*Corresponding Author: Kuo-Chen Chou, Gordon Life Science Institute, Boston, Massachusetts 02478, United States of America
Citation: Kuo-Chen Chou, (2021) The Benefits of Door Open for a Country. J. Scientific Research and Biomedical Informatics, 2(2); Doi:10.31579/jsrbi.2021/011
Copyright: © 2021 Kuo-Chen Chou, This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Received: 19 February 2021 | Accepted: 24 March 2021 | Published: 30 March 2021
Keywords: deng xiaoping; mao zedong and buddha; cross the river by feeling the stones; cats able to catch mice are good ones regardless their color; no one is perfect, and no gold is really pure
Abstract
In this paper, the amazing and fantastic benefits by taking the door open policy have been presented. It is within (1979-1949) = 30 years, China has become from a very poor country to the second richest country only next to USA. It is anticipated China will become the richest country in 2030.
Introduction
After the notorious “Cultural Revolution” developed by Mao Zedong, the dictator and head of China, had been defeated, China was under the leadership of Deng Xiaoping. He adopted the door-opening policy with the philosophy: (1) “Cross the river by feeling the stones meaning “摸著石头过河”, (2) “No one is perfect, and no gold is really pure” meaning “人无完人, 金无足赤”, and (3) Cats able to catch mice are certainly good regardless of their color” meaning “不管白猫黑猫,能够捕捉老鼠就是好猫”. Ever since then, China has been starting economically booming, and has become the second richest country next to USA only. It is anticipated that China will be superior to USA in about 2030.
Growing-up and strongly established
As a consequence of the “door open” theory, Prof. Dr. Chou was invited by Professor Sture Forsén, the then “Chairman of Nobel Prize Committee”, to work in Chemical Center of Lund University as a Visiting Professor. Deng Xiaoping’s policy can stimulate a lot of great creativities, as indicated by a lot of achievements. Listed below are just a few.
- Proposing graphical rules in molecular biology.
- Proposing pseudo amino acid composition [1-16].
- Proposing Pseudo K-Tuple Nucleotide Composition [17-20].
- Proposing Pseudo-in-one [21, 22].
- Proposing 5-steps rule [7, 8, 23-63, 64 , 65-95].
- Proposing “The Biological Functions of Low-Frequency Phonons, [96-105].
III. CONCLUSIVE REMARKS
It is really awesome and amazing to adopt the Door-opening policy to achieve such great achievements during so short period of time, fully indicating “the community with a shared future for mankind” (i.e., “人类命运共同体”) and “Multilateralism” (i.e., “多边主义” by Xi Jinping is indeed very correct and wise.
References
- [1] K.C. Chou, Prediction of protein cellular attributes using pseudo amino acid composition, PROTEINS: Structure, Function, and Genetics (Erratum: ibid., 2001, Vol.44, 60), 43 (2001) 246-255.
View at Publisher |
View at Google Scholar
- [2] K.C. Chou, Y.D. Cai, Predicting protein quaternary structure by pseudo amino acid composition, Proteins: Struct., Funct., Genet., 53 (2003) 282-289.
View at Publisher |
View at Google Scholar
- [3] 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.
View at Publisher |
View at Google Scholar
- [4] 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.
View at Publisher |
View at Google Scholar
- [5] K.C. Chou, Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes, Bioinformatics, 21 (2005) 10-19.
View at Publisher |
View at Google Scholar
- [6] K.C. Chou, Pseudo amino acid composition and its applications in bioinformatics, proteomics and system biology, Current Proteomics, 6 (2009) 262-274.
View at Publisher |
View at Google Scholar
- [7] K.C. Chou, Some remarks on protein attribute prediction and pseudo amino acid composition (50th Anniversary Year Review, 5-steps rule), J. Theor. Biol., 273 (2011) 236-247.
View at Publisher |
View at Google Scholar
- [8] 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.
View at Publisher |
View at Google Scholar
- [9] 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.
View at Publisher |
View at Google Scholar
- [10] K.C. Chou, An Insightful 20-Year Recollection Since the Birth of Pseudo Amino Acid Components, JOURNAL OF MATHEMATICS, STATISTICS AND COMPUTING, 1 (2019) 5-16.
View at Publisher |
View at Google Scholar
- [11] K.C. Chou, How the Artificial Intelligence Tool iRNA-PseU is Working in Predicting the RNA Pseudouridine Sites, Biomed J Sci & Tech Res https://doi.org/10.26717/BJSTR.2020.24.004016, 24 (2020).
View at Publisher |
View at Google Scholar
- [12] K.C. Chou, An insightful 20-year recollection since the birth of pseudo amino acid components, Amino Acids, in press (2020).
View at Publisher |
View at Google Scholar
- [13] K.C. Chou, The Significant and Profound Impacts of Chou's Pseudo Amino Acid Composition or PseAAC, Natural Acience, 12 (2020) 647-658.
View at Publisher |
View at Google Scholar
- [14] K.C. Chou, The Significant and Profound Impacts of Pseudo K-Tuple Nucleotide Composition, Voice of the Publisher (VP), 6 (2020) 91-101.
View at Publisher |
View at Google Scholar
- [15] K.C. Chou, Analyze the Role of “Pseudo Amino Acid Composition” in Stimulating the Drug Development. Annual Cas Rep Rev: ACRR-161, Annals of Case Reports & Reviews (ACRR), (2020).
View at Publisher |
View at Google Scholar
- [16] K.C. Chou, The Significant and Profound Impacts of Pseudo K-Tuple Nucleotide Composition, Archives of Molecular Medicine Journal (Arch Mol Med J), Vol.1 (2020) page 1-4.
View at Publisher |
View at Google Scholar
- [17] H. Lin, E.Z. Deng, H. Ding, W. Chen, K.C. Chou, iPro54-PseKNC: a sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition, Nucleic Acids Res., 42 (2014) 12961-12972.
View at Publisher |
View at Google Scholar
- [18] W. Chen, T.Y. Lei, D.C. Jin, H. Lin, K.C. Chou, PseKNC: a flexible web-server for generating pseudo K-tuple nucleotide composition, Anal. Biochem., 456 (2014) 53-60.
View at Publisher |
View at Google Scholar
- [19] S.H. Guo, E.Z. Deng, L.Q. Xu, H. Ding, H. Lin, W. Chen, K.C. Chou, iNuc-PseKNC: a sequence-based predictor for predicting nucleosome positioning in genomes with pseudo k-tuple nucleotide composition, Bioinformatics, 30 (2014) 1522-1529.
View at Publisher |
View at Google Scholar
- [20] B. Liu, L. Fang, S. Wang, X. Wang, H. Li, K.C. Chou, Identification of microRNA precursor with the degenerate K-tuple or Kmer strategy, Journal of Theoretical Biology, 385 (2015) 153-159.
View at Publisher |
View at Google Scholar
- [21] B. Liu, F. Liu, X. Wang, J. Chen, L. Fang, K.C. Chou, Pse-in-One: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences, Nucleic Acids Res., 43 (2015) W65-W71.
View at Publisher |
View at Google Scholar
- [22] B. Liu, H. Wu, K.C. Chou, Pse-in-One 2.0: An improved package of web servers for generating various modes of pseudo components of DNA, RNA, and protein sequences, Natural Science, 9 (2017) 67-91.
View at Publisher |
View at Google Scholar
- [23] O. Barukab, Y.D. Khan, S.A. Khan, K.C. Chou, iSulfoTyr-PseAAC: Identify tyrosine sulfation sites by incorporating statistical moments via Chou's 5-steps rule and pseudo components Current Genomics, 20 (2019) 306-320.
View at Publisher |
View at Google Scholar
- [24] Y. Chen, X. Fan, Use Chou's 5-Steps Rule to Reveal Active Compound and Mechanism of Shuangsheng Pingfei San on Idiopathic Pulmonary Fibrosis, Current Molecular Medicine, 19 (2019) 511-563.
View at Publisher |
View at Google Scholar
- [25] K.C. Chou, Recent progresses in predicting protein subcellular localization with artificial intelligence tools developed via the 5-steps rule, Medicinal Chemistry, Submitted (2019).
View at Publisher |
View at Google Scholar
- [26] 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.
View at Publisher |
View at Google Scholar
- [27] 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.
View at Publisher |
View at Google Scholar
- [28] K.C. Chou, An Insightful 10-year Recollection Since the Emergence of the 5-steps Rule, Current Pharmaceutical Design, 25 (2019) 4223-4234.
View at Publisher |
View at Google Scholar
- [29] X. Du, Y. Diao, H. Liu, S. Li, MsDBP: Exploring DNA-binding Proteins by Integrating Multi-scale Sequence Information via Chou's 5-steps Rule, Journal of Proteome Research, 18 (2019) 3119-3132.
View at Publisher |
View at Google Scholar
- [30] A. Dutta, A. Dalmia, A. R, K.K. Singh, A. Anand, Using the Chou's 5-steps rule to predict splice junctions with interpretable bidirectional long short-term memory networks, Comput Biol Med, 116 (2019) 103558.
View at Publisher |
View at Google Scholar
- [31] 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.
View at Publisher |
View at Google Scholar
- [32] 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.
View at Publisher |
View at Google Scholar
- [33] S. Ilyas, W. Hussain, A. Ashraf, Y.D. Khan, S.A. Khan, K.C. Chou, iMethylK-PseAAC: Improving accuracy for lysine methylation sites identification by incorporating statistical moments and position relative features into general PseAAC via Chou’s 5-steps rule, Current Genomics, doi: 10.2174/1389202920666190809095206 (2019).
View at Publisher |
View at Google Scholar
- [34] 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.
View at Publisher |
View at Google Scholar
- [35] S. Khan, M. Khan, N. Iqbal, T. Hussain, S.A. Khan, K.C. Chou, A Two-Level Computation Model Based on Deep Learning Algorithm for Identification of piRNA and Their Functions via Chou's 5-Steps Rule Human Genetics 19 (2019) 756-799.
View at Publisher |
View at Google Scholar
- [36] J. Lan, J. Liu, C. Liao, D.J. Merkler, Q. Han, J. Li, A Study for Therapeutic Treatment against Parkinson’s Disease via Chou's 5-steps Rule, Current Topics in Medicinal Chemistry, 19 (2019) 2318-2333.
View at Publisher |
View at Google Scholar
- [37] 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.
View at Publisher |
View at Google Scholar
- [38] Y. Liang, S. Zhang, Identifying DNase I hypersensitive sites using multi-features fusion and F-score features selection via Chou's 5-steps rule, Biophys Chem, 253 (2019) 106227.
View at Publisher |
View at Google Scholar
- [39] A. Wiktorowicz, A. Wit, A. Dziewierz, L. Rzeszutko, D. Dudek, P. Kleczynski, Calcium Pattern Assessment in Patients with Severe Aortic Stenosis Via the Chou's 5-Steps Rule, Current Pharmaceutical Design 25 (2019) 6-31.
View at Publisher |
View at Google Scholar
- [40] L. Yang, Y. Lv, S. Wang, Q. Zhang, Y. Pan, D. Su, Q. Lu, Y. Zuo, Identifying FL11 subtype by characterizing tumor immune microenvironment in prostate adenocarcinoma via Chou's 5-steps rule, Genomics, 112 (2019) 1500-1515.
View at Publisher |
View at Google Scholar
- [41] M.A. Akmal, W. Hussain, N. Rasool, Y.D. Khan, S.A. Khan, K.C. Chou, Using Chou's 5-steps rule to predict O-linked serine glycosylation sites by blending position relative features and statistical moment, IEEE/ACM Trans Comput Biol Bioinform, PP (2020).
View at Publisher |
View at Google Scholar
- [42] H. Bouziane, A. Chouarfia, Use of Chou's 5-steps rule to predict the subcellular localization of gram-negative and gram-positive bacterial proteins by multi-label learning based on gene ontology annotation and profile alignment, J Integr Bioinform, (2020).
View at Publisher |
View at Google Scholar
- [43] P. Charoenkwan, N. Schaduangrat, C. Nantasenamat, T. Piacham, W. Shoombuatong, 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. , 21 (2020) 75.
View at Publisher |
View at Google Scholar
- [44] P. Charoenkwan, N. Schaduangrat, C. Nantasenamat, T. Piacham, W. Shoombuatong, 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, Int J Mol Sci, 21 (2020).
View at Publisher |
View at Google Scholar
- [45] Y. Chen, X. Fan, Use of Chou's 5-Steps Rule to Reveal Active Compound and Mechanism of Shuangshen Pingfei San on Idiopathic Pulmonary Fibrosis, Curr Mol Med, 20 (2020) 220-230.
View at Publisher |
View at Google Scholar
- [46] K.C. Chou, Proposing 5-Steps Rule Is a Notable Milestone for Studying Molecular Biology, Natural Science, 12 (2020) 74-79.
View at Publisher |
View at Google Scholar
- [47] K.C. Chou, The Significant and Profound Impacts of Chou’s 5-Steps Rule, Natural Science, 12 (2020) 633-637.
View at Publisher |
View at Google Scholar
- [48] K.C. Chou, Analyze the Role of the “5-Steps Rule” Guidelines in Stimulating the Drug Development (Short Communication), Scholarly Journal of Food and Nutrition (SJFN), 3 (2020) 385-386.
View at Publisher |
View at Google Scholar
- [49] L. Du, Q. Meng, H. Jiang, Y. Li, 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 (2020) 56452-56461.
View at Publisher |
View at Google Scholar
- [50] A. Dutta, A. Dalmia, A. R, K.K. Singh, A. Anand, Using the Chou's 5-steps rule to predict splice junctions with interpretable bidirectional long short-term memory networks, Comput Biol Med, 116 (2020) 103558.
View at Publisher |
View at Google Scholar
- [51] 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.
View at Publisher |
View at Google Scholar
- [52] 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.
View at Publisher |
View at Google Scholar
- [53] C. Koyunoğlu, Use Chou's 5-Steps Rule to Reveal Why SARS+ MERS= COVID-19, J Biochem Analyt Stud, 2020, (2020).
View at Publisher |
View at Google Scholar
- [54] W. Lin, X. Xiao, W. Qiu, K.C. Chou, Use Chou's 5-Steps Rule to Predict Remote Homology Proteins by Merging Grey Incidence Analysis and Domain Similarity Analysis, Natural Science, 12 (2020) 181-198.
View at Publisher |
View at Google Scholar
- [55] D. Nguyen, T. Ho-Quang, L. Nguyen Quoc Khanh, V. Dinh-Phan, Y.Y. Ou, Use Chou's 5-steps rule with different word embedding types to boost performance of electron transport protein prediction model, IEEE/ACM Trans Comput Biol Bioinform, PP (2020).
View at Publisher |
View at Google Scholar
- [56] R.P. Pandey, S. Kumar, S. Ahmad, A. Vibhuti, V.S. Raj, A.K. Verma, P. Sharma, E. Leal, Use Chou's 5-steps rule to evaluate protective efficacy induced by antigenic proteins of Mycobacterium tuberculosis encapsulated in chitosan nanoparticles, Life Sci., 256 (2020) 117961.
View at Publisher |
View at Google Scholar
- [57] T. Roy, P. Bhattacharjee, A LabVIEW-based real-time modeling approach via Chou's 5-steps rule for detection of abnormalities in cancer cells, Gene Reports, (2020) 100788.
View at Publisher |
View at Google Scholar
- [58] H. Vundavilli, A. Datta, C. Sima, J. Hua, R. Lopes, M. Bittner, Using Chou's 5-steps rule to Model Feedback in Lung Cancer IEEE Journal of Biomedical and Health Informatics, 21 (2020) 1-24.
View at Publisher |
View at Google Scholar
- [59] L. Yang, Y. Lv, S. Wang, Q. Zhang, Y. Pan, D. Su, Q. Lu, Y. Zuo, Identifying FL11 subtype by characterizing tumor immune microenvironment in prostate adenocarcinoma via Chou's 5-steps rule, Genomics, 112 (2020) 1500-1515.
View at Publisher |
View at Google Scholar
- [60] S. Zhang, T. Xue, Use Chou’s 5-steps rule to identify DNase I hypersensitive sites via dinucleotide property matrix and extreme gradient boosting, Molecular Genetics and Genomics, 295 (2020).
View at Publisher |
View at Google Scholar
- [61] S. Zhang, T. Xue, Use Chou's 5-steps rule to identify DNase I hypersensitive sites via dinucleotide property matrix and extreme gradient boosting, Molecular genetics and genomics : MGG, (2020).
View at Publisher |
View at Google Scholar
- [62] Z. Zhang, L. Wang, Using Chou's 5-steps rule to identify N(6)-methyladenine sites by ensemble learning combined with multiple feature extraction methods, J. Biomol. Struct. Dyn., (2020) 1-11.
View at Publisher |
View at Google Scholar
- [63] X.F. Zhao, Z. Min, X. Wei, Y. Ju, Using the Chou's 5-steps rule, Transient Overexpression Technique, Subcellular Location, and Bioinformatic Analysis to verify the Function of Vitis vinifera O-methyltranferase 3 (VvOMT3) Protein, Plant Physiology and Biochemistry, 151 (2020) 621-629.
View at Publisher |
View at Google Scholar
- [64] H. Wang, Y. Ding, J. Tang, Q. Zou, F. Guo, Identify RNA-associated subcellular localizations based on multi-label learning using Chou's 5-steps rule, BMC Genomics, 22 (2021) 22-56.
View at Publisher |
View at Google Scholar
- [65] 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.
View at Publisher |
View at Google Scholar
- [66] T.A. Jones, S. Thirup, Using known substructures in protein model building and crystallography, EMBO J., 5 (1986) 819-822.
View at Publisher |
View at Google Scholar
- [67] J.M. Parker, D. Guo, R.S. Hodges, New hydrophilicity scale derived from high-performance liquid chromatography peptide retention data: correlation of predicted surface residues with antigenicity and X-ray-derived accessible sites, Biochemistry, 25 (1986) 5425-5432.
View at Publisher |
View at Google Scholar
- [68] K.C. Chou, Graphic rules in steady and non-steady enzyme kinetics, J. Biol. Chem., 264 (1989) 12074-12079.
View at Publisher |
View at Google Scholar
- [69] 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.
View at Publisher |
View at Google Scholar
- [70] K.C. Chou, Graphic rule for non-steady-state enzyme kinetics and protein folding kinetics, Journal of Mathematical Chemistry, 12 (1993) 97-108.
View at Publisher |
View at Google Scholar
- [71] C.T. Zhang, K.C. Chou, Graphic analysis of codon usage strategy in 1490 human proteins, J. Protein Chem., 12 (1993) 329-335.
View at Publisher |
View at Google Scholar
- [72] P.J. Artymiuk, A.R. Poirrette, H.M. Grindley, D.W. Rice, P. Willett, A graph-theoretic approach to the identification of three-dimensional patterns of amino acid side-chains in protein structures, J. Mol. Biol., 243 (1994) 327-344.
View at Publisher |
View at Google Scholar
- [73] D. Fischer, H. Wolfson, S.L. Lin, R. Nussinov, Three-dimensional, sequence order-independent structural comparison of a serine protease against the crystallographic database reveals active site similarities: potential implications to evolution and to protein folding, Protein Science, 3 (1994) 769-778.
View at Publisher |
View at Google Scholar
- [74] J.P. Hays, S.H. Myint, PCR sequencing of the spike genes of geographically and chronologically distinct human coronaviruses 229E, Journal of Virology Methods, 75 (1998) 179-193.
View at Publisher |
View at Google Scholar
- [75] N.R. Taylor, A. Cleasby, O. Singh, T. Skarzynski, A.J. Wonacott, P.W. Smith, S.L. Sollis, P.D. Howes, P.C. Cherry, R. Bethell, P. Colman, J. Varghese, Dihydropyrancarboxamides related to zanamivir: a new series of inhibitors of influenza virus sialidases. 2. Crystallographic and molecular modeling study of complexes of 4-amino-4H-pyran-6-carboxamides and sialidase from influenza virus types A and B, J. Med. Chem., 41 (1998) 798-807.
View at Publisher |
View at Google Scholar
- [76] Editorial, Whither crystallography, Nature Structural Biology, 8 (2001) 909.
View at Publisher |
View at Google Scholar
- [77] A. Schmidt, C. Jelsch, P. Ostergaard, W. Rypniewski, V.S. Lamzin, Trypsin revisited: crystallography AT (SUB) atomic resolution and quantum chemistry revealing details of catalysis, J. Biol. Chem., 278 (2003) 43357-43362.
View at Publisher |
View at Google Scholar
- [78] X.Q. Qi, J. Wen, Z.H. Qi, New 3D graphical representation of DNA sequence based on dual nucleotides, Journal of Theroretical Biology, 249 (2007) 681–690.
View at Publisher |
View at Google Scholar
- [79] 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.
View at Publisher |
View at Google Scholar
- [80] H. Gonzalez-Diaz, L.G. Perez-Montoto, A. Duardo-Sanchez, E. Paniagua, S. Vazquez-Prieto, R. Vilas, M.A. Dea-Ayuela, F. Bolas-Fernandez, C.R. Munteanu, J. Dorado, J. Costas, F.M. Ubeira, Generalized lattice graphs for 2D-visualization of biological information, J. Theor. Biol., 261 (2009) 136-147.
View at Publisher |
View at Google Scholar
- [81] C.R. Munteanu, A.L. Magalhaes, E. Uriarte, H. Gonzalez-Diaz, Multi-target QPDR classification model for human breast and colon cancer-related proteins using star graph topological indices, J. Theor. Biol., 257 (2009) 303-311.
View at Publisher |
View at Google Scholar
- [82] A. Perez-Bello, C.R. Munteanu, F.M. Ubeira, A.L. De Magalhaes, E. Uriarte, H. Gonzalez-Diaz, Alignment-free prediction of mycobacterial DNA promoters based on pseudo-folding lattice network or star-graph topological indices, J. Theor. Biol., 256 (2009) 458-466.
View at Publisher |
View at Google Scholar
- [83] L.G. Perez-Montoto, L. Santana, H. Gonzalez-Diaz, Scoring function for DNA-drug docking of anticancer and antiparasitic compounds based on spectral moments of 2D lattice graphs for molecular dynamics trajectories, Eur. J. Med. Chem., 44 (2009) 4461-4469.
View at Publisher |
View at Google Scholar
- [84] J.H. Wong, T.B. Ng, Studies on an Antifungal Protein and a Chromatographically and Structurally Related Protein Isolated from the Culture Broth of Bacillus amyloliquefaciens, Protein & Peptide Letters, 16 (2009) 1399-1406.
View at Publisher |
View at Google Scholar
- [85] J.F. Yu, X. Sun, J.H. Wang, TN curve: a novel 3D graphical representation of DNA sequence based on trinucleotides and its applications, J. Theor. Biol., 261 (2009) 459-468.
View at Publisher |
View at Google Scholar
- [86] K.C. Chou, Graphic rule for drug metabolism systems, Current Drug Metabolism, 11 (2010) 369-378.
View at Publisher |
View at Google Scholar
- [87] 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.
View at Publisher |
View at Google Scholar
- [88] 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.
View at Publisher |
View at Google Scholar
- [89] Z.H. Qi, L. Li, Z.M. Zhang, X.Q. Qi, Self-similarity analysis of eubacteria genome based on weighted graph, J. Theor. Biol., 280 (2011) 10-18.
View at Publisher |
View at Google Scholar
- [90] G. Xie, Z. Mo, Three 3D graphical representations of DNA primary sequences based on the classifications of DNA bases and their applications, J. Theor. Biol., 269 (2011) 123-130.
View at Publisher |
View at Google Scholar
- [91] V. Aguiar-Pulido, C.R. Munteanu, J.A. Seoane, E. Fernandez-Blanco, L.G. Perez-Montoto, H. Gonzalez-Diaz, J. Dorado, Naive Bayes QSDR classification based on spiral-graph Shannon entropies for protein biomarkers in human colon cancer, Mol Biosyst, 8 (2012) 1716-1722.
View at Publisher |
View at Google Scholar
- [92] P. Tripathi, P.N. Pandey, A novel alignment-free method to classify protein folding types by combining spectral graph clustering with Chou's pseudo amino acid composition, J. Theor. Biol., 424 (2017) 49-54.
View at Publisher |
View at Google Scholar
- [93] K.C. Chou, A Stimulating Recollection of Chou's Graph Theory in Enzyme Kinetics, Voice of Publication (VP), 2020 (2020) 161-163.
View at Publisher |
View at Google Scholar
- [94] K.C. Chou, Applications of graph theory to enzyme kinetics and protein folding kinetics: steady and non-steady state systems (Short Communication), Journal of Sensor Networks and Data Communications, 1 (2020) 06-07.
View at Publisher |
View at Google Scholar
- [95] K.C. Chou, Revisiting the paper on “Applications of graph theory to enzyme kinetics and protein folding kinetics: steady and non-steady state systems” (Short Communication), J, Biotechnology and Bioprocessing, 1 (2020) 1-2.
View at Publisher |
View at Google Scholar
- [96] K.C. Chou, N.Y. Chen, The biological functions of low-frequency phonons, Scientia Sinica, 20 (1977) 447-457.
View at Publisher |
View at Google Scholar
- [97] P.C. Painter, L.E. Mosher, C. Rhoads, Low-frequency modes in the Raman spectrum of DNA, Biopolymers, 20 (1981) 243-247.
View at Publisher |
View at Google Scholar
- [98] P.C. Painter, L.E. Mosher, C. Rhoads, Low-frequency modes in the Raman spectra of proteins, Biopolymers, 21 (1982) 1469-1472.
View at Publisher |
View at Google Scholar
- [99] H. Urabe, Y. Tominaga, Low-frequency collective modes of DNA double helix by Raman spectroscopy, Biopolymers, 21 (1982) 2477-2481.
View at Publisher |
View at Google Scholar
- [100] K.C. Chou, Low-frequency vibrations of helical structures in protein molecules, Biochem. J., 209 (1983) 573-580.
View at Publisher |
View at Google Scholar
- [101] K.C. Chou, Identification of low-frequency modes in protein molecules, Biochem. J., 215 (1983) 465-469.
View at Publisher |
View at Google Scholar
- [102] K.C. Chou, Biological functions of low-frequency vibrations ( phonons). 3. Helical structures and microenvironment, Biophys. J., 45 (1984) 881-889.
View at Publisher |
View at Google Scholar
- [103] K.C. Chou, The biological functions of low-frequency phonons. 4. Resonance effects and allosteric transition, Biophysical Chemistry, 20 (1984) 61-71.
View at Publisher |
View at Google Scholar
- [104] K.C. Chou, Low-frequency vibrations of DNA molecules, Biochem. J., 221 (1984) 27-31.
View at Publisher |
View at Google Scholar
- [105] K.C. Chou, Low-frequency motions in protein molecules: beta-sheet and beta-barrel, Biophys. J., 48 (1985) 289-297.
View at Publisher |
View at Google Scholar