info@auctorespublishing.com
+1-(302)-520-2632
+1-(302)-520-2632
logo

The Benefits of Door Open for a Country

  1. Home
  2. Journals
  3. Journal of Scientific Research and Biomedical Informatics
  4. Archives
Submit Guidelines

Review Article | DOI: https://doi.org/10.31579/jsrbi.2021/011

The Benefits of Door Open for a Country

  • Kuo-Chen Chou 1*
  • 1* Gordon Life Science Institute, Boston, Massachusetts 02478, United States of America

*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.

  1. Proposing graphical rules in molecular biology.
  2. Proposing pseudo amino acid composition [1-16].
  3. Proposing Pseudo K-Tuple Nucleotide Composition [17-20].
  4. Proposing Pseudo-in-one [21, 22].
  5. Proposing 5-steps rule [7, 8, 23-63, 64 , 65-95].
  6. 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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [76] Editorial, Whither crystallography, Nature Structural Biology, 8 (2001) 909.
    View at Publisher | View at Google Scholar
  77. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [104] K.C. Chou, Low-frequency vibrations of DNA molecules, Biochem. J., 221 (1984) 27-31.
    View at Publisher | View at Google Scholar
  105. [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

Dear Editorial Team, Clinical Medical Reviews and Reports. My experience with the journal was highly positive. The peer-review process was rigorous, constructive, and completed in a timely manner. The reviewers provided valuable comments that helped improve the quality and clarity of our manuscript. The editorial office was professional, responsive, and supportive throughout all stages of the publication process. Communication was clear and efficient, and any questions were addressed promptly. Overall, I found the journal to maintain high scientific standards and an excellent publication workflow. I would be pleased to consider submitting future work to this journal. Best wishes from, Elena Popa.

img

Dr Elena Popa

It was my pleasure to submit my testimonial concerning the Reviewer Board of our Scientific Journal “Brain and Neurological Disorders”. The Reviewers focused on some modifications and their contribution was helpful. The ladies of our Editorial Office were also supported my efforts. It was my honor to have such a co-operation and I am looking forward for more collaboration.

img

Dr Nikolaos Andreas Chrysanthakopoulos

Dear Grace Pierce, Editorial Coordinator of Journal of Clinical Research and Reports, Thank you for the speedy and efficient peer review process. I appreciate the fact that your peer reviewers do not take months to respond like with some other journals. I would also like to thank the editorial office for responding quickly to my questions. It is an excellent journal. I plan to submit more manuscripts in the future. Best wishes from, Robert W. McGee

img

Robert W McGee

Dear Grace Pierce, Editorial Coordinator of Journal of Clinical Research and Reports, Working with you and your team on our recent publication in JCRR has been a truly wonderful and enjoyable experience. The responses were prompt, and the reviewers were patient, constructive, and highly professional. One reviewer in particular gave me the feeling that a professor was carefully reading and commenting on my coursework, which was deeply touching. The entire process was straightforward and hassle‑free, with no tedious online forms to complete. I highly recommend this journal. Best wishes from, DR Aibing Rao, Head of R&D

img

Aibing Rao

I Appreciate the Opportunity to Share my Experience with the Journal of Clinical Research and Reports. The peer review process was timely and constructive, and the feedback provided helped improve the quality of our manuscript. The editorial office was professional, responsive, and supportive throughout the process, ensuring smooth communication and efficient handling of the submission. Overall, it was a positive experience collaborating with your team.

img

Kashani Mehdi

Dear Mercy Grace, Editorial Coordinator of Obstetrics Gynecology and Reproductive Sciences, We would like to express our gratitude for your help at all stages of publishing and editing the article. The editors of the magazine answer all the necessary questions and help at every stage. We will definitely continue to cooperate and publish other works in the Obstetrics Gynecology and Reproductive Sciences! Best wishes from, Alla Konstantinovna Politova,

img

Alla Konstantinovna Politova