It is extremely fearful for the pestilences covering our Earth. Does that mean the “World End” is around the corner? For the so-called “Atheists” originally proposed by Karl Max and Friedrich Engels, “there is a Beginning, there must be an End”, meaning our Earth will finally no longer exist in the entire Universe by colliding with the other planet. According to Holly Bible, however, Jesus, will send out his angels to separate the wicked from the righteous and throw the former into the fiery furnace. For such a special time-period, many useful ideas or outcomes can be acquired by the Internet Institutes.
KeywordsPandemic COVID-19CoronavirusAtheistsFiery FurnaceEvil and RighteousInternet Institutes
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Tahir, M. and Hayat, M. (2016) iNuc-STNC: A Sequence-Based Predictor for Identification of Nucleosome Positioning in Genomes by Extending the Concept of SAAC and Chou’s PseAAC. Molecular BioSystems, 12, 2587-2593. https://doi.org/10.1039/C6MB00221H
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Butt, A.H., Rasool, N. and Khan, Y.D. (2019) Prediction of Antioxidant Proteins by Incorporating Statistical Moments Based Features into Chou’s PseAAC. Journal of Theoretical Biology, 473, 1-8. https://doi.org/10.1016/j.jtbi.2019.04.019
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Hayat, M. and Iqbal, N. (2014) 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, 184-192. https://doi.org/10.1016/j.cmpb.2014.06.007
Ahmad, S., Kabir, M. and Hayat, M. (2015) 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, 165-174. https://doi.org/10.1016/j.cmpb.2015.07.005
Dehzangi, A., Heffernan, R., Sharma, A., Lyons, J., Paliwal, K. and Sattar, A. (2015) Gram-Positive and Gram-Negative Protein Subcellular Localization by Incorporating Evolutionary-Based Descriptors into Chou’s General PseAAC. Journal of Theoretical Biology, 364, 284-294. https://doi.org/10.1016/j.jtbi.2014.09.029
Sharma, R., Dehzangi, A., Lyons, J., Paliwal, K., Tsunoda, T. and Sharma, A. (2015) Predict Gram-Positive and Gram-Negative Subcellular Localization via Incorporating Evolutionary Information and Physicochemical Features Into Chou’s General PseAAC. IEEE Transactions on NanoBioscience, 14, 915-926. https://doi.org/10.1109/TNB.2015.2500186
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Behbahani, M., Mohabatkar, H. and Nosrati, M. (2016) Analysis and Comparison of Lignin Peroxidases between Fungi and Bacteria Using Three Different Modes of Chou’s General Pseudo Amino Acid Composition. Journal of Theoretical Biology, 411, 1-5. https://doi.org/10.1016/j.jtbi.2016.09.001
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Ju, Z., Cao, J.Z. and Gu, H. (2016) Predicting Lysine Phosphoglycerylation with Fuzzy SVM by Incorporating k-Spaced Amino Acid Pairs into Chou’s General PseAAC. Journal of Theoretical Biology, 397, 145-150. https://doi.org/10.1016/j.jtbi.2016.02.020
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Jiao, Y.S. and Du, P.F. (2017) Predicting Protein Submitochondrial Locations by Incorporating the Positional-Specific Physicochemical Properties into Chou’s General Pseudo-Amino Acid Compositions. Journal of Theoretical Biology, 416, 81-87. https://doi.org/10.1016/j.jtbi.2016.12.026
Ju, Z. and He, J.J. (2017) Prediction of Lysine Crotonylation Sites by Incorporating the Composition of k-Spaced Amino Acid Pairs into Chou’s General PseAAC. Journal of Molecular Graphics and Modelling, 77, 200-204. https://doi.org/10.1016/j.jmgm.2017.08.020
Khan, M., Hayat, M., Khan, S.A. and Iqbal, N. (2017) Unb-DPC: Identify Mycobacterial Membrane Protein Types by Incorporating Un-Biased Dipeptide Composition into Chou’s General PseAAC. Journal of Theoretical Biology, 415, 13-19. https://doi.org/10.1016/j.jtbi.2016.12.004
Liang, Y. and Zhang, S. (2017) Predict Protein Structural Class by Incorporating Two Different Modes of Evolutionary Information into Chou’s General Pseudo Amino Acid Composition. Journal of Molecular Graphics and Modelling, 78, 110-117. https://doi.org/10.1016/j.jmgm.2017.10.003
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Xu, C., Ge, L., Zhang, Y., Dehmer, M. and Gutman, I. (2017) Prediction of Therapeutic Peptides by Incorporating q-Wiener Index into Chou’s General PseAAC. Journal of Biomedical Informatics, 75, 63-69.
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Ghauri, A.W., Khan, Y.D., Rasool, N., Khan, S.A. and Chou, K.C. (2018) pNitro-Tyr-PseAAC: Predict Nitrotyrosine Sites in Proteins by Incorporating Five Features into Chou’s General PseAAC. Current Pharmaceutical Design, 24, 4034-4043. https://doi.org/10.2174/1381612825666181127101039
Ju, Z. and Wang, S.Y. (2018) Prediction of Citrullination Sites by Incorporating k-Spaced Amino Acid Pairs into Chou’s General Pseudo Amino Acid Composition. Gene, 664, 78-83. https://doi.org/10.1016/j.gene.2018.04.055
Krishnan, M.S. (2018) Using Chou’s General PseAAC to Analyze the Evolutionary Relationship of Receptor Associated Proteins (RAP) with Various Folding Patterns of Protein Domains. Journal of Theoretical Biology, 445, 62-74. https://doi.org/10.1016/j.jtbi.2018.02.008
Liang, Y. and Zhang, S. (2018) Identify Gram-Negative Bacterial Secreted Protein Types by Incorporating Different Modes of PSSM into Chou’s General PseAAC via Kullback-Leibler Divergence. Journal of Theoretical Biology, 454, 22-29. https://doi.org/10.1016/j.jtbi.2018.05.035
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Mei, J. and Zhao, J. (2018) Analysis and Prediction of Presynaptic and Postsynaptic Neurotoxins by Chou’s General Pseudo Amino Acid Composition and Motif Features. Journal of Theoretical Biology, 427, 147-153. https://doi.org/10.1016/j.jtbi.2018.03.034
Rahman, S.M., Shatabda, S., Saha, S., Kaykobad, M. and Sohel Rahman, M. (2018) DPP-PseAAC: A DNA-Binding Protein Prediction Model Using Chou’s General PseAAC. Journal of Theoretical Biology, 452, 22-34. https://doi.org/10.1016/j.jtbi.2018.05.006
Sankari, E.S. and Manimegalai, D.D. (2018) Predicting Membrane Protein Types by Incorporating a Novel Feature Set into Chou’s General PseAAC. Journal of Theoretical Biology, 455, 319-328. https://doi.org/10.1016/j.jtbi.2018.07.032
Srivastava, A., Kumar, R. and Kumar, M. (2018) BlaPred: Predicting and Classifying Beta-Lactamase Using a 3-Tier Prediction System via Chou’s General PseAAC. Journal of Theoretical Biology, 457, 29-36. https://doi.org/10.1016/j.jtbi.2018.08.030
Zhang, S. and Duan, X. (2018) Prediction of Protein Subcellular Localization with Oversampling Approach and Chou’s General PseAAC. Journal of Theoretical Biology, 437, 239-250. https://doi.org/10.1016/j.jtbi.2017.10.030
Adilina, S., Farid, D.M. and Shatabda, S. (2019) Effective DNA Binding Protein Prediction by Using Key Features via Chou’s General PseAAC. Journal of Theoretical Biology, 460, 64-78. https://doi.org/10.1016/j.jtbi.2018.10.027
Behbahani, M., Nosrati, M., Moradi, M. and Mohabatkar, H. (2019) Using Chou’s General Pseudo Amino Acid Composition to Classify Laccases from Bacterial and Fungal Sources via Chou’s Five-Step Rule. Applied Biochemistry and Biotechnology, 190, 1035-1048. https://doi.org/10.1007/s12010-019-03141-8
Chen, G., Cao, M., Yu, J., Guo, X. and Shi, S. (2019) Prediction and Functional Analysis of Prokaryote Lysine Acetylation Site by Incorporating Six Types of Features into Chou’s General PseAAC. Journal of Theoretical Biology, 461, 92-101. https://doi.org/10.1016/j.jtbi.2018.10.047
Nazari, I., Tahir, M., Tayari, H. and Chong, K.T. (2019) iN6-Methyl (5-Step): Identifying RNA N6-Methyladenosine Sites Using Deep Learning Mode via Chou’s 5-Step Rules and Chou’s General PseKNC. Chemometrics and Intelligent Laboratory Systems, 193, Article ID: 103811. https://doi.org/10.1016/j.chemolab.2019.103811
Shen, Y., Tang, J. and Guo, F. (2019) Identification of Protein Subcellular Localization via Integrating Evolutionary and Physicochemical Information into Chou’s General PseAAC. Journal of Theoretical Biology, 462, 230-239. https://doi.org/10.1016/j.jtbi.2018.11.012
Wang, L., Zhang, R. and Mu, Y. (2019) Fu-SulfPred: Identification of Protein S-Sulfenylation Sites by Fusing Forests via Chou’s General PseAAC. Journal of Theoretical Biology, 461, 51-58. https://doi.org/10.1016/j.jtbi.2018.10.046
Xiao, X., Cheng, X., Chen, G., Mao, Q. and Chou, K.C. (2019) pLoc_bal-mVirus: Predict Subcellular Localization of Multi-Label Virus Proteins by Chou’s General PseAAC and IHTS Treatment to Balance Training Dataset. Medicinal Chemistry, 15, 496-509. https://doi.org/10.2174/1573406415666181217114710
Behbahani, M., Nosrati, M., Moradi, M. and Mohabatkar, H. (2020) Using Chou’s General Pseudo Amino Acid Composition to Classify Laccases from Bacterial and Fungal Sources via Chou’s Five-Step Rule. Applied Biochemistry and Biotechnology, 190, 1035-1048. https://doi.org/10.1007/s12010-019-03141-8
Chen, Y. and Fan, X. (2019) Use Chou’s 5-Steps Rule to Reveal Active Compound and Mechanism of Shuangsheng Pingfei San on Idiopathic Pulmonary Fibrosis. Current Molecular Medicine, 20, 220-230. https://doi.org/10.2174/1566524019666191011160543
Du, X., Diao, Y., Liu, H. and Li, S. (2019) MsDBP: Exploring DNA-Binding Proteins by Integrating Multi-Scale Sequence Information via Chou’s 5-Steps Rule. Journal of Proteome Research, 18, 3119-3132. https://doi.org/10.1021/acs.jproteome.9b00226
Dutta, A., Dalmia, A., A. R, Singh, K.K. and Anand, A. (2019) Using the Chou’s 5-Steps Rule to Predict Splice Junctions with Interpretable Bidirectional Long Short-Term Memory Networks. Computers in Biology and Medicine, 116, Article ID: 103558. https://doi.org/10.1016/j.compbiomed.2019.103558
Hussain, W., Khan, S.D., Rasool, N., Khan, S.A. and Chou, K.C. (2019) SPalmitoylC-PseAAC: A Sequence-Based Model Developed via Chou’s 5-Steps Rule and General PseAAC for Identifying S-Palmitoylation Sites in Proteins. Analytical Biochemistry, 568, 14-23. https://doi.org/10.1016/j.ab.2018.12.019
Hussain, W., Khan, Y.D., Rasool, N., Khan, S.A. and Chou, K.C. (2019) SPrenylC-PseAAC: A Sequence-Based Model Developed via Chou’s 5-Steps Rule and General PseAAC for Identifying S-Prenylation Sites in Proteins. Journal of Theoretical Biology, 468, 1-11. https://doi.org/10.1016/j.jtbi.2019.02.007
Jun, Z. and Wang, S.Y. (2019) Identify Lysine Neddylation Sites Using Bi-Profile Bayes Feature Extraction via the Chou’s 5-Steps Rule and General Pseudo Components. Current Genomics, 20, 592-601. https://doi.org/10.2174/1389202921666191223154629
Khan, S., Khan, M., Iqbal, N., Hussain, T., Khan, S.A. and Chou, K.C. (2020) A Two-Level Computation Model Based on Deep Learning Algorithm for Identification of piRNA and Their Functions via Chou’s 5-Steps Rule. International Journal of Peptide Research and Therapeutics, 26, 795-809. https://doi.org/10.1007/s10989-019-09887-3
Lan, J., Liu, J., Liao, C., Merkler, D.J., Han, Q. and Li, J. (2019) A Study for Therapeutic Treatment against Parkinson’s Disease via Chou’s 5-Steps Rule. Current Topics in Medicinal Chemistry, 19, 2318-2333. http://www.eurekaselect.com/175887/article https://doi.org/10.2174/1568026619666191019111528
Liang, R., Xie, J., Zhang, C., Zhang, M., Huang, H., Huo, H., Cao, X. and Niu, B. (2019) Identifying Cancer Targets Based on Machine Learning Methods via Chou’s 5-Steps Rule and General Pseudo Components. Current Topics in Medicinal Chemistry, 19, 2301-2317. https://doi.org/10.2174/1568026619666191016155543
Liang, Y. and Zhang, S. (2019) Identifying DNase I Hypersensitive Sites Using Multi-Features Fusion and F-Score Features Selection via Chou’s 5-Steps Rule. Biophysical Chemistry, 253, Article ID: 106227. https://doi.org/10.1016/j.bpc.2019.106227
Wiktorowicz, A., Wit, A., Dziewierz, A., Rzeszutko, L., Dudek, D. and Kleczynski, P. (2019) Calcium Pattern Assessment in Patients with Severe Aortic Stenosis via the Chou’s 5-Steps Rule. Current Pharmaceutical Design, 25, 3769-3775. https://doi.org/10.2174/1381612825666190930101258
Yang, L., Lv, Y., Wang, S., Zhang, Q., Pan, Y., Su, D., Lu, Q. and Zuo, Y. (2019) Identifying FL11 Subtype by Characterizing Tumor Immune Microenvironment in Prostate Adenocarcinoma via Chou’s 5-Steps Rule. Genomics, 112, 1500-1515. https://doi.org/10.1016/j.ygeno.2019.08.021
Akmal, M.A., Hussain, W., Rasool, N., Khan, Y.D., Khan, S.A. and Chou, K.C. (2020) Using Chou’s 5-Steps Rule to Predict O-Linked Serine Glycosylation Sites by Blending Position Relative Features and Statistical Moment. IEEE/ACM Transactions on Computational Biology and Bioinformatics, in press. https://doi.org/10.1109/TCBB.2020.2968441
Charoenkwan, P., Schaduangrat, N., Nantasenamat, C., Piacham, T. and Shoombuatong, W. (2020) iQSP: A Sequence-Based Tool for the Prediction and Analysis of Quorum Sensing Peptides via Chou’s 5-Steps Rule and Informative Physicochemical Properties. International Journal of Molecular Sciences, 21, 75. https://doi.org/10.3390/ijms21010075
Charoenkwan, P., Schaduangrat, N., Nantasenamat, C., Piacham, T. and Shoombuatong, W. (2020) Correction: Shoombuatong, W., et al. iQSP: A Sequence-Based Tool for the Prediction and Analysis of Quorum Sensing Peptides via Chou’s 5-Steps Rule and Informative Physicochemical Properties. Int. J. Mol. Sci. 2020, 21, 75. International Journal of Molecular Sciences, 21, 2629. https://doi.org/10.3390/ijms21072629
Chen, Y. and Fan, X. (2020) Use of Chou’s 5-Steps Rule to Reveal Active Compound and Mechanism of Shuangshen Pingfei San on Idiopathic Pulmonary Fibrosis. Current Molecular Medicine, 20, 220-230. https://doi.org/10.2174/1566524019666191011160543
Du, L., Meng, Q., Jiang, H. and Li, Y. (2020) Using Evolutionary Information and Multi-Label Linear Discriminant Analysis to Predict the Subcellular Location of Multi-Site Bacterial Proteins via Chou’s 5-Steps Rule. IEEE Access, 8, 56452-56461. https://doi.org/10.1109/ACCESS.2020.2982160
Dutta, A., Dalmia, A., A. R, Singh, K.K. and Anand, A. (2020) Using the Chou’s 5-Steps Rule to Predict Splice Junctions with Interpretable Bidirectional Long Short-Term Memory Networks. Computers in Biology and Medicine, 116, Article ID: 103558. https://doi.org/10.1016/j.compbiomed.2019.103558
Ju, Z. and Wang, S.Y. (2020) 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, 859-866. https://doi.org/10.1016/j.ygeno.2019.05.027
Kabir, M., Ahmad, S., Iqbal, M. and Hayat, M. (2020) 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, 276-285. https://doi.org/10.1016/j.ygeno.2019.02.006
Lin, W., Xiao, X., Qiu, W. and Chou, K.C. (2020) Use Chou’s 5-Steps Rule to Predict Remote Homology Proteins by Merging Grey Incidence Analysis and Domain Similarity Analysis. Natural Science, 12, 181-198. https://doi.org/10.4236/ns.2020.123016
Yang, L., Lv, Y., Wang, S., Zhang, Q., Pan, Y., Su, D., Lu, Q. and Zuo, Y. (2020) Identifying FL11 Subtype by Characterizing Tumor Immune Microenvironment in Prostate Adenocarcinoma via Chou’s 5-Steps Rule. Genomics, 112, 1500-1515. https://doi.org/10.1016/j.ygeno.2019.08.021
Chou, K.C., Lin, W.Z. and Xiao, X. (2011) Wenxiang: A Web-Server for Drawing Wenxiang Diagrams. Natural Science, 3, 862-865. https://doi.org/10.4236/ns.2011.310111
Zhou, G.P. (2011) The Disposition of the LZCC Protein Residues in Wenxiang Diagram Provides New Insights into the Protein-Protein Interaction Mechanism. Journal of Theoretical Biology, 284, 142-148. https://doi.org/10.1016/j.jtbi.2011.06.006
Zhou, G.P., Chen, D., Liao, S. and Huang, R.B. (2016) Recent Progresses in Studying Helix-Helix Interactions in Proteins by Incorporating the Wenxiang Diagram into the NMR Spectroscopy. Current Topics in Medicinal Chemistry, 16, 581-590. https://doi.org/10.2174/1568026615666150819104617
Chou, K.C. (1993) A Vectorized Sequence-Coupling Model for Predicting HIV Protease Cleavage Sites in Proteins. The Journal of Biological Chemistry, 268, 16938-16948.
Chou, K.C. and Zhang, C.T. (1992) Diagrammatization of Codon Usage in 339 HIV Proteins and Its Biological Implication. AIDS Research and Human Retroviruses, 8, 1967-1976. https://doi.org/10.1089/aid.1992.8.1967
Chou, J.J. (1993) Predicting Cleavability of Peptide Sequences by HIV Protease via Correlation-Angle Approach. Journal of Protein Chemistry, 12, 291-302. https://doi.org/10.1007/BF01028191
Chou, K.C., Tomasselli, A.L., Reardon, I.M. and Heinrikson, R.L. (1996) Predicting HIV Protease Cleavage Sites in Proteins by a Discriminant Function Method. Proteins: Structure, Function, and Bioinformatics, 24, 51-72. https://doi.org/10.1002/(SICI)1097-0134(199601)24:1 3.0.CO;2-R
Chou, K.C. and Zhang, C.T. (1993) Studies on the Specificity of HIV Protease: An Application of Markov Chain Theory. Journal of Protein Chemistry, 12, 709-724. https://doi.org/10.1007/BF01024929
Chou, K.C., Zhang, C.T. and Kezdy, F.J. (1993) A Vector Approach to Predicting HIV Protease Cleavage Sites in Proteins. Proteins: Structure, Function, and Bioinformatics, 16, 195-204. https://doi.org/10.1002/prot.340160206
Chou, K.C. and Forsen, S. (1980) Graphical Rules for Enzyme-Catalyzed Rate Laws. Biochemical Journal, 187, 829-835. https://doi.org/10.1042/bj1870829
Chou, K.C. (1981) A New Graphical Rule for Rate Laws of Enzyme Reactions with Branched Pathways. Canadian Journal of Biochemistry, 59, 757-761. https://doi.org/10.1139/o81-105
Chou, K.C., Carter, R.E. and Forsen, S. (1981) A New Graphical Method for Deriving Rate Equations for Complicated Mechanisms. Chemica Scripta, 18, 82-86.
Chou, K.C. and Forsen, S. (1981) Graphical Rules of Steady-State Reaction Systems. Canadian Journal of Chemistry, 59, 737-755. https://doi.org/10.1139/v81-107
Chou, K.C. and Liu, W.M. (1981) Graphical Rules for Non-Steady State Enzyme Kinetics. Journal of Theoretical Biology, 91, 637-654. https://doi.org/10.1016/0022-5193(81)90215-0
Chou, K.C. (1983) Advances in Graphical Methods of Enzyme Kinetics. Biophysical Chemistry, 17, 51-55. https://doi.org/10.1016/0301-4622(83)87013-6
Chou, K.C. (1989) Graphic Rules in Steady and Non-Steady Enzyme Kinetics. The Journal of Biological Chemistry, 264, 12074-12079.
Chou, K.C. (1993) Graphic Rule for Non-Steady-State Enzyme Kinetics and Protein Folding Kinetics. Journal of Mathematical Chemistry, 12, 97-108. https://doi.org/10.1007/BF01164628
Chou, K.C. (2010) Graphic Rule for Drug Metabolism Systems. Current Drug Metabolism, 11, 369-378. https://doi.org/10.2174/138920010791514261
Chou, K.C., Chen, N.Y. and Forsen, S. (1981) The Biological Functions of Low-Frequency Phonons: 2. Cooperative Effects. Chemica Scripta, 18, 126-132.
Chou, K.C., Li, T.T. and Forsen, S. (1980) The Critical Spherical Shell in Enzymatic Fast Reaction Systems. Biophysical Chemistry, 12, 265-269. https://doi.org/10.1016/0301-4622(80)80003-2
Chou, K.C. and Forsen, S. (1980) Diffusion-Controlled Effects in Reversible Enzymatic Fast Reaction System: Critical Spherical Shell and Proximity Rate Constants. Biophysical Chemistry, 12, 255-263. https://doi.org/10.1016/0301-4622(80)80002-0
Li, T.T., Chou, K.C. and Forsen, S. (1980) The Flow of Substrate Molecules in Fast Enzyme-Catalyzed Reaction Systems. Chemica Scripta, 16, 192-196.
Chou, K.C., Forsen, S. and Zhou, G.Q. (1980) Three Schematic Rules for Deriving Apparent Rate Constants. Chemica Scripta, 16, 109-113.
Chou, K.C. (1983) Low-Frequency Vibrations of Helical Structures in Protein Molecules. Biochemical Journal, 209, 573-580. https://doi.org/10.1042/bj2090573
Chou, K.C. (1983) Identification of Low-Frequency Modes in Protein Molecules. Biochemical Journal, 215, 465-469. https://doi.org/10.1042/bj2150465
Zhou, G.P. and Deng, M.H. (1984) An Extension of Chou’s Graphic Rules for Deriving Enzyme Kinetic Equations to Systems Involving Parallel Reaction Pathways. Biochemical Journal, 222, 169-176. https://doi.org/10.1042/bj2220169
Chou, K.C. (1984) Biological Functions of Low-Frequency Vibrations (Phonons). III. Helical Structures and Microenvironment. Biophysical Journal, 45, 881-889. https://doi.org/10.1016/S0006-3495(84)84234-4
Chou, K.C. (1984) The Biological Functions of Low-Frequency Phonons. 4. Resonance Effects and Allosteric Transition. Biophysical Chemistry, 20, 61-71. https://doi.org/10.1016/0301-4622(84)80005-8
Chou, K.C. (1984) Low-Frequency Vibrations of DNA Molecules. Biochemical Journal, 221, 27-31. https://doi.org/10.1042/bj2210027
Chou, K.C. (1985) Low-Frequency Motions in Protein Molecules: Beta-Sheet and Beta-Barrel. Biophysical Journal, 48, 289-297. https://doi.org/10.1016/S0006-3495(85)83782-6
Chou, K.C. (1985) Prediction of a Low-Frequency Mode in Bovine Pancreatic Trypsin Inhibitor Molecule. International Journal of Biological Macromolecules, 7, 77-80. https://doi.org/10.1016/0141-8130(85)90035-2
Chou, K.C. and Kiang, Y.S. (1985) The Biological Functions of Low-Frequency Phonons: 5. A Phenomenological Theory. Biophysical Chemistry, 22, 219-235. https://doi.org/10.1016/0301-4622(85)80045-4
Chou, K.C. (1986) Origin of Low-Frequency Motion in Biological Macromolecules: A View of Recent Progress of Quasi-Continuity Model. Biophysical Chemistry, 25, 105-116. https://doi.org/10.1016/0301-4622(86)87001-6
Chou, K.C. (1987) The Biological Functions of Low-Frequency Phonons: 6. A Possible Dynamic Mechanism of Allosteric Transition in Antibody Molecules. Biopolymers, 26, 285-295. https://doi.org/10.1002/bip.360260209
Chou, K.C. (1988) Review: Low-Frequency Collective Motion in Biomacromolecules and Its Biological Functions. Biophysical Chemistry, 30, 3-48. https://doi.org/10.1016/0301-4622(88)85002-6
Chou, K.C. and Maggiora, G.M. (1988) The Biological Functions of Low-Frequency Phonons: 7. The Impetus for DNA to Accommodate Intercalators. British Polymer Journal, 20, 143-148. https://doi.org/10.1002/pi.4980200209
Chou, K.C. (1989) Low-Frequency Resonance and Cooperativity of Hemoglobin. Trends in Biochemical Sciences, 14, 212-213. https://doi.org/10.1016/0968-0004(89)90026-1
Chou, K.C., Maggiora, G.M. and Mao, B. (1989) Quasi-Continuum Models of Twist-Like and Accordion-Like Low-Frequency Motions in DNA. Biophysical Journal, 56, 295-305. https://doi.org/10.1016/S0006-3495(89)82676-1
Chou, K.C. (1990) Review: Applications of Graph Theory to Enzyme Kinetics and Protein Folding Kinetics. Steady and Non-Steady State Systems. Biophysical Chemistry, 35, 1-24. https://doi.org/10.1016/0301-4622(90)80056-D
Althaus, I.W., Chou, J.J., Gonzales, A.J., Diebel, M.R., Chou, K.C., Kezdy, F.J., Romero, D.L., Aristoff, P.A., Tarpley, W.G. and Reusser, F. (1993) Steady-State Kinetic Studies with the Non-Nucleoside HIV-1 Reverse Transcriptase Inhibitor U-87201E. The Journal of Biological Chemistry, 268, 6119-6124.
Althaus, I.W., Gonzales, A.J., Chou, J.J., Diebel, M.R., Chou, K.C., Kezdy, F.J., Romero, D.L., Aristoff, P.A., Tarpley, W.G. and Reusser, F. (1993) The Quinoline U-78036 Is a Potent Inhibitor of HIV-1 Reverse Transcriptase. The Journal of Biological Chemistry, 268, 14875-14880.
Althaus, I.W., Chou, J.J., Gonzales, A.J., Diebel, M.R., Chou, K.C., Kezdy, F.J., Romero, D.L., Aristoff, P.A., Tarpley, W.G. and Reusser, F. (1993) Kinetic Studies with the Nonnucleoside HIV-1 Reverse Transcriptase Inhibitor U-88204E. Biochemistry, 32, 6548-6554. https://doi.org/10.1021/bi00077a008
Althaus, I.W., Chou, J.J., Gonzales, A.J., Diebel, M.R., Chou, K.C., Kezdy, F.J., Romero, D.L., Aristoff, P.A., Tarpley, W.G. and Reusser, F. (1994) Steady-State Kinetic Studies with the Polysulfonate U-9843, an HIV Reverse Transcriptase Inhibitor. Experientia, 50, 23-28. https://doi.org/10.1007/BF01992044
Althaus, I.W., Chou, J.J., Gonzales, A.J., Diebel, M.R., Chou, K.C., Kezdy, F.J., Romero, D.L., Thomas, R.C., Aristoff, P.A., Tarpley, W.G. and Reusser, F. (1994) Kinetic Studies with the Non-Nucleoside Human Immunodeficiency Virus Type-1 Reverse Transcriptase Inhibitor U-90152E. Biochemical Pharmacology, 47, 2017-2028. https://doi.org/10.1016/0006-2952(94)90077-9
Chou, K.C., Kezdy, F.J. and Reusser, F. (1994) Review: Kinetics of Processive Nucleic Acid Polymerases and Nucleases. Analytical Biochemistry, 221, 217-230. https://doi.org/10.1006/abio.1994.1405
Chou, K.C., Zhang, C.T. and Maggiora, G.M. (1994) Solitary Wave Dynamics as a Mechanism for Explaining the Internal Motion during Microtubule Growth. Biopolymers, 34, 143-153. https://doi.org/10.1002/bip.360340114
Althaus, I.W., Chou, K.C., Franks, K.M., Diebel, M.R., Kezdy, F.J., Romero, D.L., Thomas, R.C., Aristoff, P.A., Tarpley, W.G. and Reusser, F. (1996) The Benzylthio-Pyrididine U-31,355, a Potent Inhibitor of HIV-1 Reverse Transcriptase. Biochemical Pharmacology, 51, 743-750. https://doi.org/10.1016/0006-2952(95)02390-9
Liu, H., Wang, M. and Chou, K.C. (2005) Low-Frequency Fourier Spectrum for Predicting Membrane Protein Types. Biochemical and Biophysical Research Communications (BBRC), 336, 737-739. https://doi.org/10.1016/j.bbrc.2005.08.160
Gordon, G. (2007) Designed Electromagnetic Pulsed Therapy: Clinical Applications. Journal of Cellular Physiology, 212, 579-582. https://doi.org/10.1002/jcp.21025
Andraos, J. (2008) Kinetic Plasticity and the Determination of Product Ratios for Kinetic Schemes Leading to Multiple Products without Rate Laws: New Methods Based on Directed Graphs. Canadian Journal of Chemistry, 86, 342-357. https://doi.org/10.1139/v08-020
Chou, K.C. and Shen, H.B. (2009) FoldRate: A Web-Server for Predicting Protein Folding Rates from Primary Sequence. The Open Bioinformatics Journal, 3, 31-50. https://doi.org/10.2174/1875036200903010031
Shen, H.B., Song, J.N. and Chou, K.C. (2009) Prediction of Protein Folding Rates from Primary Sequence by Fusing Multiple Sequential Features. Journal of Biomedical Science and Engineering (JBiSE), 2, 136-143. https://doi.org/10.4236/jbise.2009.23024
Wang, J.F. and Chou, K.C. (2009) Insight into the Molecular Switch Mechanism of Human Rab5a from Molecular Dynamics Simulations. Biochemical and Biophysical Research Communications (BBRC), 390, 608-612. https://doi.org/10.1016/j.bbrc.2009.10.014
Gordon, G. (2008) 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, 152-156. https://doi.org/10.4236/jbise.2008.13025
Madkan, A., Blank, M., Elson, E., Chou, K.C., Geddis, M.S. and Goodman, R. (2009) Steps to the Clinic with ELF EMF. Natural Science, 1, 157-165. https://doi.org/10.4236/ns.2009.13020
Lian, P., Wei, D.Q., Wang, J.F. and Chou, K.C. (2011) An Allosteric Mechanism Inferred from Molecular Dynamics Simulations on Phospholamban Pentamer in Lipid Membranes. PLoS ONE, 6, e18587. https://doi.org/10.1371/journal.pone.0018587
Liao, Q.H., Gao, Q.Z., Wei, J. and Chou, K.C. (2011) Docking and Molecular Dynamics Study on the Inhibitory Activity of Novel Inhibitors on Epidermal Growth Factor Receptor (EGFR). Medicinal Chemistry, 7, 24-31. https://doi.org/10.2174/157340611794072698
Wang, J.F. and Chou, K.C. (2012) Recent Advances in Computational Studies on Influenza a Virus M2 Proton Channel. Mini Reviews in Medicinal Chemistry, 12, 971-978. https://doi.org/10.2174/138955712802762275
Zhang, T., Wei, D.Q. and Chou, K.C. (2012) A Pharmacophore Model Specific to Active Site of CYP1A2 with a Novel Molecular Modeling Explorer and CoMFA. Medicinal Chemistry, 8, 198-207. https://doi.org/10.2174/157340612800493601
Jia, J., Liu, Z., Xiao, X. and Chou, K.C. (2015) iPPI-Esml: An Ensemble Classifier for Identifying the Interactions of Proteins by Incorporating Their Physicochemical Properties and Wavelet Transforms into PseAAC. Journal of Theoretical Biology, 377, 47-56. https://doi.org/10.1016/j.jtbi.2015.04.011
Jia, J., Liu, Z., Xiao, X., Liu, B. and Chou, K.C. (2016) Identification of Protein-Protein Binding Sites by Incorporating the Physicochemical Properties and Stationary Wavelet Transforms into Pseudo Amino Acid Compo sition (iPPBS-PseAAC). Journal of Biomolecular Structure and Dynamics (JBSD), 34, 1946-1961. https://doi.org/10.1080/07391102.2015.1095116
Chou, K.C. (2019) Proposing Pseudo Amino Acid Components Is an Important Milestone for Proteome and Genome Analyses. International Journal for Peptide Research and Therapeutics (IJPRT), 26, 1085-1098. https://doi.org/10.1007/s10989-019-09910-7
Chou, K.C. (2019) Artificial Intelligence (AI) Tools Constructed via the 5-Steps Rule for Predicting Post-Translational Modifications. Trends in Artificial Intelligence (TIA), 3, 60-74. https://doi.org/10.36959/643/304
Chou, K.C. (2019) Impacts of Pseudo Amino Acid Components and 5-Steps Rule to Proteomics and Proteome Analysis. Current Topics in Medicinal Chemistry, 19, 2283-2300. https://doi.org/10.2174/1568026619666191018100141
Chou, K.C. (2019) An Insightful Recollection for Predicting Protein Subcellular Locations in Multi-Label Systems. Genomics. (In Press) https://doi.org/10.1016/j.ygeno.2019.08.022
Chou, K.C. (2019) Advance in Predicting Subcellular Localization of Multi-Label Proteins and Its Implication for Developing Multi-Target Drugs. Current Medicinal Chemistry, 26, 4918-4943. https://doi.org/10.2174/0929867326666190507082559
Shao, Y.T. and Chou, K.C. (2020) pLoc_Deep-mAnimal: A Novel Deep CNN-BLSTM Network to Predict Subcellular Localization of Animal Proteins. Natural Science, 12, 281-291. https://doi.org/10.4236/ns.2020.125024
Shao, Y.T. and Chou, K.C. (2020) pLoc_Deep-mEuk: Predict Subcellular Localization of Eukaryotic Proteins by Deep Learning. Natural Science, 12, 400-428. https://doi.org/10.4236/ns.2020.126034
Liu, X.X. and Chou, K.C. (2020) pLoc_Deep-mGneg: Predict Subcellular Localization of Gram Negative Bacterial Proteins by Deep Learning. Advances in Bioscience and Biotechnology (ABB), 11, 141-152. https://doi.org/10.4236/abb.2020.115011
Shao, Y.T., Liu, X.X., Lu, Z. and Chou, K.C. (2020) pLoc_Deep-mHum: Predict Subcellular Localization of Human Proteins by Deep Learning. Natural Science, in press, 1-31.
Shao, Y.T., Liu, X.X., Lu, Z. and Chou, K.C. (2020) pLoc_Deep-mPlant: Predict Subcellular Localization of Plant Proteins by Deep Learning. Natural Science, 12, 237-247. https://doi.org/10.4236/ns.2020.125021
Shao, Y.H. and Chou, K.C. (2020) pLoc_Deep-mVirus: A CNN Model for Predicting Subcellular Localization of Virus Proteins by Deep Learning. Natural Science, 12, 388-399. https://doi.org/10.4236/ns.2020.126033
Chou, K.C. (2020) Showcase to Illustrate How the Web-Server iATC_Deep-mISF Is Working. Global Journal of Science Frontier Research: G Bio-Tech & Genetics, 20, 1-3.
K.-C. Chou (2020), Showcase to Illustrate How the Web-Server iKcr-PseEns Is Working. Journal of Medical Care Research and Review, 3, 331-347.
Chou, K.C. (2020) Showcase to Illustrate How the Web-Server iPTM-mLys Is Working. Infotext Journal of Infectious Diseases and Therapy (IJID), 1, 1-16.
Chou, K.C. (2020) Showcase to Illustrate How the Web-Server iRNA-Methyl Is Working. Journal of Molecular Genetics, 3, 1-7.
Chou, K.C. (2020) Showcase to Illustrate How the Web-Server iSNO-AAPair Is Working. Journal of Genetics and Genomics, 24, 18156-18160.
Chou, K.C. (2020) Showcase to Illustrate How the Web-Server pLoc_Deep-mAnimal Is Working. American Journal of Virology & Disease, 2, 1-2.
Chou, K.C. (2020) Showcase to Illustrate How the Web-Server pLoc_Deep-mGneg Is Working. Clinical and Medical Case Report Journal, 1, 1-2.
Chou, K.C. (2020) Showcase to Illustrate How the Web-Server pLoc_Deep-mPlant Is Working. Integrative Molecular Biology and Biotechnology, 1, 1-2.
Chou, K.C. (2020) Showcase to Illustrate How the Web-Server pLoc_Deep-mVirus Is Working. Clinical Research and Trials, 6, 1-2.
K.-C. Chou (2020) Showcase to Illustrate How the Web-Server pSuc-Lys Is Working. Social Sciences and Humanities Open Access Journal, 1, 1-3.
Chou, K.C. (2020) Showcase to Illustrate How the Webserver pLoc_bal-mEuk Is Working. Biomedical Journal of Scientific & Technical Research, 24, Article ID: 18156. https://doi.org/10.26717/BJSTR.2020.24.004033