NSNatural Science2150-4091Scientific Research Publishing10.4236/ns.2020.127039NS-101484ArticlesBiomedical&Life Sciences Chemistry&Materials Science Earth&Environmental Sciences Medicine&Healthcare Physics&Mathematics The Pandemic Pestilences and Internet Institutes Kuo-ChenChou1Pandemic COVID-19, Pestilences, Atheists, Christians, Internet Institutes0907202012074955154, July 202012, July 2020 15, July 2020© Copyright 2014 by authors and Scientific Research Publishing Inc. 2014This work is licensed under the Creative Commons Attribution International License (CC BY). http://creativecommons.org/licenses/by/4.0/

Whether the recent “Pandemic Pestilences” are of clear sign for the “World End”? Both “Atheists” and “Christians” have deeply believed so. During such a subtle time-period, many exciting results can be produced by the Internet Institutes.

Pandemic COVID-19 Pestilences Atheists Christians Internet Institutes1. INTRODUCTION

As of July-06-2020, about 200 countries on the Earth have been attacked by Pestilences 2019 (COVID-19): for USA that consists of 50 states, 48 states have been identified with “Pestilences”. The total number of the cases concerned is 2,803,454 of which 130,995 result in deaths. For United Kingdom, the corresponding numbers are 283,757 and 43,995, respectively.

2. FACTS AND DISCUSSIONS

It is much more powerful than the “Atomic Bomb” detonated over Japanese city of Hiroshima on August 6, 1945. The bombing killed 129,000 people.

It is also much more horrified than the Terrorists Attack on September 11, 2001 (often referred to “911”). The attacks resulted in 2977 fatalities, over 25,000 injuries, and substantial long-term health consequences.

The number of deaths caused by Pestilences has also significantly exceeded that of military persons killed in any of war involved with USA.

For the so-called “Atheists”, typically represented by “Karl Max” and “Friedrich Engels”, have written in their books: “there is a Beginning, there must be an End”, clearly stating: “the Earth will eventually disappear owing to colliding with the other planet”.

According to Bible, however, close to the “World-End”, nation will rise against nation, and kingdom against kingdom. There will be great earthquakes, famines and pestilences in various places, and fearful events and great signs from Heaven.

Even in the Pachelbel tune that has been played most frequently and constantly, some female singers ask God the two questions: “Why”? and “When”? The 1st question is about “why there is the End of World” while the 2nd question is about “when it will come true”.

Right before the World-End, Jesus will send out his angels to weed out those who are sin, evil and wicked. They will be thrown by the angels into the fiery furnace, where they will be weeping and gnashing of teeth. In contrast to this, the righteous will be raised to the Heaven.

Pestilences or 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 4000 deaths, although no remarkable infectious cases reported in Wuhan. Nevertheless and unfortunately, the 2nd-wave coronavirus diseases have been also identified on Beijing during May 2020. This kind of originally from “East-Globe” or “Eastern hemisphere” to “West “Globe” or “Western Hemisphere” and then kicked back from the West to the East again, very much like playing “Tennis”, “Ping-Pong” or “Badminton”, “Ball”. The extremely dangerous Ball is none but “Coronavirus” or “Pestilences”.

Since all the scientists working in a sharing laboratory of the Universities or most conversional Institutes must wear masks except those working in the “Internet Institute” (Figure 1) such as the “Gordon Life Scient Institute” [1,2]. And the results thus obtained will be of real usage for the other planet as indicated in [3] as well as widely and increasingly agreeable as supported by many papers from different angles or aspects, particularly for the idea of “Pseudo Amino Acid Composition” or PseAAC” [4-74], the “5-steps Rule” [75-97], the “Wenxiang Diagram” [98-100], the “HIV protease inhibitor prediction” [101-106], and the “Graphic Rules” [107-115]. Using graphic approaches to study biological and medical systems can provide an intuitive vision and useful insights for helping analyze complicated relations therein as shown by the eight master pieces of pioneering papers from the then Chairman of Nobel Prize Committee Sture Forsen [107,109,110,116-120] and many follow-up papers [67,98,99,113,115,121-163], and a series of recent papers [164-180].

Moreover, it is instructive to point out that, based on the works on protein conformation [181-196], the new classifications for protein structures have been developed [102,105,106,143,197-207], that based on the works of low-frequency internal motions [116,121,122,124-132,134,208], that based on the works on the graphical rules for biological systems [108-115,136,209,210], that based on the works on enzyme kinetics [108,111-114,119,136,209,211-232], that based on the works on HIV proteases [101,102,104,106,233-238], that Post-translational modification (PTM) [239-270], and that protein subcellular location prediction [162,268,271-288].

3. CONCLUSIONS

For our Earth, after several waves of the killings as described in the Section 2, the time of its “End” will become much faster according to the exponential mode. Before its “End”, it will provide the most useful knowledge to do the science with the “Internet Institutes.”

CONFLICTS OF INTEREST

The authors declare no conflicts of interest regarding the publication of this paper.

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(1982) Origin of the Right-Handed Twist of Beta-Sheets of Poly(LVal) Chains. Proceedings of the National Academy of Sciences of the United States of America, 79, 7047-7051. https://doi.org/10.1073/pnas.79.22.7047Chou, K.C., Nemethy, G. and Scheraga, H.A. (1983) Energetic Approach to Packing of α-Helices: 1. Equivalent Helices. The Journal of Physical Chemistry, 87, 2869-2881. https://doi.org/10.1021/j100238a033Chou, K.C., Nemethy, G. and Scheraga, H.A. (1983) Role of Interchain Interactions in the Stabilization of Right-Handed Twist of β-Sheets. Journal of Molecular Biology, 168, 389-407. https://doi.org/10.1016/S0022-2836(83)80025-4Chou, K.C., Nemethy, G. and Scheraga, H.A. (1983) Effects of Amino Acid Composition on the Twist and the Relative Stability of Parallel and Antiparallel β-Sheets. Biochemistry, 22, 6213-6221. https://doi.org/10.1021/bi00295a027Chou, K.C., Nemethy, G. and Scheraga, H.A. (1984) Energetic Approach to Packing of α-Helices: 2. General Treatment of Nonequivalent and Nonregular Helices. Journal of American Chemical Society, 106, 3161-3170. https://doi.org/10.1021/ja00323a017Scheraga, H.A., Chou, K.C. and Nemethy, G. (1984) Protein Structure and Function, from Colloidal to a Molecular View. Carlsberg Research Communications, 49, 1-55. https://doi.org/10.1007/BF02913964Chou, K.C., Nemethy, G., Pottle, M.S. and Scheraga, H.A. (1985) The Folding of the Twisted Beta-Sheet in Bovine Pancreatic Trypsin Inhibitor. Biochemistry, 24, 7948-7953. https://doi.org/10.1021/bi00348a016Chou, K.C., Nemethy, G., Rumsey, S., Tuttle, R.W. and Scheraga, H.A. (1985) Interactions between an α-Helix and a β-Sheet: Energetics of α/β Packing in Proteins. Journal of Molecular Biology, 186, 591-609. https://doi.org/10.1016/0022-2836(85)90133-0Gerritsen, M., Chou, K.C., Nemethy, G. and Scheraga, H.A. (1985) Energetics of Multi-Helix Interactions in Protein Folding: Application to Myoglobin. 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Proteins: Structure, Function and Bioinformatics, 8, 14-22. https://doi.org/10.1002/prot.340080105Chou, K.C., Nemethy, G. and Scheraga, H.A. (1990) Review: Energetics of Interactions of Regular Structural Elements in Proteins. Accounts of Chemical Research, 23, 134-141. https://doi.org/10.1021/ar00173a003Chou, K.C., Maggiora, G.M. and Scheraga, H.A. (1992) The Role of Loop-Helix Interactions in Stabilizing Four-Helix Bundle Proteins. Proceedings of the National Academy of Sciences of the United States of America, 89, 7315-7319. https://doi.org/10.1073/pnas.89.16.7315Chou, K.C. and Zhang, C.T. (1992) A Correlation Coefficient Method to Predicting Protein Structural Classes from Amino Acid Compositions. European Journal of Biochemistry, 207, 429-433. https://doi.org/10.1111/j.1432-1033.1992.tb17067.xZhang, C.T. and Chou, K.C. (1992) An Optimization Approach to Predicting Protein Structural Class from Amino Acid Composition. 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Proteins: Structure, Function and Bioinformatics, 21, 118-126. https://doi.org/10.1002/prot.340210205Zhang, C.T., Chou, K.C. and Maggiora, G.M. (1995) Predicting Protein Structural Classes from Amino Acid Composition: Application of Fuzzy Clustering. Protein Engineering, 8, 425-435. https://doi.org/10.1093/protein/8.5.425Chou, K.C., Zhang, C.T. and Elrod, D.W. (1996) Do Antisense Proteins Exist? Journal of Protein Chemistry, 15, 59-61. https://doi.org/10.1007/BF01886811Maggiora, G.M., Zhang, C.T., Chou, K.C. and Elrod, D.W. (1996) Combining Fuzzy Clustering and Neural Networks to Predict Protein Structural Classes. In: Devillers, J., Ed., Neural Networks in QSAR and Drug Design, Academic Press, London, 255-279. https://doi.org/10.1016/B978-012213815-7/50012-1Chou, K.C., Liu, W., Maggiora, G.M. and Zhang, C.T. (1998) Prediction and Classification of Domain Structural Classes. 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(2004) A Novel Approach to Predict Active Sites of Enzyme Molecules. Proteins: Structure, Function and Genetics, 55, 77-82. https://doi.org/10.1002/prot.10622Chou, K.C. and Cai, Y.D. (2004) Predicting Enzyme Family Class in a Hybridization Space. Protein Science, 13, 2857-2863. https://doi.org/10.1110/ps.04981104Chou, K.C. and Cai, Y.D. (2004) Using GO-PseAA Predictor to Predict Enzyme Sub-Class. Biochemical and Biophysical Research Communications (BBRC), 325, 506-509. https://doi.org/10.1016/j.bbrc.2004.10.058Cai, Y.D. and Chou, K.C. (2005) Using Functional Domain Composition to Predict Enzyme Family Classes. Journal of Proteome Research, 4, 109-111. https://doi.org/10.1021/pr049835pCai, Y.D. and Chou, K.C. (2005) Predicting Enzyme Subclass by Functional Domain Composition and Pseudo Amino Acid Composition. Journal of Proteome Research, 4, 967-971. https://doi.org/10.1021/pr0500399Cai, Y.D., Zhou, G.P. and Chou, K.C. (2005) Predicting Enzyme Family Classes by Hybridizing Gene Product Composition and Pseudo Amino Acid Composition. Journal of Theoretical Biology, 234, 145-149. https://doi.org/10.1016/j.jtbi.2004.11.017Chou, K.C. (2005) Using Amphiphilic Pseudo Amino Acid Composition to Predict Enzyme Subfamily Classes. Bioinformatics, 21, 10-19. https://doi.org/10.1093/bioinformatics/bth466Shen, H.B. and Chou, K.C. (2007) EzyPred: A Top-Down Approach for Predicting Enzyme Functional Classes and Subclasses. Biochemical and Biophysical Research Communications (BBRC), 364, 53-59. https://doi.org/10.1016/j.bbrc.2007.09.098Wei, H., Zhang, R., Wang, C., Zheng, H., Chou, K.C. and Wei, D.Q. (2007) Molecular Insights of SAH Enzyme Catalysis and Their Implication for Inhibitor Design. Journal of Theoretical Biology, 244, 692-702. https://doi.org/10.1016/j.jtbi.2006.09.011Min, J.L., Xiao, X. and Chou, K.C. (2013) iEzy-Drug: A Web Server for Identifying the Interaction between Enzymes and Drugs in Cellular Networking. BioMed Research International (BMRI), 2013, Article ID: 701317. https://doi.org/10.1155/2013/701317Shen, H.B. and Chou, K.C. (2008) HIVcleave: A Web-Server for Predicting HIV Protease Cleavage Sites in Proteins. Analytical Biochemistry, 375, 388-390. https://doi.org/10.1016/j.ab.2008.01.012Sirois, S., Touaibia, M., Chou, K.C. and Roy, R. (2007) Glycosylation of HIV-1 gp120 V3 Loop: Towards the Rational Design of a Synthetic Carbohydrate Vaccine. Current Medicinal Chemistry, 14, 3232-3242. https://doi.org/10.2174/092986707782793826Gao, W.N., Wei, D.Q., Li, Y., Gao, H., Xu, W.R., Li, A.X. and Chou, K.C. (2007) Agaritine and Its Derivatives Are Potential Inhibitors against HIV Proteases. Medicinal Chemistry, 3, 221-226. https://doi.org/10.2174/157340607780620644Sirois, S., Tsoukas, C.M., Chou, K.C., Wei, D.Q., Boucher, C. and Hatzakis, G.E. (2005) Selection of Molecular Descriptors with Artificial Intelligence for the Understanding of HIV-1 Protease Peptidomimetic Inhibitors-Activity. Medicinal Chemistry, 1, 173-184. https://doi.org/10.2174/1573406053175238Sirois, S., Sing, T. and Chou, K.C. (2005) Review: HIV-1 gp120 V3 Loop for Structure-Based Drug Design. Current Protein and Peptide Science, 6, 413-422. https://doi.org/10.2174/138920305774329359Cai, Y.D., Yu, H. and Chou, K.C. (1998) Using Neural Network for Prediction of HIV Protease Cleavage Sites in Proteins. Journal of Protein Chemistry, 17, 607-615. https://doi.org/10.1007/BF02780962Xu, Y., Ding, J., Wu, L.Y. and Chou, K.C. (2013) iSNO-PseAAC: Predict Cysteine S-Nitrosylation Sites in Proteins by Incorporating Position Specific Amino Acid Propensity into Pseudo Amino Acid Composition. PLoS ONE, 8, e55844. https://doi.org/10.1371/journal.pone.0055844Xu, Y., Shao, X.J., Wu, L.Y., Deng, N.Y. and Chou, K.C. (2013) iSNO-AAPair: Incorporating Amino Acid Pairwise Coupling into PseAAC for Predicting Cysteine S-Nitrosylation Sites in Proteins. PeerJ, 1, e171. https://doi.org/10.7717/peerj.171Xu, Y., Wen, X., Shao, X.J., Deng, N.Y. and Chou, K.C. (2014) iHyd-PseAAC: Predicting Hydroxyproline and Hydroxylysine in Proteins by Incorporating Dipeptide Position-Specific Propensity into Pseudo Amino Acid Composition. International Journal of Molecular Sciences, 15, 7594-7610. https://doi.org/10.3390/ijms15057594Xu, Y., Wen, X., Wen, L.S., Wu, L.Y., Deng, N.Y. and Chou, K.C. (2014) iNitro-Tyr: Prediction of Nitrotyrosine Sites in Proteins with General Pseudo Amino Acid Composition. PLoS ONE, 9, e105018. https://doi.org/10.1371/journal.pone.0105018Xu, Y. and Chou, K.C. (2016) Recent Progress in Predicting Posttranslational Modification Sites in Proteins. Current Topics in Medicinal Chemistry, 16, 591-603. https://doi.org/10.2174/1568026615666150819110421Liu, L.M., Xu, Y. and Chou, K.C. (2017) iPGK-PseAAC: Identify Lysine Phosphoglycerylation Sites in Proteins by Incorporating Four Different Tiers of Amino Acid Pairwise Coupling Information into the General PseAAC. Medicinal Chemistry, 13, 552-559. https://doi.org/10.2174/1573406413666170515120507Xu, Y., Li, C. and Chou, K.C. (2017) iPreny-PseAAC: Identify C-Terminal Cysteine Prenylation Sites in Proteins by Incorporating Two Tiers of Sequence Couplings into PseAAC. Medicinal Chemistry, 13, 544-551. https://doi.org/10.2174/1573406413666170419150052Chen, W., Lin, H., Feng, P.M., Ding, C., Zuo, Y.C. and Chou, K.C. (2012) iNuc-PhysChem: A Sequence-Based Predictor for Identifying Nucleosomes via Physicochemical Properties. PLoS ONE, 7, e47843. https://doi.org/10.1371/journal.pone.0047843Chen, W., Feng, P.M., Lin, H. and Chou, K.C. (2013) iRSpot-PseDNC: Identify Recombination Spots with Pseudo Dinucleotide Composition. Nucleic Acids Research, 41, e68. https://doi.org/10.1093/nar/gks1450Feng, P.M., Chen, W., Lin, H. and Chou, K.C. (2013) iHSP-PseRAAAC: Identifying the Heat Shock Protein Families Using Pseudo Reduced Amino Acid Alphabet Composition. Analytical Biochemistry, 442, 118-125. https://doi.org/10.1016/j.ab.2013.05.024Chen, W., Feng, P.M., Deng, E.Z., Lin, H. and Chou, K.C. (2014) iTIS-PseTNC: A Sequence-Based Predictor for Identifying Translation Initiation Site in Human Genes Using Pseudo Trinucleotide Composition. Analytical Biochemistry, 462, 76-83. https://doi.org/10.1016/j.ab.2014.06.022Chen, W., Feng, P.M., Lin, H. and Chou, K.C. (2014) iSS-PseDNC: Identifying Splicing Sites Using Pseudo Dinucleotide Composition. Biomed Research International (BMRI), 2014, Article ID: 623149. https://doi.org/10.1155/2014/623149Chen, W., Lei, T.Y., Jin, D.C., Lin, H. and Chou, K.C. (2014) PseKNC: A Flexible Web-Server for Generating Pseudo K-Tuple Nucleotide Composition. Analytical Biochemistry, 456, 53-60. https://doi.org/10.1016/j.ab.2014.04.001Ding, H., Deng, E.Z., Yuan, L.F., Liu, L., Lin, H., Chen, W. and Chou, K.C. (2014) iCTX-Type: A Sequence-Based Predictor for Identifying the Types of Conotoxins in Targeting ion Channels. BioMed Research International (BMRI), 2014, Article ID: 286419. https://doi.org/10.1155/2014/286419Guo, S.H., Deng, E.Z., Xu, L.Q., Ding, H., Lin, H., Chen, W. and Chou, K.C. (2014) iNuc-PseKNC: A Sequence-Based Predictor for Predicting Nucleosome Positioning in Genomes with Pseudo k-Tuple Nucleotide Composition. Bioinformatics, 30, 1522-1529. https://doi.org/10.1093/bioinformatics/btu083Lin, H., Deng, E.Z., Ding, H., Chen, W. and Chou, K.C. (2014) iPro54-PseKNC: A Sequence-Based Predictor for Identifying Sigma-54 Promoters in Prokaryote with Pseudo k-Tuple Nucleotide Composition. Nucleic Acids Research, 42, 12961-12972. https://doi.org/10.1093/nar/gku1019Chen, W., Feng, P., Ding, H., Lin, H. and Chou, K.C. (2015) iRNA-Methyl: Identifying N6-Methyladenosine Sites Using Pseudo Nucleotide Composition. Analytical Biochemistry, 490, 26-33. https://doi.org/10.1016/j.ab.2015.08.021Chen, W., Feng, P., Ding, H., Lin, H. and Chou, K.C. (2015) Benchmark Data for Identifying N6-Methyladenosine Sites in the Saccharomyces cerevisiae Genome. Data in Brief, 5, 376-378. https://doi.org/10.1016/j.dib.2015.09.008Chen, W., Lin, H. and Chou, K.C. (2015) Pseudo Nucleotide Composition or PseKNC: An Effective Formulation for Analyzing Genomic Sequences. Molecular BioSystems, 11, 2620-2634. https://doi.org/10.1039/C5MB00155BChen, W., Zhang, X., Brooker, J., Lin, H., Zhang, L. and Chou, K.C. (2015) PseKNC-General: A Cross-Platform Package for Generating Various Modes of Pseudo Nucleotide Compositions. Bioinformatics, 31, 119-120. https://doi.org/10.1093/bioinformatics/btu602Chen, W., Ding, H., Feng, P., Lin, H. and Chou, K.C. (2016) iACP: A Sequence-Based Tool for Identifying Anticancer Peptides. Oncotarget, 7, 16895-16909. https://doi.org/10.18632/oncotarget.7815Chen, W., Feng, P., Ding, H., Lin, H. and Chou, K.C. (2016) Using Deformation Energy to Analyze Nucleosome Positioning in Genomes. Genomics, 107, 69-75. https://doi.org/10.1016/j.ygeno.2015.12.005Chen, W., Tang, H., Ye, J., Lin, H. and Chou, K.C. (2016) iRNA-PseU: Identifying RNA Pseudouridine Sites. Molecular Therapy—Nucleic Acids, 5, e332.Zhang, C.J., Tang, H., Li, W.C., Lin, H., Chen, W. and Chou, K.C. (2016) iOri-Human: Identify Human Origin of Replication by Incorporating Dinucleotide Physicochemical Properties into Pseudo Nucleotide Composition. Oncotarget, 7, 69783-69793. https://doi.org/10.18632/oncotarget.11975Chen, W., Feng, P., Yang, H., Ding, H., Lin, H. and Chou, K.C. (2017) iRNA-AI: Identifying the Adenosine to Inosine Editing Sites in RNA Sequences. Oncotarget, 8, 4208-4217. https://doi.org/10.18632/oncotarget.13758Feng, P., Ding, H., Yang, H., Chen, W., Lin, H. and Chou, K.C. (2017) iRNA-PseColl: Identifying the Occurrence Sites of Different RNA Modifications by Incorporating Collective Effects of Nucleotides into PseKNC. Molecular Therapy—Nucleic Acids, 7, 155-163. https://doi.org/10.1016/j.omtn.2017.03.006Cai, L., Huang, T., Su, J., Zhang, X., Chen, W., Zhang, F., He, L. and Chou, K.C. (2018) Implications of Newly Identified Brain eQTL Genes and Their Interactors in Schizophrenia. Molecular Therapy—Nucleic Acids, 12, 433-442. https://doi.org/10.1016/j.omtn.2018.05.026Chen, W., Ding, H., Zhou, X., Lin, H. and Chou, K.C. (2018) iRNA(m6A)-PseDNC: Identifying N6-Methyladenosine Sites Using Pseudo Dinucleotide Composition. Analytical Biochemistry, 561-562, 59-65. https://doi.org/10.1016/j.ab.2018.09.002Chen, W., Feng, P., Yang, H., Ding, H., Lin, H. and Chou, K.C. (2018) iRNA-3typeA: Identifying 3-Types of Modification at RNA’s Adenosine Sites. 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(1998) Using Discriminant Function for Prediction of Subcellular Location of Prokaryotic Proteins. Biochemical and Biophysical Research Communications (BBRC), 252, 63-68. https://doi.org/10.1006/bbrc.1998.9498Chou, K.C. and Elrod, D.W. (1999) Protein Subcellular Location Prediction. Protein Engineering, Design and Selection, 12, 107-118. https://doi.org/10.1093/protein/12.2.107Chou, K.C. and Elrod, D.W. (1999) Prediction of Membrane Protein Types and Subcellular Locations. Proteins: Structure, Function, and Bioinformatics, 34, 137-153. https://doi.org/10.1002/(SICI)1097-0134(19990101)34:1<137::AID-PROT11>3.0.CO;2-OChou, K.C. (2000) Prediction of Protein Structural Classes and Subcellular Locations. Current Protein and Peptide Science, 1, 171-208. https://doi.org/10.2174/1389203003381379Chou, K.C. (2000) Prediction of Protein Subcellular Locations by Incorporating Quasi-Sequence-Order Effect. Biochemical and Biophysical Research Communications (BBRC), 278, 477-483. https://doi.org/10.1006/bbrc.2000.3815Cai, Y.D., Liu, X.J., Xu, X.B. and Chou, K.C. (2002) Support Vector Machines for Prediction of Protein Subcellular Location by Incorporating Quasi-Sequence-Order Effect. Journal of Cellular Biochemistry, 84, 343-348. https://doi.org/10.1002/jcb.10030Cai, Y.D. and Chou, K.C. (2003) Nearest Neighbour Algorithm for Predicting Protein Subcellular Location by Combining Functional Domain Composition and Pseudo Amino Acid Composition. Biochemical and Biophysical Research Communications (BBRC), 305, 407-411. https://doi.org/10.1016/S0006-291X(03)00775-7Chou, K.C. and Cai, Y.D. (2003) Prediction and Classification of Protein Subcellular Location: Sequence-Order Effect and Pseudo Amino Acid Composition. Journal of Cellular Biochemistry, 90, 1250-1260. (Addendum, ibid. 2004, 91, 1085) https://doi.org/10.1002/jcb.10719Chou, K.C. and Cai, Y.D. (2004) Prediction of Protein Subcellular Locations by GO-FunD-PseAA Predictor. Biochemical and Biophysical Research Communications (BBRC), 320, 1236-1239. https://doi.org/10.1016/j.bbrc.2004.06.073Gao, Y., Shao, S.H., Xiao, X., Ding, Y.S., Huang, Y.S., Huang, Z.D. and Chou, K.C. (2005) Using Pseudo Amino Acid Composition to Predict Protein Subcellular Location: Approached with Lyapunov Index, Bessel Function, and Chebyshev Filter. Amino Acids, 28, 373-376. https://doi.org/10.1007/s00726-005-0206-9Xiao, X., Shao, S., Ding, Y., Huang, Z., Huang, Y. and Chou, K.C. (2005) Using Complexity Measure Factor to Predict Protein Subcellular Location. Amino Acids, 28, 57-61. https://doi.org/10.1007/s00726-004-0148-7Chou, K.C. and Shen, H.B. (2006) Predicting Protein Subcellular Location by Fusing Multiple Classifiers. Journal of Cellular Biochemistry, 99, 517-527. https://doi.org/10.1002/jcb.20879Chou, K.C. and Shen, H.B. (2006) Predicting Eukaryotic Protein Subcellular Location by Fusing Optimized Evidence-Theoretic K-Nearest Neighbor Classifiers. Journal of Proteome Research, 5, 1888-1897. https://doi.org/10.1021/pr060167cXiao, X., Shao, S.H., Ding, Y.S., Huang, Z.D. and Chou, K.C. (2006) Using Cellular Automata Images and Pseudo Amino Acid Composition to Predict Protein Subcellular Location. Amino Acids, 30, 49-54. https://doi.org/10.1007/s00726-005-0225-6Chou, K.C. and Shen, H.B. (2007) Euk-mPLoc: A Fusion Classifier for Large-Scale Eukaryotic Protein Subcellular Location Prediction by Incorporating Multiple Sites. Journal of Proteome Research, 6, 1728-1734. https://doi.org/10.1021/pr060635iChou, K.C. and Shen, H.B. (2007) Recent Progresses in Protein Subcellular Location Prediction. Analytical Biochemistry, 370, 1-16. https://doi.org/10.1016/j.ab.2007.07.006Wan, S.B., Hu, L.L., Niu, S., Wang, K., Cai, Y.D. and Chou, K.C. (2011) Identification of Multiple Subcellular Locations for Proteins in Budding Yeast. Current Bioinformatics, 6, 71-80.Chou, K.C., Wu, Z.C. and Xiao, X. (2012) iLoc-Hum: Using Accumulation-Label Scale to Predict Subcellular Locations of Human Proteins with Both Single and Multiple Sites. Molecular BioSystems, 8, 629-641. https://doi.org/10.1039/C1MB05420A