. Adilina, D.M. Farid, S. Shatabda, Effective DNA binding protein prediction by using key features via Chou’s general PseAAC, J. Theor. Biol., 460 (2019) 64-78.
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M. Awais, W. Hussain, Y.D. Khan, N. Rasool, S.A. Khan, K.C. Chou, iPhosH-PseAAC: Identify phosphohistidine sites in proteins by blending statistical moments and position relative features according to the Chou’s 5-step rule and general pseudo amino acid composition, IEEE/ACM Trans Comput Biol Bioinform, 19 (2019) 1-21.
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M. Behbahani, M. Nosrati, M. Moradi, H. Mohabatkar, Using Chou’s General Pseudo Amino Acid Composition to Classify Laccases from Bacterial and Fungal Sources via Chou’s Five-Step Rule, Appl. Biochem. Biotechnol., 190 (2019) 1035-1048.
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G. Chen, M. Cao, J. Yu, X. Guo, S. Shi, Prediction and functional analysis of prokaryote lysine acetylation site by incorporating six types of features into Chou’s general PseAAC, J. Theor. Biol., 461 (2019) 92-101.
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A. Ehsan, M.K. Mahmood, Y.D. Khan, O.M. Barukab, S.A. Khan, K.C. Chou, iHyd-PseAAC (EPSV): Identify hydroxylation sites in proteins by extracting enhanced position and sequence variant feature via Chou’s 5-step rule and general pseudo amino acid composition, Current Genomics, 20 (2019) 124-133.
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W. Hussain, S.D. Khan, N. Rasool, S.A. Khan, K.C. Chou, SPalmitoylC-PseAAC: A sequence-based model developed via Chou’s 5-steps rule and general PseAAC for identifying S-palmitoylation sites in proteins, Anal. Biochem., 568 (2019) 14-23.
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W. Hussain, Y.D. Khan, N. Rasool, S.A. Khan, K.C. Chou, SPrenylC-PseAAC: A sequence-based model developed via Chou’s 5-steps rule and general PseAAC for identifying S-prenylation sites in proteins, J. Theor. Biol., 468 (2019) 1-11.
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Z. Jun, S.Y. Wang, Identify Lysine Neddylation Sites Using Bi-profile Bayes Feature Extraction via the Chou’s 5-steps Rule and General Pseudo Components, Current Genomics, 20 (2019) 592-601.
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R. Liang, J. Xie, C. Zhang, M. Zhang, H. Huang, H. Huo, X. Cao, B. Niu, Identifying Cancer Targets Based on Machine Learning Methods via Chou’s 5-steps Rule and General Pseudo Components, Current Topics in Medicnal Chemistry, 19 (2019) 2301-2317.
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Y. Shen, J. Tang, F. Guo, Identification of protein subcellular localization via integrating evolutionary and physicochemical information into Chou’s general PseAAC, J. Theor. Biol., 462 (2019) 230-239.
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L. Wang, R. Zhang, Y. Mu, Fu-SulfPred: Identification of Protein S-sulfenylation Sites by Fusing Forests via Chou’s General PseAAC, J. Theor. Biol., 461 (2019) 51-58.
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X. Xiao, X. Cheng, G. Chen, Q. Mao, K.C. Chou, pLoc_bal-mVirus: Predict Subcellular Localization of Multi-Label Virus Proteins by Chou’s General PseAAC and IHTS Treatment to Balance Training Dataset, Med Chem, 15 (2019) 496-509.
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S. Akbar, A.U. Rahman, M. Hayat, S. M., cACP: Classifying anticancer peptides using discriminative intelligent model via Chou’s 5-step rules and general pseudo components, Chemometrics and Intelligent Laboratory (CHEMOLAB), 196 (2020) 103912.
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M. Behbahani, M. Nosrati, M. Moradi, H. Mohabatkar, Using Chou’s General Pseudo Amino Acid Composition to Classify Laccases from Bacterial and Fungal Sources via Chou’s Five-Step Rule, Appl. Biochem. Biotechnol., 190 (2020) 1035-1048.
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Z. Ju, S.Y. Wang, Prediction of lysine formylation sites using the composition of k-spaced amino acid pairs via Chou’s 5-steps rule and general pseudo components, Genomics, 112 (2020) 859-866.
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M. Kabir, S. Ahmad, M. Iqbal, M. Hayat, iNR-2L: A two-level sequence-based predictor developed via Chou’s 5-steps rule and general PseAAC for identifying nuclear receptors and their families, Genomics, 112 (2020) 276-285.
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Y.D. Khan, N. Amin, W. Hussain, N. Rasool, S.A. Khan, K.C. Chou, iProtease-PseAAC(2L): A two-layer predictor for identifying proteases and their types using Chou’s 5-step-rule and general PseAAC, Anal. Biochem., 588 (2020) 113477.
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P. Xuan, H. Cui, T. Shen, N. Sheng, T. Zhang, HeteroDualNet: A Dual Convolutional Neural Network With Heterogeneous Layers for Drug-Disease Association Prediction via Chou’s Five-Step Rule, 10 (2019) 1301.
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P. Xuan, H. Cui, T. Shen, N. Sheng, T. Zhang, HeteroDualNet: A Dual Convolutional Neural Network With Heterogeneous Layers for Drug-Disease Association Prediction via Chou’s Five-Step Rule, Frontiers in Pharmacology, 10 (2019) 1301.
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Y. Zou, Y. Ding, J. Tang, F. Fei Guo, L. Peng, FKRR-MVSF: A fuzzy kernel ridge regression model for identifying DNA-binding proteins by multi-view sequence features via Chou’s five-step rule, Int J Mol Sci, 2019 (2019) 1-14.
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K.C. Chou, W.Z. Lin, X. Xiao, Wenxiang: a web-server for drawing wenxiang diagrams Natural Science, 3 (2011) 862-865
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G.P. Zhou, The disposition of the LZCC protein residues in wenxiang diagram provides new insights into the protein-protein interaction mechanism, J. Theor. Biol., 284 (2011) 142-148.
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S. Sirois, T. Sing, K.C. Chou, Review: HIV-1 gp120 V3 loop for structure-based drug design, Current Protein and Peptide Science, 6 (2005) 413-422.
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S. Sirois, C.M. Tsoukas, K.C. Chou, D.Q. Wei, C. Boucher, G.E. Hatzakis, Selection of Molecular Descriptors with Artificial Intelligence for the Understanding of HIV-1 Protease Peptidomimetic Inhibitors-activity, Medicinal Chemistry, 1 (2005) 173-184.
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W.N. Gao, D.Q. Wei, Y. Li, H. Gao, W.R. Xu, A.X. Li, K.C. Chou, Agaritine and its derivatives are potential inhibitors against HIV proteases, Medicinal Chemistry, 3 (2007) 221-226.
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S. Sirois, M. Touaibia, K.C. Chou, R. Roy, Review: Glycosylation of HIV-1 gp120 V3 loop: towards the rational design of a synthetic carbohydrate vaccine, Current Medicinal Chemistry, 14 (2007) 3232-3242.
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[108]
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H.B. Shen, K.C. Chou, HIVcleave: a web-server for predicting HIV protease cleavage sites in proteins, Anal. Biochem., 375 (2008) 388-390.
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[109]
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J. Dev, D. Park, Q. Fu, J. Chen, H.J. Ha, F. Ghantous, T. Herrmann, W. Chang, Z. Liu, G. Frey, M.S. Seaman, B. Chen, J.J. Chou, Structural Basis for Membrane Anchoring of HIV-1 Envelope Spike, Science 353 (2016) 172-175.
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[110]
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B. Chen, J.J. Chou, Structure of the transmembrane domain of HIV-1 envelope glycoprotein, FEBS J, 284 (2017) 1171-1177.
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[111]
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A. Piai, J. Dev, Q. Fu, J.J. Chou, Stability and Water Accessibility of the Trimeric Membrane Anchors of the HIV-1 Envelope Spikes, J. Am. Chem. Soc., 139 (2017) 18432-18435.
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[112]
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Q. Fu, M.M. Shaik, Y. Cai, F. Ghantous, A. Piai, H. Peng, S. Rits-Volloch, Z. Liu, S.C. Harrison, M.S. Seaman, B. Chen, J.J. Chou, Structure of the membrane proximal external region of HIV-1 envelope glycoprotein, Proc. Natl. Acad. Sci. U. S. A., 115 (2018) E8892-E8899.
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[113]
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F. Li, C. Fan, T.T. Marquez-Lago, A.L. Jerico Revote, C. Jia, Y. Zhu, A.I. Smith, K.C. Chou, G.I. Webb, Q. Liu, L. Wei, J. Li, J. Song, PRISM: a comprehensive 3D structure database for post-translational modifications and mutations with functional impact, bioRxiv (Cold Spring Harbor Laboratory), doi: dx.doi.org/10.1101/523.
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[114]
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Q.S. Du, S.Q. Wang, D.Q. Wei, Y. Zhu, H. Guo, S. Sirois, K.C. Chou, Polyprotein Cleavage Mechanism of SARS CoV Mpro and Chemical Modification of Octapeptide, Peptides, 25 (2004) 1857-1864.
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[115]
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Q.S. Du, S. Wang, D.Q. Wei, S. Sirois, K.C. Chou, Molecular modelling and chemical modification for finding peptide inhibitor against SARS CoV Mpro, Anal. Biochem., 337 (2005) 262-270.
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[116]
|
Q.S. Du, S.Q. Wang, Z.Q. Jiang, W.N. Gao, Y.D. Li, D.Q. Wei, K.C. Chou, Application of bioinformatics in search for cleavable peptides of SARS-CoV Mpro and chemical modification of octapeptides, Medicinal Chemistry, 1 (2005) 209-213.
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[117]
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Y. Xu, K.C. Chou, Recent progress in predicting posttranslational modification sites in proteins, Curr Top Med Chem, 16 (2016) 591-603.
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[118]
|
P. Feng, H. Ding, H. Yang, W. Chen, H. Lin, K.C. Chou, iRNA-PseColl: Identifying the occurrence sites of different RNA modifications by incorporating collective effects of nucleotides into PseKNC, Molecular Therapy - Nucleic Acids 7(2017) 155-163.
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[119]
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W. Chen, P. Feng, H. Yang, H. Ding, H. Lin, K.C. Chou, iRNA-3typeA: identifying 3-types of modification at RNA’s adenosine sites, Molecular Therapy: Nucleic Acid, 11 (2018) 468-474.
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[120]
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Z. Chen, X. Liu, F. Li, C. Li, T. Marquez-Lago, A. Leier, T. Akutsu, G.I. Webb, D. Xu, A.I. Smith, L. Li, K.C. Chou, J. Song, Large-scale comparative assessment of computational predictors for lysine post-translational modification sites, Brief in Bioinform, doi: 10.1093/bib/bby089 (2018).
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[121]
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K.C. Chou, Artificial intelligence (AI) tools constructed via the 5-steps rule for predicting post-translational modifications, Trends in Artificial Inttelengence (TIA), 3 (2019) 60-74.
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[122]
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K.C. Chou, Progresses in predicting post-translational modification (2019), International Journal of Peptide Research and Therapeutics (IJPRT), 26 (2020) 873-888.
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[123]
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K.C. Chou, S.P. Jiang, Studies on the rate of diffusion-controlled reactions of enzymes, Scientia Sinica, 17 (1974) 664-680.
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[124]
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K.C. Chou, Studies on the enzyme kinetics of the cavity-active site, Acta Biochimica et Biophysica Sinica, 7 (1975) 95-103.
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[125]
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K.C. Chou, C.K. Kuo, T.T. Li, The quantitative relations between diffusion-controlled reaction rate and characteristic parameters in enzyme-substrate reaction system: 2. Charged substrates, Scientia Sinica, 18 (1975) 366-380.
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[126]
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K.C. Chou, The kinetics of the combination reaction between enzyme and substrate, Scientia Sinica, 19 (1976) 505-528.
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[127]
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T.T. Li, K.C. Chou, The quantitative relations between diffusion-controlled reaction rate and characteristic parameters in enzyme-substrate reaction system: 1. Neutral substrates, Scientia Sinica, 19 (1976) 117-136.
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[128]
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K.C. Chou, The kinetics of the combination reaction between enzyme and substrate: 1. Stochastic analysis, activation energy and multiple-active-site, Acta Biochimica et Biophysica Sinica, 9 (1977) 79-94.
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[129]
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K.C. Chou, The kinetics of the combination reaction between enzyme and substrate: 2. Multi-barrier reaction and measuring signal, Acta Biochimica et Biophysica Sinica, 9 (1977) 175-186.
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[130]
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K.C. Chou, S.P. Jiang, W.M. Liu, C.H. Fee, Graph theory of enzyme kinetics: 1. Steady-state reaction system, Scientia Sinica, 22 (1979) 341-358.
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[131]
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K.C. Chou, A new schematic method in enzyme kinetics, Eur. J. Biochem., 113 (1980) 195-198.
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[132]
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T.T. Li, K.C. Chou, S. Forsen, The flow of substrate molecules in fast enzyme-catalyzed reaction systems, Chemica Scripta, 16 (1980) 192-196.
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[133]
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K.C. Chou, Two new schematic rules for rate laws of enzyme-catalyzed reactions, J. Theor. Biol., 89 (1981) 581-592.
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[134]
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K.C. Chou, A new graphical rule for rate laws of enzyme reactions with branched pathways, Canadian Journal of Biochemistry, 59 (1981) 757-761.
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[135]
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K.C. Chou, T.T. Li, G.Q. Zhou, A semi-analytical expression for the concentration distribution of substrate molecules in fast, enzyme-catalyzed reaction systems, Biochim. Biophys. Acta, 657 (1981) 304-308.
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[136]
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K.C. Chou, W.M. Liu, Graphical rules for non-steady state enzyme kinetics, J. Theor. Biol., 91 (1981) 637-654.
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[137]
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K.C. Chou, G.P. Zhou, Role of the protein outside active site on the diffusion-controlled reaction of enzyme, Journal of American Chemical Society, 104 (1982) 1409-1413.
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[138]
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K.C. Chou, Advances in graphical methods of enzyme kinetics, Biophysical Chemistry, 17 (1983) 51-55.
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[139]
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K.C. Chou, Graphic rules in steady and non-steady enzyme kinetics, J. Biol. Chem., 264 (1989) 12074-12079.
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[140]
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K.C. Chou, Review: Applications of graph theory to enzyme kinetics and protein folding kinetics. Steady and non-steady state systems, Biophysical Chemistry, 35 (1990) 1-24.
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[141]
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K.C. Chou, Graphic rule for non-steady-state enzyme kinetics and protein folding kinetics, Journal of Mathematical Chemistry, 12 (1993) 97-108.
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[142]
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K.C. Chou, D.W. Elrod, Prediction of enzyme family classes, Journal of Proteome Research, 2 (2003) 183-190.
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[143]
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K.C. Chou, Y.D. Cai, A novel approach to predict active sites of enzyme molecules, Proteins: Struct., Funct., Genet., 55 (2004) 77-82.
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[144]
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K.C. Chou, Y.D. Cai, Predicting enzyme family class in a hybridization space, Protein Science, 13 (2004) 2857-2863.
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[145]
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K.C. Chou, Y.D. Cai, Using GO-PseAA predictor to predict enzyme sub-class, Biochemical and Biophysical Research Communications (BBRC), 325 (2004) 506-509.
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[146]
|
Y.D. Cai, K.C. Chou, Using functional domain composition to predict enzyme family classes, Journal of Proteome Research, 4 (2005) 109-111.
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[147]
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Y.D. Cai, K.C. Chou, Predicting enzyme subclass by functional domain composition and pseudo amino acid composition, Journal of Proteome Research, 4 (2005) 967-971.
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[148]
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Y.D. Cai, G.P. Zhou, K.C. Chou, Predicting enzyme family classes by hybridizing gene product composition and pseudo amino acid composition, J. Theor. Biol., 234 (2005) 145-149.
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[149]
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K.C. Chou, Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes, Bioinformatics, 21 (2005) 10-19.
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[150]
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H.B. Shen, K.C. Chou, EzyPred: A top-down approach for predicting enzyme functional classes and subclasses, Biochem Biophys Res Comm (BBRC), 364 (2007) 53-59.
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[151]
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H. Wei, R. Zhang, C. Wang, H. Zheng, K.C. Chou, D.Q. Wei, Molecular insights of SAH enzyme catalysis and their implication for inhibitor design, J. Theor. Biol., 244 (2007) 692-702.
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[152]
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J.L. Min, X. Xiao, K.C. Chou, iEzy-Drug: A web server for identifying the interaction between enzymes and drugs in cellular networking, BioMed Research International (BMRI), 2013 (2013) 701317.
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[153]
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K.C. Chou, D.W. Elrod, Using discriminant function for prediction of subcellular location of prokaryotic proteins, Biochem Biophys Res Commun (BBRC), 252 (1998) 63-68.
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[154]
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K.C. Chou, D.W. Elrod, Protein subcellular location prediction, Protein Eng., 12 (1999) 107-118.
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[155]
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K.C. Chou, D.W. Elrod, Prediction of membrane protein types and subcellular locations, Proteins: Struct., Funct., Genet., 34 (1999) 137-153.
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[156]
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K.C. Chou, Review: Prediction of protein structural classes and subcellular locations, Current Protein and Peptide Science, 1 (2000) 171-208.
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[157]
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K.C. Chou, Prediction of protein subcellular locations by incorporating quasi-sequence-order effect, Biochem Biophys Res Comm (BBRC), 278 (2000) 477-483.
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[158]
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Y.D. Cai, X.J. Liu, X.B. Xu, K.C. Chou, Support vector machines for prediction of protein subcellular location by incorporating quasi-sequence-order effect, J. Cell. Biochem., 84 (2002) 343-348.
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[159]
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Y.D. Cai, K.C. Chou, Nearest neighbour algorithm for predicting protein subcellular location by combining functional domain composition and pseudo amino acid composition, Biochem Biophys Res Comm (BBRC), 305 (2003) 407-411.
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[160]
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K.C. Chou, Y.D. Cai, Prediction and classification of protein subcellular location: sequence-order effect and pseudo amino acid composition, Journal of Cellular Biochemistry (Addendum, ibid. 2004, 91, 1085), 90 (2003) 1250-1260.
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[161]
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K.C. Chou, Y.D. Cai, Prediction of protein subcellular locations by GO-FunD-PseAA predicor, Biochemical and Biophysical Research Communications (BBRC), 320 (2004) 1236-1239.
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[162]
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K.C. Chou, Y.D. Cai, Predicting subcellular localization of proteins by hybridizing functional domain composition and pseudo amino acid composition, J. Cell. Biochem., 91 (2004) 1197-1203.
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[163]
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Y. Gao, S.H. Shao, X. Xiao, Y.S. Ding, Y.S. Huang, Z.D. Huang, K.C. Chou, Using pseudo amino acid composition to predict protein subcellular location: approached with Lyapunov index, Bessel function, and Chebyshev filter, Amino Acids, 28 (2005) 373-376.
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[164]
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X. Xiao, S. Shao, Y. Ding, Z. Huang, Y. Huang, K.C. Chou, Using complexity measure factor to predict protein subcellular location, Amino Acids, 28 (2005) 57-61.
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[165]
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K.C. Chou, H.B. Shen, Predicting protein subcellular location by fusing multiple classifiers, J. Cell. Biochem., 99 (2006) 517-527.
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[166]
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K.C. Chou, H.B. Shen, Hum-PLoc: A novel ensemble classifier for predicting human protein subcellular localization, Biochem. Biophys. Res. Commun. (BBRC), 347 (2006) 150-157.
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[167]
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K.C. Chou, H.B. Shen, Predicting eukaryotic protein subcellular location by fusing optimized evidence-theoretic K-nearest neighbor classifiers, Journal of Proteome Research, 5 (2006) 1888-1897.
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[168]
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K.C. Chou, H.B. Shen, Addendum to “Hum-PLoc: A novel ensemble classifier for predicting human protein subcellular localization”, Biochem. Biophys. Res. Commun. (BBRC), 348 (2006) 1479.
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[169]
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X. Xiao, S.H. Shao, Y.S. Ding, Z.D. Huang, K.C. Chou, Using cellular automata images and pseudo amino acid composition to predict protein subcellular location, Amino Acids, 30 (2006) 49-54.
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[170]
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K.C. Chou, H.B. Shen, Euk-mPLoc: a fusion classifier for large-scale eukaryotic protein subcellular location prediction by incorporating multiple sites, Journal of Proteome Research, 6 (2007) 1728-1734.
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[171]
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K.C. Chou, H.B. Shen, Recent progresses in protein subcellular location prediction, Anal. Biochem., 370 (2007) 1-16.
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[172]
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H.B. Shen, K.C. Chou, Gpos-PLoc: an ensemble classifier for predicting subcellular localization of Gram-positive bacterial proteins, Protein Engineering, Design, and Selection, 20 (2007) 39-46.
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[173]
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H.B. Shen, J. Yang, K.C. Chou, Review: Methodology development for predicting subcellular localization and other attributes of proteins, Expert Review of Proteomics, 4 (2007) 453-463.
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[174]
|
K.C. Chou, H.B. Shen, Cell-PLoc: A package of Web servers for predicting subcellular localization of proteins in various organisms, Nature Protocols, 3 (2008) 153-162.
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[175]
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H.B. Shen, K.C. Chou, A top-down approach to enhance the power of predicting human protein subcellular localization: Hum-mPLoc 2.0, Anal. Biochem., 394 (2009) 269-274.
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[176]
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H.B. Shen, K.C. Chou, Gpos-mPLoc: A top-down approach to improve the quality of predicting subcellular localization of Gram-positive bacterial proteins, Protein & Peptide Letters, 16 (2009) 1478-1484.
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[177]
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K.C. Chou, H.B. Shen, Cell-PLoc 2.0: An improved package of web-servers for predicting subcellular localization of proteins in various organisms, Natural Science, 2 (2010) 1090-1103.
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[178]
|
H.B. Shen, K.C. Chou, Gneg-mPLoc: A top-down strategy to enhance the quality of predicting subcellular localization of Gram-negative bacterial proteins, Journal of Theoretical Biology, 264 (2010) 326-333.
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[179]
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S.B. Wan, L.L. Hu, S. Niu, K. Wang, Y.D. Cai, K.C. Chou, Identification of multiple subcellular locations for proteins in budding yeast, Current Bioinformatics, 6 (2011) 71-80.
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[180]
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K.C. Chou, Z.C. Wu, X. Xiao, iLoc-Hum: Using accumulation-label scale to predict subcellular locations of human proteins with both single and multiple sites, Molecular Biosystems, 8 (2012) 629-641.
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[181]
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W.Z. Lin, J.A. Fang, X. Xiao, K.C. Chou, iLoc-Animal: A multi-label learning classifier for predicting subcellular localization of animal proteins Molecular BioSystems, 9 (2013) 634-644.
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[182]
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X. Cheng, X. Xiao, K.C. Chou, pLoc-mPlant: predict subcellular localization of multi-location plant proteins via incorporating the optimal GO information into general PseAAC, Molecular BioSystems, 13 (2017) 1722-1727.
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[183]
|
X. Cheng, X. Xiao, K.C. Chou, pLoc-mVirus: predict subcellular localization of multi-location virus proteins via incorporating the optimal GO information into general PseAAC, Gene (Erratum: ibid., 2018, Vol.644, 156-156), 628 (2017) 315-321.
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[184]
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X. Cheng, S.G. Zhao, W.Z. Lin, X. Xiao, K.C. Chou, pLoc-mAnimal: predict subcellular localization of animal proteins with both single and multiple sites, Bioinformatics, 33 (2017) 3524-3531.
|
[185]
|
X. Xiao, X. Cheng, S. Su, Q. Nao, K.C. Chou, pLoc-mGpos: Incorporate key gene ontology information into general PseAAC for predicting subcellular localization of Gram-positive bacterial proteins, Natural Science, 9 (2017) 330-349.
|
[186]
|
X. Cheng, X. Xiao, K.C. Chou, pLoc-mEuk: Predict subcellular localization of multi-label eukaryotic proteins by extracting the key GO information into general PseAAC, Genomics, 110 (2018) 50-58.
|
[187]
|
X. Cheng, X. Xiao, K.C. Chou, pLoc-mGneg: Predict subcellular localization of Gram-negative bacterial proteins by deep gene ontology learning via general PseAAC, Genomics, 110 (2018) 231-239.
|
[188]
|
X. Cheng, X. Xiao, K.C. Chou, pLoc-mHum: predict subcellular localization of multi-location human proteins via general PseAAC to winnow out the crucial GO information, Bioinformatics, 34 (2018) 1448-1456.
|
[189]
|
X. Cheng, X. Xiao, K.C. Chou, pLoc_bal-mGneg: predict subcellular localization of Gram-negative bacterial proteins by quasi-balancing training dataset and general PseAAC, Journal of Theoretical Biology, 458 (2018) 92-102.
|
[190]
|
X. Cheng, X. Xiao, K.C. Chou, pLoc_bal-mPlant: predict subcellular localization of plant proteins by general PseAAC and balancing training dataset Curr Pharm Des, 24 (2018) 4013-4022.
|
[191]
|
Z.D. Su, Y. Huang, Z.Y. Zhang, Y.W. Zhao, D. Wang, W. Chen, K.C. Chou, H. Lin, iLoc-lncRNA: predict the subcellular location of lncRNAs by incorporating octamer composition into general PseKNC, Bioinformatics, 34 (2018) 4196-4204.
|
[192]
|
X. Cheng, W.Z. Lin, X. Xiao, K.C. Chou, pLoc_bal-mAnimal: predict subcellular localization of animal proteins by balancing training dataset and PseAAC, Bioinformatics, 35 (2019) 398-406.
|
[193]
|
K.C. Chou, Advance in predicting subcellular localization of multi-label proteins and its implication for developing multi-target drugs, Current Medicinal Chemistry 26 (2019) 4918-4943.
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[194]
|
K.C. Chou, Recent progresses in predicting protein subcellular localization with artificial intelligence tools developed via the 5-steps rule, Medicinal Chemistry, Submitted (2019).
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[195]
|
K.C. Chou, An insightful recollection for predicting protein subcellular locations in multi-label systems, Natural Science, (2019).
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[196]
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K.C. Chou, Recent Progresses in Predicting Protein Subcellular Localization with Artificial Intelligence (AI) Tools Developed Via the 5-Steps Rule, Japanese Journal of Gastroenterology and Hepatology
https://www.jjgastrohepto.org/v2issue4.php 2(2019) 1-4.
|
[197]
|
K.C. Chou, The pLoc_bal-mPlant is a Powerful Artificial Intelligence Tool for Predicting the Subcellular Localization of Plant Proteins Purely based on their Sequence Information, Int J Nutr Sci., 4 (2019) 1-4.
|
[198]
|
K.C. Chou, The pLoc_bal-mPlant is a powerful artificial intelligence tool for predicting the subcellular localization of plant proteins purely based on their sequence information, J Stem Cell Res Med, 4 (2019) 1-4.
|
[199]
|
K.C. Chou, The pLoc_bal-mAnimal is a powerful artificial intelligence tool for predicting the subcellular localization of animal proteins based on their sequence information alone, Scientific Journal of Biometrics & Biostatistics, 2 (2019) 1-13.
|
[200]
|
K.C. Chou, X. Cheng, X. Xiao, pLoc_bal-mHum: predict subcellular localization of human proteins by PseAAC and quasi-balancing training dataset Genomics, 111 (2019) 1274-1282.
|
[201]
|
K.C. Chou, X. Cheng, X. Xiao, pLoc_bal-mEuk: predict subcellular localization of eukaryotic proteins by general PseAAC and quasi-balancing training dataset, Med Chem, 15 (2019) 472-485.
|
[202]
|
X. Xiao, X. Cheng, G. Chen, Q. Mao, K.C. Chou, pLoc_bal-mGpos: predict subcellular localization of Gram-positive bacterial proteins by quasi-balancing training dataset and PseAAC, Genomics, 111 (2019) 886-892.
|
[203]
|
K.C. Chou, The pLoc_bal-mGneg Predictor is a Powerful Web-Server for Identifying the Subcellular Localization of Gram-Negative Bacterial Proteins based on their Sequences Information Alone, ijSci, 9 (2020) 27-34.
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[204]
|
K.C. Chou, The pLoc_bal-mVirus is a powerful artificial intelligence tool for predicting the subcellular localization of virus proteins according to their sequence information alone, J Gent & Genome, 4 (2020).
|
[205]
|
K.C. Chou, The pLoc_bal-mHum is a Powerful Web-Serve for Predicting the Subcellular Localization of Human Proteins Purely Based on Their Sequence Information, Adv Bioeng Biomed Sci Res, 3 (2020) 1-5.
|
[206]
|
K.C. Chou, The pLoc_bal-mGpos is a powerful artificial intelligence tool for predicting the subcellular localization of Gram-positive bacterial proteins according to their sequence information alone, Glo J of Com Sci and Infor Tec, 2 (2020) 01-13.
|
[207]
|
X.X. Liu, K.C. Chou, pLoc_Deep-mGneg: predict subcellular localization of Gram negative bacterial proteins by deep learning Advances in Bioscience and Biotechnology (ABB) 11 (2020) 141-152.
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[208]
|
Y.H. Shao, K.C. Chou, pLoc_Deep-mVirus: A CNN Model for Predicting Subcellular Localization of Virus Proteins by Deep Learning, Natural Science, 12 (2020) 1-12.
|
[209]
|
Y.T. Shao, K.C. Chou, pLoc_Deep-mEuk: predict subcellular localization of eukaryotic proteins by deep learning Natural Science, 12 (2020) 1-29.
|
[210]
|
Y.T. Shao, K.C. Chou, pLoc_Deep-mAnimal: A Novel Deep CNN-BLSTM Network to Predict Subcellular Localization of Animal Proteins Natural Science, 12 (2020) 281-291.
|
[211]
|
Y.T. Shao, X.X. Liu, Z. Lu, K.C. Chou, pLoc_Deep-mHum: predict subcellular localization of human proteins by deep learning Natural Science, 12 (2020) 526-547.
|
[212]
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Y.T. Shao, X.X. Liu, Z. Lu, K.C. Chou, pLoc_Deep-mPlant: predict subcellular localization of plant proteins by deep learning Natural Science 12 (2020) 237-247.
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[213]
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K.C. Chou, S. Forsen, Graphical rules for enzyme-catalyzed rate laws, Biochem. J., 187 (1980) 829-835.
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[214]
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K.C. Chou, R.E. Carter, S. Forsen, A new graphical method for deriving rate equations for complicated mechanisms, Chemica Scripta, 18 (1981) 82-86.
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[215]
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K.C. Chou, S. Forsen, Graphical rules of steady-state reaction systems, Can. J. Chem., 59 (1981) 737-755.
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[216]
|
C.T. Zhang, K.C. Chou, Graphic analysis of codon usage strategy in 1490 human proteins, J. Protein Chem., 12 (1993) 329-335.
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[217]
|
K.C. Chou, Graphic rule for drug metabolism systems, Current Drug Metabolism, 11 (2010) 369-378.
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[218]
|
Z.C. Wu, X. Xiao, K.C. Chou, 2D-MH: A web-server for generating graphic representation of protein sequences based on the physicochemical properties of their constituent amino acids, J. Theor. Biol., 267 (2010) 29-34.
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[219]
|
T. Huang, L. Chen, Y.D. Cai, K.C. Chou, Classification and analysis of regulatory pathways using graph property, biochemical and physicochemical property, and functional property, PLoS ONE, 6 (2011) e25297.
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[220]
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K.C. Chou, S. Forsen, Diffusion-controlled effects in reversible enzymatic fast reaction system: Critical spherical shell and proximity rate constants, Biophysical Chemistry, 12 (1980) 255-263.
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[221]
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K.C. Chou, S. Forsen, G.Q. Zhou, Three schematic rules for deriving apparent rate constants, Chemica Scripta, 16 (1980) 109-113.
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[222]
|
K.C. Chou, T.T. Li, S. Forsen, The critical spherical shell in enzymatic fast reaction systems, Biophysical Chemistry, 12 (1980) 265-269.
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[223]
|
K.C. Chou, N.Y. Chen, S. Forsen, The biological functions of low-frequency phonons: 2. Cooperative effects, Chemica Scripta, 18 (1981) 126-132.
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[224]
|
K.C. Chou, N.Y. Chen, The biological functions of low-frequency phonons, Scientia Sinica, 20 (1977) 447-457.
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[225]
|
K.C. Chou, Low-frequency vibrations of helical structures in protein molecules, Biochem. J., 209 (1983) 573-580.
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[226]
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K.C. Chou, Identification of low-frequency modes in protein molecules, Biochem. J., 215 (1983) 465-469.
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[227]
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G.P. Zhou, M.H. Deng, An extension of Chou’s graphic rules for deriving enzyme kinetic equations to systems involving parallel reaction pathways, Biochem. J., 222 (1984) 169-176.
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[228]
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K.C. Chou, Biological functions of low-frequency vibrations ( phonons). 3. Helical structures and microenvironment, Biophys. J., 45 (1984) 881-889.
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[229]
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K.C. Chou, The biological functions of low-frequency phonons. 4. Resonance effects and allosteric transition, Biophysical Chemistry, 20 (1984) 61-71.
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[230]
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K.C. Chou, Low-frequency vibrations of DNA molecules, Biochem. J., 221 (1984) 27-31.
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[231]
|
K.C. Chou, Low-frequency motions in protein molecules: beta-sheet and beta-barrel, Biophys. J., 48 (1985) 289-297.
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[232]
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K.C. Chou, Prediction of a low-frequency mode in bovine pancreatic trypsin inhibitor molecule, International Journal of Biological Macromolecules, 7 (1985) 77-80.
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[233]
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K.C. Chou, Y.S. Kiang, The biological functions of low-frequency phonons: 5. A phenomenological theory, Biophysical Chemistry, 22 (1985) 219-235.
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[234]
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K.C. Chou, Origin of low-frequency motion in biological macromolecules: A view of recent progress of quasi-continuity model, Biophysical Chemistry, 25 (1986) 105-116.
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K.C. Chou, The biological functions of low-frequency phonons: 6. A possible dynamic mechanism of allosteric transition in antibody molecules, Biopolymers, 26 (1987) 285-295.
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K.C. Chou, Review: Low-frequency collective motion in biomacromolecules and its biological functions, Biophysical Chemistry, 30 (1988) 3-48.
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K.C. Chou, G.M. Maggiora, The biological functions of low-frequency phonons: 7. The impetus for DNA to accommodate intercalators, British Polymer Journal, 20 (1988) 143-148.
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K.C. Chou, Low-frequency resonance and cooperativity of hemoglobin, Trends Biochem. Sci., 14 (1989) 212-213.
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K.C. Chou, G.M. Maggiora, B. Mao, Quasi-continuum models of twist-like and accordion-like low-frequency motions in DNA, Biophys. J., 56 (1989) 295-305.
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I.W. Althaus, J.J. Chou, A.J. Gonzales, M.R. Diebel, K.C. Chou, F.J. Kezdy, D.L. Romero, R.C. Thomas, P.A. Aristoff, W.G. Tarpley, F. Reusser, Kinetic studies with the non-nucleoside human immunodeficiency virus type-1 reverse transcriptase inhibitor U-90152e, Biochem. Pharmacol., 47 (1994) 2017-2028.
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K.C. Chou, F.J. Kezdy, F. Reusser, Review: Kinetics of processive nucleic acid polymerases and nucleases, Anal. Biochem., 221 (1994) 217-230.
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K.C. Chou, C.T. Zhang, G.M. Maggiora, Solitary wave dynamics as a mechanism for explaining the internal motion during microtubule growth, Biopolymers, 34 (1994) 143-153.
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H. Liu, M. Wang, K.C. Chou, Low-frequency Fourier spectrum for predicting membrane protein types, Biochem Biophys Res Commun (BBRC), 336 (2005) 737-739.
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G. Gordon, Designed Electromagnetic Pulsed Therapy: Clinical Applications, J. Cell. Physiol., 212 (2007) 579-582.
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J. Andraos, Kinetic plasticity and the determination of product ratios for kinetic schemes leading to multiple products without rate laws: new methods based on directed graphs, Can. J. Chem., 86 (2008) 342-357.
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K.C. Chou, H.B. Shen, FoldRate: A web-server for predicting protein folding rates from primary sequence, The Open Bioinformatics Journal, 3 (2009) 31-50
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H.B. Shen, J.N. Song, K.C. Chou, Prediction of protein folding rates from primary sequence by fusing multiple sequential features Journal of Biomedical Science and Engineering (JBiSE), 2 (2009) 136-143.
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J.F. Wang, K.C. Chou, Insight into the molecular switch mechanism of human Rab5a from molecular dynamics simulations, Biochem Biophys Res Commun (BBRC), 390 (2009) 608-612.
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G. Gordon, Extrinsic electromagnetic fields, low frequency (phonon) vibrations, and control of cell function: a non-linear resonance system, Journal of Biomedical Science and Engineering (JBiSE), 1 (2008) 152-156
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A. Madkan, M. Blank, E. Elson, K.C. Chou, M.S. Geddis, R. Goodman, Steps to the clinic with ELF EMF Natural Science 1(2009) 157-165.
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P. Lian, D.Q. Wei, J.F. Wang, K.C. Chou, An allosteric mechanism inferred from molecular dynamics simulations on phospholamban pentamer in lipid membranes, PLoS ONE, 6 (2011) e18587.
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Q.H. Liao, Q.Z. Gao, J. Wei, K.C. Chou, Docking and Molecular Dynamics Study on the Inhibitory Activity of Novel Inhibitors on Epidermal Growth Factor Receptor (EGFR), Medicinal Chemistry, 7 (2011) 24-31.
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J. Li, D.Q. Wei, J.F. Wang, Z.T. Yu, K.C. Chou, Molecular Dynamics Simulations of CYP2E1, Medicinal Chemistry, 8 (2012) 208-221.
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J.F. Wang, K.C. Chou, Recent advances in computational studies on influenza a virus m2 proton channel, Mini Reviews in Medicinal Chemistry, 12 (2012) 971-978.
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T. Zhang, D.Q. Wei, K.C. Chou, A Pharmacophore Model Specific to Active Site of CYP1A2 with a Novel Molecular Modeling Explorer and CoMFA, Medicinal Chemistry, 8 (2012) 198-207.
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J. Jia, Z. Liu, X. Xiao, K.C. Chou, iPPI-Esml: an ensemble classifier for identifying the interactions of proteins by incorporating their physicochemical properties and wavelet transforms into PseAAC, J. Theor. Biol., 377 (2015) 47-56.
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J. Jia, Z. Liu, X. Xiao, B. Liu, K.C. Chou, Identification of protein-protein binding sites by incorporating the physicochemical properties and stationary wavelet transforms into pseudo amino acid composition (iPPBS-PseAAC), J Biomol Struct Dyn (JBSD) 34 (2016) 1946-1961.
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K.C. Chou, Proposing pseudo amino acid components is an important milestone for proteome and genome analyses (2019), International Journal for Peptide Research and Therapeutics (IJPRT) 26 (2019) 1085-1098.
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K.C. Chou, Impacts of pseudo amino acid components and 5-steps rule to proteomics and proteome analysis, Current Topics in Medicinak Chemistry (CTMC) (Special Issue ed. G.P Zhou), 19 (2019) 2283-2300.
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K.C. Chou, Coronavirus and Gordon Life Science Institute, Natural Science, 12 (2020) 429-440.
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K.C. Chou, The Implication of “I Am the Alpha and the Omega” to Internet Institutes, Natural Science, 12 (2020) 482-494.
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K.C. Chou, Noah’s Ark and Internet Institutes: When and Why?, Natural Science, 12 (2020) 470-481.
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K.C. Chou, The Pandemic Pestilences and Internet Institutes, Natural Science, 12 (2020) 495-515.
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