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 NS  Vol.12 No.7 , July 2020
Coronavirus and Gordon Life Science Institute
Abstract: The impacts of coronavirus to our planet are unprecedented; i.e., they are going to eliminate the entire mankind. Driven by such horrible situation, some philosophical viewpoints have naturally happened. Whether the “World End” must come? If yes, when? During this waiting period, the best way to do science is via the Internet Institute, such as “Gordon Life Science Institute”, and the results thus achieved will be most rewarding.

1. INTRODUCTION

As of June-30-2020, more than 200 countries on our planet have been attacked by the coronavirus disease 2019 (COVID-19): for USA alone with reported 2,682,424 cases of which 128,824 resulted in deaths; for United Kingdom with 312,654 cases and 43,730 deaths.

2. FACTS AND DISCUSSIONS

The damage power of COVID-19 is overwhelmingly stronger than “atomic bombs” (2nd World War, 1945) or any kind of terrorists (“911”, 2001). The death number has also far exceeded the death number reported for any war of USA history. Accordingly, such unprecedented power must come from the “Creator” rather than from being created human beings.

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome, which was first identified in December 2019 in Wuhan, Hubei, China. After April 2020 and causing about 4,000 deaths, although no remarkable infectious cases reported in Wuhan. Unfortunately, the 2nd-round coronavirus diseases have started landing on Beijing during May 2020. This kind of first from “Eastern countries” to Western Countries” and then as a feedback from the West to the East, very much like playing “ping-pong” or “Tennis” ball. Here, the ball is none but the “Coronavirus”.

Facing such environments, all the scientists working in a sharing laboratory of the Universities or most conversional Institutes must or being forced to wear masks except those working in the “Internet Institute” such as the “Gordon Life Scient Institute” [1 , 2]. And the results thus obtained will be most awarding as elaborated in [3]. As concurred by a series of interesting publications, particularly for the idea of “Pseudo Amino Acid Composition” [4-99], and “5-Steps Rule” [100-140].

3. CONCLUSIONS

For the planet where we are staying, after several rounds of the killings as described in the Section 2, its “End” will be expedited exponentially with time. Before its “End”, it will be most awarding to do science with the “Internet Institutes”.

Cite this paper: Chou, K.C. (2020) Coronavirus and Gordon Life Science Institute. Natural Science, 12, 429-440. doi: 10.4236/ns.2020.127035.
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[82]   Tripathi, P. and Pandey, P.N. (2017) A Novel Alignment-Free Method to Classify Protein Folding Types by Combining Spectral Graph Clustering with Chou’s Pseudo Amino Acid Composition. Journal of Theoretical Biology, 424, 49-54.
https://doi.org/10.1016/j.jtbi.2017.04.027

[83]   Yu, B., Lou, L., Li, S., Zhang, Y., Qiu, W., Wu, X., Wang, M. and Tian, B. (2017) Prediction of Protein Structural Class for Low-Similarity Sequences Using Chou’s Pseudo Amino Acid Composition and Wavelet Denoising. Journal of Molecular Graphics and Modelling, 76, 260-273.
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[84]   Al Maruf, M.A. and Shatabda, S. (2018) iRSpot-SF: Prediction of Recombination Hotspots by Incorporating Sequence Based Features into Chou’s Pseudo Components. Genomics, 111, 966-972.
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[85]   Arif, M., Hayat, M. and Jan, Z. (2018) iMem-2LSAAC: A Two-Level Model for Discrimination of Membrane Proteins and Their Types by Extending the Notion of SAAC into Chou’s Pseudo Amino Acid Composition. Journal of Theoretical Biology, 442, 11-21.
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[86]   Cui, X., Yu, Z., Yu, B., Wang, M., Tian, B. and Ma, Q. (2018) UbiSitePred: A Novel Method for Improving the Accuracy of Ubiquitination Sites Prediction by Using LASSO to Select the Optimal Chou’s Pseudo Components. Chemometrics and Intelligent Laboratory Systems, 184, 28-43.
https://doi.org/10.1016/j.chemolab.2018.11.012

[87]   Mei, J. and Zhao, J. (2018) Prediction of HIV-1 and HIV-2 Proteins by Using Chou’s Pseudo Amino Acid Compositions and Different Classifiers. Scientific Reports, 8, Article No. 2359.
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[88]   Qiu, W., Li, S., Cui, X., Yu, Z., Wang, M., Du, J., Peng, Y. and Yu, B. (2018) Predicting Protein Submitochondrial Locations by Incorporating the Pseudo-Position Specific Scoring Matrix into the General Chou’s Pseudo-Amino Acid Composition. Journal of Theoretical Biology, 450, 86-103.
https://doi.org/10.1016/j.jtbi.2018.04.026

[89]   Zhang, L. and Kong, L. (2018) iRSpot-ADPM: Identify Recombination Spots by Incorporating the Associated Dinucleotide Product Model into Chou’s Pseudo Components. Journal of Theoretical Biology, 441, 1-8.
https://doi.org/10.1016/j.jtbi.2017.12.025

[90]   Zhang, S., Yang, K., Lei, Y. and Song, K. (2018) iRSpot-DTS: Predict Recombination Spots by Incorporating the Dinucleotide-Based Spare-Cross Covariance Information into Chou’s Pseudo Components. Genomics, 11, 457-464.

[91]   Zhao, W., Wang, L., Zhang, T.X., Zhao, Z.N. and Du, P.F. (2018) A Brief Review on Software Tools in Generating Chou’s Pseudo-Factor Representations for All Types of Biological Sequences. Protein & Peptide Letters, 25, 822-829.
https://doi.org/10.2174/0929866525666180905111124

[92]   Al Maruf, M.A. and Shatabda, S. (2019) iRSpot-SF: Prediction of Recombination Hotspots by Incorporating Sequence Based Features into Chou’s Pseudo Components. Genomics, 111, 966-972.
https://doi.org/10.1016/j.ygeno.2018.06.003

[93]   Nosrati, M., Mohabatkar, H. and Behbahani, M. (2019) Introducing of an Integrated Artificial Neural Network and Chou’s Pseudo Amino Acid Composition Approach for Computational Epitope-Mapping of Crimean-Congo Haemorrhagic Fever Virus Antigens. International Immunopharmacology, 78, Article ID: 106020.
https://www.sciencedirect.com/science/article/pii/S1567576919321277
https://doi.org/10.1016/j.intimp.2019.106020


[94]   Pan, Y., Wang, S., Zhang, Q., Lu, Q., Su, D., Zuo, Y. and Yang, L. (2019) Analysis and Prediction of Animal Toxins by Various Chou’s Pseudo Components and Reduced Amino Acid Compositions. Journal of Theoretical Biology, 462, 221-229.
https://doi.org/10.1016/j.jtbi.2018.11.010

[95]   Tahir, M., Tayara, H. and Chong, K.T. (2019) iRNA-PseKNC(2methyl): Identify RNA 2’-O-Methylation Sites by Convolution Neural Network and Chou’s Pseudo Components. Journal of Theoretical Biology, 465, 1-6.
https://doi.org/10.1016/j.jtbi.2018.12.034

[96]   Tian, B., Wu, X., Chen, C., Qiu, W., Ma, Q. and Yu, B. (2019) Predicting Protein-Protein Interactions by Fusing Various Chou’s Pseudo Components and Using Wavelet Denoising Approach. Journal of Theoretical Biology, 462, 329-346.
https://doi.org/10.1016/j.jtbi.2018.11.011

[97]   Zhang, L. and Kong, L. (2019) iRSpot-PDI: Identification of Recombination Spots by Incorporating Dinucleotide Property Diversity Information into Chou’s Pseudo Components. Genomics, 111, 457-464.
https://doi.org/10.1016/j.ygeno.2018.03.003

[98]   Zhang, S., Yang, K., Lei, Y. and Song, K. (2019) iRSpot-DTS: Predict Recombination Spots by Incorporating the Dinucleotide-Based Spare-Cross Covariance Information into Chou’s Pseudo Components. Genomics, 111, 1760-1770.
https://doi.org/10.1016/j.ygeno.2018.11.031

[99]   Nosrati, M., Mohabatkar, H. and Behbahani, M. (2020) Introducing of an Integrated Artificial Neural Network and Chou’s Pseudo Amino Acid Composition Approach for Computational Epitope-Mapping of Crimean-Congo Haemorrhagic Fever Virus Antigens. International Immunopharmacology, 78, Article ID: 106020.
https://doi.org/10.1016/j.intimp.2019.106020

[100]   Butt, A.H. and Khan, Y.D. (2018) Prediction of S-Sulfenylation Sites Using Statistical Moments Based Features via Chou’s 5-Step Rule. International Journal of Peptide Research and Therapeutics.
https://doi.org/10.1007/s10989-019-09931-2

[101]   Awais, M., Hussain, W., Khan, Y.D., Rasool, N., Khan, S.A. and Chou, K.C. (2019) iPhosH-PseAAC: Identify Phosphohistidine Sites in Proteins by Blending Statistical Moments and Position Relative Features According to the Chou’s 5-Step Rule and General Pseudo Amino Acid Composition. IEEE/ACM Transactions on Computational Biology and Bioinformatics.
https://doi.org/10.1109/TCBB.2019.2919025

[102]   Barukab, O., Khan, Y.D., Khan, S.A. and Chou, K.C. (2019) iSulfoTyr-PseAAC: Identify Tyrosine Sulfation Sites by Incorporating Statistical Moments via Chou’s 5-Steps Rule and Pseudo Components. Current Genomics, 20, 306-320.
https://doi.org/10.2174/1389202920666190819091609

[103]   Butt, A.H. and Khan, Y.D. (2019) Prediction of S-Sulfenylation Sites Using Statistical Moments Based Features via Chou’s 5-Step Rule. International Journal of Peptide Research and Therapeutics (IJPRT).
https://doi.org/10.1007/s10989-019-09931-2

[104]   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.
https://doi.org/10.2174/1566524019666191011160543

[105]   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

[106]   Dutta, A., Dalmia, A., 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

[107]   Ehsan, A., Mahmood, M.K., Khan, Y.D., Barukab, O.M., Khan, S.A. and Chou, K.C. (2019) iHyd-PseAAC (EPSV): Identify Hydroxylation Sites in Proteins by Extracting Enhanced Position and Sequence Variant Feature via Chou’s 5-Step Rule and General Pseudo Amino Acid Composition. Current Genomics, 20, 124-133.
https://doi.org/10.2174/1389202920666190325162307

[108]   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

[109]   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

[110]   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

[111]   Khan, S., Khan, M., Iqbal, N., Hussain, T., Khan, S.A. and Chou, K.C. (2019) 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.
https://doi.org/10.1007/s10989-019-09887-3

[112]   Khan, Z.U., Ali, F., Khan, I.A., Hussain, Y. and Pi, D. (2019) iRSpot-SPI: Deep Learning-Based Recombination Spots Prediction by Incorporating Secondary Sequence Information Coupled with Physio-Chemical Properties via Chou’s 5-Step Rule and Pseudo Components. Chemometrics and Intelligent Laboratory Systems (CHEMOLAB), 189, 169-180.
https://doi.org/10.1016/j.chemolab.2019.05.003

[113]   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


[114]   Le, N.Q.K. (2019) iN6-methylat (5-Step): Identifying DNA N(6)-Methyladenine Sites in Rice Genome Using Continuous Bag of Nucleobases via Chou’s 5-Step Rule. Molecular Genetics and Genomics: MGG, 294, 1173-1182.
https://doi.org/10.1007/s00438-019-01570-y

[115]   Le, N.Q.K., Yapp, E.K.Y., Ho, Q.T., Nagasundaram, N., Ou, Y.Y. and Yeh, H.Y. (2019) iEnhancer-5Step: Identifying Enhancers Using Hidden Information of DNA Sequences via Chou’s 5-Step Rule and Word Embedding. Analytical Biochemistry, 571, 53-61.
https://doi.org/10.1016/j.ab.2019.02.017

[116]   Le, N.Q.K., Yapp, E.K.Y., Ou, Y.Y. and Yeh, H.Y. (2019) iMotor-CNN: Identifying Molecular Functions of Cytoskeleton Motor Proteins Using 2D Convolutional Neural Network via Chou’s 5-Step Rule. Analytical Biochemistry, 575, 17-26.
https://doi.org/10.1016/j.ab.2019.03.017

[117]   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 Medicnal Chemistry, 19, 2301-2317.
https://doi.org/10.2174/1568026619666191016155543

[118]   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

[119]   Liu, Z., Dong, W., Jiang, W. and He, Z. (2019) csDMA: An Improved Bioinformatics Tool for Identifying DNA 6 mA Modifications via Chou’s 5-Step Rule. Scientific Reports, 9, Article No. 13109.
https://doi.org/10.1038/s41598-019-49430-4

[120]   Malebary, S.J., Rehman, M.S.U. and Khan, Y.D. (2019) iCrotoK-PseAAC: Identify Lysine Crotonylation Sites by Blending Position Relative Statistical Features According to the Chou’s 5-Step Rule. PLoS ONE, 14, e0223993.
https://doi.org/10.1371/journal.pone.0223993

[121]   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 (CHEMOLAB), 193, Article ID: 103811.
https://doi.org/10.1016/j.chemolab.2019.103811

[122]   Ning, Q., Ma, Z. and Zhao, X. (2019) dForml(KNN)-PseAAC: Detecting Formylation Sites from Protein Sequences Using K-Nearest Neighbor Algorithm via Chou’s 5-Step Rule and Pseudo Components. Journal of Theoretical Biology, 470, 43-49.
https://doi.org/10.1016/j.jtbi.2019.03.011

[123]   Tahir, M., Tayara, H. and Chong, K.T. (2019) iDNA6mA (5-Step Rule): Identification of DNA N6-Methyladenine Sites in the Rice Genome by Intelligent Computational Model via Chou’s 5-Step Rule. CHEMOLAB, 189, 96-101.
https://doi.org/10.1016/j.chemolab.2019.04.007

[124]   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

[125]   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

[126]   Akbar, S., Rahman, A.U., Hayat, M., et al. (2020) cACP: Classifying Anticancer Peptides Using Discriminative Intelligent Model via Chou’s 5-Step Rules and General Pseudo Components. Chemometrics and Intelligent Laboratory (CHEMOLAB), 196, Article ID: 103912.
https://doi.org/10.1016/j.chemolab.2019.103912

[127]   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

[128]   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

[129]   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

[130]   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

[131]   Dobosz, R., Mucko, J. and Gawinecki, R. (2020) Using Chou’s 5-Step Rule to Evaluate the Stability of Tautomers: Susceptibility of 2-[(Phenylimino)-methyl]-cyclohexane-1,3-diones to Tautomerization Based on the Calculated Gibbs Free Energies. Energies, 13, 183.
https://doi.org/10.3390/en13010183

[132]   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

[133]   Dutta, A., Dalmia, A., 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

[134]   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

[135]   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

[136]   Khan, Y.D., Amin, N., Hussain, W., Rasool, N., Khan, S.A. and Chou, K.C. (2020) iProtease-PseAAC(2L): A Two-Layer Predictor for Identifying Proteases and Their Types Using Chou’s 5-Step-Rule and General PseAAC. Analytical Biochemistry, 588, Article ID: 113477.
https://doi.org/10.1016/j.ab.2019.113477

[137]   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

[138]   Vishnoi, S., Garg, P. and Arora, P. (2020) Physicochemical n-Grams Tool: A Tool for Protein Physicochemical Descriptor Generation via Chou’s 5-Step Rule. Chemical Biology & Drug Design, 95, 79-86.
https://doi.org/10.1111/cbdd.13617

[139]   Vundavilli, H., Datta, A., Sima, C., Hua, J., Lopes, R. and Bittner, M. (2020) Using Chou’s 5-Steps Rule to Model Feedback in Lung Cancer. IEEE Journal of Biomedical and Health Informatics, 21, 1-24.
https://doi.org/10.1109/JBHI.2019.2958042

[140]   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

 
 
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