The Significant and Profound Impacts of Gordon Life Science Institute

Kuo-Chen Chou

Gordon Life Science Institute, Boston, Massachusetts, USA.

DOI: 10.4236/vp.2021.71002                

Abstract

In this short review paper, the significant and profound impacts of the Gordon Life Science Institute have been briefly presented with crystal clear convincingness.

Keywords

Internet Institute, Gordon Life Science Institute, COVID-19, Significant Impacts, Profound Impact

The “Gordon Life Science” or “GLS” Institute is the first Internet Institute in the world. Established by Prof. Dr. Kuo-Chen Chou in 2003, right after he was retired from “Pfizer Global Research and Development” in San Diego, California.

There is a very interesting story for the name of Institute as elaborated below.

After the “gangs of four” (including Mao Zedong’s wife) was smashed and the complete failure of Mao “Cultural Revolution”, China was under the leadership of Deng Xiaoping, who took the policy of “韬光养晦” starting to open China’s door, the founder 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. It was very difficult for Swedish people to pronounce “Kuo-Chen Chou”. In order for his colleagues and friends there easier to pronounce his name, Professor Chou used “Gordon” as his name in Sweden.

In 2003, about a quarter of century later, the same name was used for the Institute, meaning that Deng’s “韬光养晦” policy can stimulate a lot of great creativities.

Accordingly, strictly speaking, it is in “Lund of Sweden” where the “Gordon Life Science Institute” has already been of pregnancy. But after 30 years in 2003, the Institute has been really born in San Diego of California. And its current liaison site is in Boston of Massachusetts. For more information about the Institute, click the button at https://gordonlifescience.org/GordonLifeScience.html.

The Institute is superior to any of the conversional institutes in both the number of the publications and their novelty (see, e.g., [1] - [339] ).

Up to March, 2019, the Institute has 26 members. Among them 5 have been selected by Thompson Reuter and Clarivate Analytics as the “Highly Cited Researcher” or “HCR”: 1) Xoan Xiao (2018), 2) Hao Lin (2018), 3) Wei Chen (2018), 4) Hong-Bin Shen (2014 and 2015), and 5) Kuo-Chen Chou for continuously 5 years (2014, 2015, 2016, 2017, and 2018), indicating that for the ratio of HCR per member, the “Gordon Life Science Institute” has already exceeded the “Broad Institute of MIT and Harvard, USA”, becoming the very top in the world.

Particularly, facing the Pandemic COVID-2019, the Gordon Life Science Institute has the overwhelming advantages to the conventional Institutions or Institutes.

It is indeed a very important strategy to develop the Internet Institutes such as “Gordon Life Science Institute”, and it is indeed very profound as well for saving our planet.

Conflicts of Interest

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

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[89]	Shen, H.B. and Chou, K.C. (2007) Using Ensemble Classifier to Identify Membrane Protein Types. Amino Acids, 32, 483-488. https://doi.org/10.1007/s00726-006-0439-2
[90]	Wang, S.Q., Du, Q.S., Zhao, K., Li, A.X., Wei, D.Q. and Chou, K.C. (2007) Virtual Screening for Finding Natural Inhibitor against Cathepsin-L for SARS Therapy. Amino Acids, 33, 129-135. https://doi.org/10.1007/s00726-006-0403-1
[91]	Chou, K.C. and Shen, H.B. (2008) Cell-PLoc: A Package of Web Servers for Predicting Subcellular Localization of Proteins in Various Organisms. Nature Protocols, 3, 153-162. https://doi.org/10.1038/nprot.2007.494
[92]	Guo, X.L., Li, L., Wei, D.Q., Zhu, Y.S. and Chou, K.C. (2008) Cleavage Mechanism of the H5N1 Hemagglutinin by Trypsin and Furin. Amino Acids, 35, 375-382. https://doi.org/10.1007/s00726-007-0611-3
[93]	Aguero-Chapin, G., Antunes, A., Ubeira, F.M., Chou, K.C. and Gonzalez-Diaz, H. (2008) Comparative Study of Topological Indices of Macro/Supra-Molecular RNA Complex Networks. Journal of Chemical Information & Modeling, 48, 2265-2277. https://doi.org/10.1021/ci8001809
[94]	Yang, Z.R. and Chou, K.C. (2008) Correlation of Metabolic Pathways with the Primary Structure in Acetylated Proteins. The Open Bioinformatics Journal, 2, 90-96. https://doi.org/10.2174/1875036200802010090
[95]	Zhang, S.W., Zhang, Y.L., Pan, Q., Cheng, Y.M. and Chou, K.C. (2008) Estimating Residue Evolutionary Conservation by Introducing von Neumann Entropy and a Novel Gap-Treating Approach. Amino Acids, 35, 495-501. https://doi.org/10.1007/s00726-007-0586-0
[96]	Shen, 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.2008.01.012
[97]	Huang, R.B., Du, Q.S., Wang, C.H. and Chou, K.C. (2008) An In-Depth Analysis of the Biological Functional Studies Based on the NMR M2 Channel Structure of Influenza A Virus. Biochemical and Biophysical Research Communications (BBRC), 377, 1243-1247. https://doi.org/10.1016/j.bbrc.2008.10.148
[98]	Wang, J.F., Wei, D.Q., Chen, C., Li, Y. and Chou, K.C. (2008) Molecular Modeling of Two CYP2C19 SNPs and Its Implications for Personalized Drug Design. Protein & Peptide Letters, 15, 27-32. https://doi.org/10.2174/092986608783330305
[99]	Wang, J.F., Wei, D.Q., Du, H.L., Li, Y.X. and Chou, K.C. (2008) Molecular Modeling Studies on NADP-Dependence of Candida tropicalis Strain Xylose Reductase. The Open Bioinformatics Journal, 2, 72-79. https://doi.org/10.2174/1875036200802010072
[100]	Du, Q.S., Huang, R.B., Wei, Y.T., Du, L.Q. and Chou, K.C. (2008) Multiple Field Three Dimensional Quantitative Structure-Activity Relationship (MF-3D-QSAR). Journal of Computational Chemistry, 29, 211-219. https://doi.org/10.1002/jcc.20776
[101]	Wang, T., Yang, J., Shen, H.B. and Chou, K.C. (2008) Predicting Membrane Protein Types by the LLDA Algorithm. Protein & Peptide Letters, 15, 915-921. https://doi.org/10.2174/092986608785849308
[102]	Xiao, X., Wang, P. and Chou, K.C. (2008) Predicting Protein Structural Classes with Pseudo Amino Acid Composition: An Approach Using Geometric Moments of Cellular Automaton Image. Journal of Theoretical Biology, 254, 691-696. https://doi.org/10.1016/j.jtbi.2008.06.016
[103]	Zhang, T.L., Ding, Y.S. and Chou, K.C. (2008) Prediction Protein Structural Classes with Pseudo Amino Acid Composition: Approximate Entropy and Hydrophobicity Pattern. Journal of Theoretical Biology, 250, 186-193. https://doi.org/10.1016/j.jtbi.2007.09.014
[104]	Chou, K.C. and Shen, H.B. (2008) ProtIdent: A Web Server for Identifying Proteases and Their Types by Fusing Functional Domain and Sequential Evolution Information. Biochemical and Biophysical Research Communications (BBRC), 376, 321-325. https://doi.org/10.1016/j.bbrc.2008.08.125
[105]	Shen, H.B. and Chou, K.C. (2008) PseAAC: A Flexible Web-Server for Generating Various Kinds of Protein Pseudo Amino Acid Composition. Analytical Biochemistry, 373, 386-388. https://doi.org/10.1016/j.ab.2007.10.012
[106]	Wang, J.F., Wei, D.Q., Li, L. and Chou, K.C. (2008) Review: Drug Candidates from Traditional Chinese Medicines. Current Topics in Medicinal Chemistry, 8, 1656-1665. https://doi.org/10.2174/156802608786786633
[107]	Wang, J.F., Wei, D.Q., Li, L. and Chou, K.C. (2008) Review: Pharmacogenomics and Personalized Use of Drugs. Current Topics of Medicinal Chemistry, 8, 1573-1579. https://doi.org/10.2174/156802608786786534
[108]	Du, Q.S., Huang, R.B. and Chou, K.C. (2008) Review: Recent Advances in QSAR and Their Applications in Predicting the Activities of Chemical Molecules, Peptides and Proteins for Drug Design. Current Protein & Peptide Science, 9, 248-259. https://doi.org/10.2174/138920308784534005
[109]	Cruz-Monteagudo, M., Munteanu, C.R., Borges, F., Natália, M., Cordeiro, D.S., Uriarte, E., Chou, K.C. and Gonzalez-Diaz, H. (2008) Stochastic Molecular Descriptors for Polymers. 4. Study of Complex Mixtures with Topological Indices of Mass Spectra Spiral and Star Networks: The Blood Proteome Case, Polymer, 49, 5575-5587. https://doi.org/10.1016/j.polymer.2008.09.070
[110]	Chou, K.C. (2009) Automated Prediction of Protein Attributes and Its Impact to Biomedicine and Drug Discovery. In: Alterovitz, G., Benson, R. and Ramoni, M.F., Eds., Automation in Proteomics and Genomics: An Engineering Case-Based Approach (Harvard-MIT Interdisciplinary Special Studies Courses), Wiley & Sons, Ltd., West Sussex, Chap. 5, 97-143. https://doi.org/10.1002/9780470741191.ch5
[111]	Gong, K., Li, L., Wang, J.F., Cheng, F., Wei, D.Q. and Chou, K.C. (2009) Binding Mechanism of H5N1 Influenza Virus Neuraminidase with Ligands and Its Implication for Drug Design. Medicinal Chemistry, 5, 242-249. https://doi.org/10.2174/157340609788185936
[112]	Wang, J.F., Yan, J.Y., Wei, D.Q. and Chou, K.C. (2009) Binding of CYP2C9 with Diverse Drugs and Its Implications for Metabolic Mechanism. Medicinal Chemistry, 5, 263-270. https://doi.org/10.2174/157340609788185954
[113]	Du, Q.S., Huang, R.B., Wang, C.H., Li, X.M. and Chou, K.C. (2009) Energetic Analysis of the Two Controversial Drug Binding Sites of the M2 Proton Channel in Influenza A Virus. Journal of Theoretical Biology, 259, 159-164. https://doi.org/10.1016/j.jtbi.2009.03.003
[114]	Du, Q.S., Huang, R.B., Wei, Y.T., Pang, Z.W., Du, L.Q. and Chou, K.C. (2009) Fragment-Based Quantitative Structure-Activity Relationship (FB-QSAR) for Fragment-Based Drug Design. Journal of Computational Chemistry, 30, 295-304. https://doi.org/10.1002/jcc.21056
[115]	Shen, H.B. and Chou, K.C. (2009) Gpos-mPLoc: A Top-Down Approach to Improve the Quality of Predicting Subcellular Localization of Gram-Positive Bacterial Proteins. Protein & Peptide Letters, 16, 1478-1484. https://doi.org/10.2174/092986609789839322
[116]	Shen, H.B. and Chou, K.C. (2009) Identification of Proteases and Their Types. Analytical Biochemistry, 385, 153-160. https://doi.org/10.1016/j.ab.2008.10.020
[117]	Wei, H., Wang, C.H., Du, Q.S., Meng, J. and Chou, K.C. (2009) Investigation into Adamantane-Based M2 Inhibitors with FB-QSAR. Medicinal Chemistry, 5, 305-317. https://doi.org/10.2174/157340609788681430
[118]	Wang, J.F., Zhang, C.C., Yan, J.Y., Chou, K.C. and Wei, D.Q. (2009) Molecular Modeling of CYP Proteins and Its Implication for Personal Drug Design. In: Alterovitz, G., Benson, R. and Ramoni, M.F., Eds., Automation in Proteomics and Genomics: An Engineering Case-Based Approach (Harvard-MIT Interdisciplinary Special Studies Courses), John Wiley & Sons, Ltd., West Sussex, Chap. 11, 275-292.
[119]	Huang, R.B., Du, Q.S., Wei, Y.T., Pang, Z.W., Wei, H. and Chou, K.C. (2009) Physics and Chemistry-Driven Artificial Neural Network for Predicting Bioactivity of Peptides and Proteins and Their Design. Journal of Theoretical Biology, 256, 428-435. https://doi.org/10.1016/j.jtbi.2008.08.028
[120]	Gu, R.X., Gu, H., Xie, Z.Y., Wang, J.F., Arias, H.R., Wei, D.Q. and Chou, K.C. (2009) Possible Drug Candidates for Alzheimer’s Disease Deduced from Studying Their Binding Interactions with alpha7 Nicotinic Acetylcholine Receptor. Medicinal Chemistry, 5, 250-262. https://doi.org/10.2174/157340609788185909
[121]	Shen, H.B. and Chou, K.C. (2009) Predicting Protein Fold Pattern with Functional Domain and Sequential Evolution Information. Journal of Theoretical Biology, 256, 441-446. https://doi.org/10.1016/j.jtbi.2008.10.007
[122]	Xiao, X., Wang, P. and Chou, K.C. (2009) Predicting Protein Quaternary Structural Attribute by Hybridizing Functional Domain Composition and Pseudo Amino Acid Composition. Journal of Applied Crystallography, 42, 169-173. https://doi.org/10.1107/S0021889809002751
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[125]	Shen, H.B., Wang, J.F., Yao, L.X., Yang, J. and Chou, K.C. (2009) Recent Progress of Bioinformatics in Membrane Protein Structural Studies. In: Alterovitz, G., Benson, R., and Ramoni, M.F., Eds., Automation in Proteomics and Genomics: An Engineering Case-Based Approach (Harvard-MIT Interdisciplinary Special Studies Courses), John Wiley & Sons, Ltd., West Sussex, Chap. 12, 293-308. https://doi.org/10.1002/9780470741191.ch12
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[127]	Madkan, A., Blank, M., Elson, E., Chou, K.C., Geddis, M.S. and Goodman, R. (2009) Steps to the Clinic with ELF EMF. Natural Science, 1, 157-165. https://doi.org/10.4236/ns.2009.13020
[128]	Chou, K.C., Wei, D.Q., Du, Q.S., Sirois, S., Shen, H.B. and Zhong, W.Z. (2009) Study of Inhibitors against SARS Coronavirus by Computational Approaches. In: Lendeckel, U. and Hooper, N.M., Eds., Proteases in Biology and Disease: Viral Proteases and Antiviral Protease Inhibitor Therapy, Springer Science, Media B.V., Berlin, 1-23. https://doi.org/10.1007/978-90-481-2348-3_1
[129]	Shen, H.B. and Chou, K.C. (2009) A Top-Down Approach to Enhance the Power of Predicting Human Protein Subcellular Localization: Hum-mPLoc 2.0. Analytical Biochemistry, 394, 269-274. https://doi.org/10.1016/j.ab.2009.07.046
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[132]	Chen, L., Huang, T., Shi, X.H., Cai, Y.D. and Chou, K.C. (2010) Analysis of Protein Pathway Networks Using Hybrid Properties. Molecules, 15, 8177-8192. http://www.mdpi.com/journal/molecules https://doi.org/10.3390/molecules15118177
[133]	Chou, K.C. and Shen, H.B. (2010) Cell-PLoc 2.0: An Improved Package of Web-Servers for Predicting Subcellular Localization of Proteins in Various Organisms. Natural Science, 2, 1090-1103. https://doi.org/10.4236/ns.2010.210136
[134]	Qi, J.P., Ding, Y.S., Shao, S.H., Zeng, X.H. and Chou, K.C. (2010) Cellular Responding Kinetics Based on a Model of Gene Regulatory Networks under Radiotherapy. Health, 2, 137-146. https://doi.org/10.4236/health.2010.22021
[135]	Du, Q.S., Wang, S.Q., Huang, R.B. and Chou, K.C. (2010) Computational 3D Structures of Drug-Targeting Proteins in the 2009-H1N1 Influenza A Virus. Chemical Physics Letters, 485, 191-195. https://doi.org/10.1016/j.cplett.2009.12.037
[136]	Chou, K.C. (2010) The Cradle of Gordon Life Science Institute and Its Development and Driving Force (Short Communication). Biomedical Journal of Scientific & Technology Research, 23, 17848-17863. https://doi.org/10.26717/BJSTR.2019.23.003978
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[138]	Shen, H.B. and Chou, K.C. (2010) Gneg-mPLoc: A Top-Down Strategy to Enhance the Quality of Predicting Subcellular Localization of Gram-Negative Bacterial Proteins. Journal of Theoretical Biology, 264, 326-333. https://doi.org/10.1016/j.jtbi.2010.01.018
[139]	Chou, K.C. (2010) Graphic Rule for Drug Metabolism Systems. Current Drug Metabolism, 11, 369-378. https://doi.org/10.2174/138920010791514261
[140]	Ren, L.H., Shen, Y.Z., Ding, Y.S. and Chou, K.C. (2011) Bio-Entity Network for Analysis of Protein-Protein Interaction Networks. Asian Journal of Control, 13, 726-737. https://doi.org/10.1002/asjc.395
[141]	Xiao, X., Wang, P. and Chou, K.C. (2011) Cellular Automata and Its Applications in Protein. Bioinformatics, Current Protein & Peptide Science, 12, 508-519. https://doi.org/10.2174/138920311796957720
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[143]	Xiao, X., Wang, P. and Chou, K.C. (2011) GPCR-2L: Predicting G Protein-Coupled Receptors and Their Types by Hybridizing Two Different Modes of Pseudo Amino Acid Compositions. Molecular Biosystems, 7, 911-919. https://doi.org/10.1039/C0MB00170H
[144]	Wan, 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. https://doi.org/10.2174/157489311795222374
[145]	Cai, L., Wang, Y., Wang, J.F. and Chou, K.C. (2011) Identification of Proteins Interacting with Human SP110 during the Process of Viral Infections. Medicinal Chemistry, 7, 121-126. https://doi.org/10.2174/157340611794859343
[146]	Lin, W.Z., Fang, J.A., Xiao, X. and Chou, K.C. (2011) iDNA-Prot: Identification of DNA Binding Proteins Using Random Forest with Grey Model. PLoS ONE, 6, e24756. https://doi.org/10.1371/journal.pone.0024756
[147]	Wang, J.F. and Chou, K.C. (2011) Insights from Modeling the 3D Structure of New Delhi Metallo-Beta-Lactamase and Its Binding Interactions with Antibiotic Drugs. PLoS ONE, 6, e18414. https://doi.org/10.1371/journal.pone.0018414
[148]	Hu, L.L., Chen, C., Huang, T., Cai, Y.D. and Chou, K.C. (2011) Predicting Biological Functions of Compounds Based on Chemical-Chemical Interactions. PLoS ONE, 6, e29491. https://doi.org/10.1371/journal.pone.0029491
[149]	Hu, L., Huang, T., Shi, X., Lu, W.C., Cai, Y.D. and Chou, K.C. (2011) Predicting Functions of Proteins in Mouse Based on Weighted Protein-Protein Interaction Network and Protein Hybrid Properties. PLoS ONE, 6, e14556. https://doi.org/10.1371/journal.pone.0014556
[150]	Hu, L.L., Niu, S., Shi, X.H., Cai, Y.D. and Chou, K.C. (2011) Prediction and Analysis of Protein Palmitoylation Sites. Biochimie, 93, 489-496. https://doi.org/10.1016/j.biochi.2010.10.022
[151]	Du, Q.S., Wei, H., Huang, R.B. and Chou, K.C. (2011) Progress in Structure-Based Drug Design against Influenza A Virus. Expert Opinion, 6, 619-631. https://doi.org/10.1517/17460441.2011.571671
[152]	Xiao, X., Wang, P. and Chou, K.C. (2011) Quat-2L: A Web-Server for Predicting Protein Quaternary Structural Attributes. Molecular Diversity, 15, 149-155. https://doi.org/10.1007/s11030-010-9227-8
[153]	Wen, Z.Z., Wang, Y.H., Yang, B., Xie, M.Q. and Chou, K.C. (2011) Roles of L5-7 Loop in the Structure and Chaperone Function of SsHSP14.1. Protein & Peptide Letters, 18, 275-281. https://doi.org/10.2174/092986611794578369
[154]	Chou, K.C. (2011) Some Remarks on Protein Attribute Prediction and Pseudo Amino Acid Composition (50th Anniversary Year Review, 5-Steps Rule). Journal of Theoretical Biology, 273, 236-247. https://doi.org/10.1016/j.jtbi.2010.12.024
[155]	Xiao, X. and Chou, K.C. (2011) Using Pseudo Amino Acid Composition to Predict Protein Attributes via Cellular Automata and Other Approaches. Current Bioinformatics, 6, 251-260. https://doi.org/10.2174/1574893611106020251
[156]	Chou, K.C., Lin, W.Z. and Xiao, X. (2011) Wenxiang: A Web-Server for Drawing Wenxiang Diagrams. Natural Science, 3, 862-865. https://doi.org/10.4236/ns.2011.310111
[157]	Ma, Y., Wang, S.Q., Xu, W.R., Wang, R.L. and Chou, K.C. (2012) Design Novel Dual Agonists for Treating Type-2 Diabetes by Targeting Peroxisome Proliferator-Activated Receptors with Core Hopping Approach. PLoS ONE, 7, e38546. https://doi.org/10.1371/journal.pone.0038546
[158]	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
[159]	Xiao, X., Wang, P. and Chou, K.C. (2012) iNR-PhysChem: A Sequence-Based Predictor for Identifying Nuclear Receptors and Their Subfamilies via Physical-Chemical Property Matrix. PLoS ONE, 7, e30869. https://doi.org/10.1371/journal.pone.0030869
[160]	Wang, J.F. and Chou, K.C. (2012) Insights into the Mutation-Induced HHH Syndrome from Modeling Human Mitochondrial Ornithine Transporter-1. PLoS ONE, 7, e31048. https://doi.org/10.1371/journal.pone.0031048
[161]	Chen, 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.0047843
[162]	Xiao, X., Lin, W.Z. and Chou, K.C. (2012) Recent Advances in Predicting G-Protein Coupled Receptor Classification. Current Bioinformatics, 7, 132-142. https://doi.org/10.2174/157489312800604426
[163]	Liu, B., Zhang, D., Xu, R., Xu, J., Wang, X., Chen, Q., Dong, Q. and Chou, K.C. (2014) Combining Evolutionary Information Extracted from Frequency Profiles with Sequence-Based Kernels for Protein Remote Homology Detection. Bioinformatics, 30, 472-479. https://doi.org/10.1093/bioinformatics/btt709
[164]	Ding, H., Deng, E.Z., Yuan, L.F., Li, 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/10.1155/2014/286419
[165]	Liu, B., Xu, J., Lan, X., Xu, R., Zhou, J., Wang, X. and Chou, K.C. (2014) iDNA-Prot|dis: Identifying DNA-Binding Proteins by Incorporating Amino Acid Distance-Pairs and Reduced Alphabet Profile into the General Pseudo Amino Acid Composition. PLoS ONE, 9, e106691. https://doi.org/10.1371/journal.pone.0106691
[166]	Xu, 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/ijms15057594
[167]	Xu, 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.0105018
[168]	Fan, Y.N., Xiao, X., Min, J.L. and Chou, K.C. (2014) iNR-Drug: Predicting the Interaction of Drugs with Nuclear Receptors in Cellular Networking. International Journal of Molecular Sciences (IJMS), 15, 4915-4937. https://doi.org/10.3390/ijms15034915
[169]	Guo, 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/btu083
[170]	Lin, 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/gku1019
[171]	Qiu, W.R., Xiao, X. and Chou, K.C. (2014) iRSpot-TNCPseAAC: Identify Recombination Spots with Trinucleotide Composition and Pseudo Amino Acid Components. International Journal of Molecular Sciences (IJMS), 15, 1746-1766. https://doi.org/10.3390/ijms15021746
[172]	Chen, 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/10.1155/2014/623149
[173]	Chen, 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.2014.06.022
[174]	Chen, 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.2014.04.001
[175]	Liu, J., Song, J., Wang, M.Y., He, L., Cai, L. and Chou, K.C. (2015) Association of EGF rs4444903 and XPD rs13181 Polymorphisms with Cutaneous Melanoma in Caucasians. Medicinal Chemistry, 11, 551-559. https://doi.org/10.2174/1573406410666141224115516
[176]	Xu, R., Zhou, J., Liu, B., He, Y.A., Zou, Q., Wang, X. and Chou, K.C. (2015) Identification of DNA-Binding Proteins by Incorporating Evolutionary Information into Pseudo Amino Acid Composition via the Top-n-Gram Approach. Journal of Biomolecular Structure & Dynamics (JBSD), 33, 1720-1730. https://doi.org/10.1080/07391102.2014.968624
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[178]	Liu, Z., Xiao, X., Qiu, W.R. and Chou, K.C. (2015) iDNA-Methyl: Identifying DNA Methylation Sites via Pseudo Trinucleotide Composition. Analytical Biochemistry, 474, 69-77. https://doi.org/10.1016/j.ab.2014.12.009
[179]	Xiao, X., Min, J.L., Lin, W.Z., Liu, Z., Chen, X. and Chou, K.C. (2015) iDrug-Target: Predicting the Interactions between Drug Compounds and Target Proteins in Cellular Networking via the Benchmark Dataset Optimization Approach. Journal of Biomolecular Structure and Dynamics (JBSD), 33, 2221-2233. https://doi.org/10.1080/07391102.2014.998710
[180]	Chou, K.C. (2015) Impacts of Bioinformatics to Medicinal Chemistry. Medicinal Chemistry, 11, 218-234. https://doi.org/10.2174/1573406411666141229162834
[181]	Jia, J., Liu, Z., Xiao, X. and Chou, K.C. (2015) iPPI-Esml: An Ensemble Classifier for Identifying the Interactions of Proteins by Incorporating Their Physicochemical Properties and Wavelet Transforms into PseAAC. Journal of Theoretical Biology, 377, 47-56. https://doi.org/10.1016/j.jtbi.2015.04.011
[182]	Liu, B., Liu, F., Fang, L., Wang, X. and Chou, K.C. (2015) repDNA: A Python Package to Generate Various Modes of Feature Vectors for DNA Sequences by Incorporating User-Defined Physicochemical Properties and Sequence-Order Effects. Bioinformatics, 31, 1307-1309. https://doi.org/10.1093/bioinformatics/btu820
[183]	Chou, K.C. (2015) An Unprecedented Revolution in Medicinal Science. Proceedings of the MOL2NET (International Conference on Multidisciplinary Sciences), Vol. 1, 1-10. https://doi.org/10.3390/MOL2NET-1-b040
[184]	Liu, L., Ma, Y.R., Wang, L., Xu, W.R., Wang, S.Q. and Chou, K.C. (2013) Find Novel Dual-Agonist Drugs for Treating Type 2 Diabetes by Means of Cheminformatics. Drug Design, Development and Therapy, 7, 279-287. https://doi.org/10.2147/DDDT.S42113
[185]	Xiao, X., Wang, P., Lin, W.Z., Jia, J.H. and Chou, K.C. (2013) iAMP-2L: A Two-Level Multi-Label Classifier for Identifying Antimicrobial Peptides and Their Functional Types. Analytical Biochemistry, 436, 168-177. https://doi.org/10.1016/j.ab.2013.01.019
[186]	Min, 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/10.1155/2013/701317
[187]	Xiao, X., Min, J.L., Wang, P. and Chou, K.C. (2013) iGPCR-Drug: A Web Server for Predicting Interaction between GPCRs and Drugs in Cellular Networking. PLoS ONE, 8, e72234. https://doi.org/10.1371/journal.pone.0072234
[188]	Feng, 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.2013.05.024
[189]	Lin, W.Z., Fang, J.A., Xiao, X. and Chou, K.C. (2013) iLoc-Animal: A Multi-Label Learning Classifier for Predicting Subcellular Localization of Animal Proteins. Molecular Biosystems, 9, 634-644. https://doi.org/10.1039/c3mb25466f
[190]	Chen, 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/gks1450
[191]	Xu, 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.171
[192]	Xu, 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.0055844
[193]	Ji, Y., Li, Y.X., Cai, Y.D. and Chou, K.C. (2013) Metagenome Assembly Validation: Which Metagenome Contigs Are Bona Fide? Current Bioinformatics, 8, 511-523. https://doi.org/10.2174/1574893611308040013
[194]	Wang, J.F. and Chou, K.C. (2013) Metallo-Beta-Lactamases: Structural Features, Antibiotic Recognition, Inhibition, and Inhibitor Design. Current Topics in Medicinal Chemistry, 13, 1242-1253. https://doi.org/10.2174/15680266113139990011
[195]	Xiao, X., Min, J.L., Wang, P. and Chou, K.C. (2013) Predict Drug-Protein Interaction in Cellular Networking. Current Topics in Medicinal Chemistry, 13, 1707-1712. https://doi.org/10.2174/15680266113139990121
[196]	Xiao, X., Lin, W.Z. and Chou, K.C. (2013) Recent Advances in Predicting Protein Classification and Their Applications to Drug Development. Current Topics in Medicinal Chemistry, 13, 1622-1635. https://doi.org/10.2174/15680266113139990113
[197]	Xiao, X., Wang, P. and Chou, K.C. (2013) Recent Progresses in Identifying Nuclear Receptors and Their Families. Current Topics in Medicinal Chemistry, 13, 1192-1200. https://doi.org/10.2174/15680266113139990006
[198]	Chou, K.C. (2013) Some Remarks on Predicting Multi-Label Attributes in Molecular Biosystems. Molecular Biosystems, 9, 1092-1100. https://doi.org/10.1039/c3mb25555g
[199]	Zhu, Y., Cong, Q.W., Liu, Y., Wan, C.L., Yu, T., He, G., He, L., Cai, L. and Chou, K.C. (2016) Antithrombin Is an Importantly Inhibitory Role against Blood Clots. Current Topics in Medicinal Chemistry, 16, 666-674. https://doi.org/10.2174/1568026616666150923152745
[200]	Chen, J., Long, X.R., Wang, L., Liu, B. and Chou, K.C. (2016) dRHP-PseRA: Detecting Remote Homology Proteins Using Profile-Based Pseudo Protein Sequence and Rank Aggregation. Scientific Reports, 6, Article No. 32333. https://doi.org/10.1038/srep32333
[201]	Chen, 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.7815
[202]	Jia, J., Liu, Z., Xiao, X., Liu, B. and Chou, K.C. (2016) iCar-PseCp: Identify Carbonylation Sites in Proteins by Monto Carlo Sampling and Incorporating Sequence Coupled Effects into General PseAAC. Oncotarget, 7, 34558-34570. https://doi.org/10.18632/oncotarget.9148
[203]	Jia, J., Liu, Z., Xiao, X., Liu, B. and Chou, K.C. (2016) Identification of Protein-Protein Binding Sites by Incorporating the Physicochemical Properties and Stationary Wavelet Transforms into Pseudo Amino Acid Composition (iPPBS-PseAAC). Journal of Biomolecular Structure and Dynamics (JBSD), 34, 1946-1961. https://doi.org/10.1080/07391102.2015.1095116
[204]	Liu, B., Long, R. and Chou, K.C. (2016) iDHS-EL: Identifying DNase I Hypersensitive Sites by Fusing Three Different Modes of Pseudo Nucleotide Composition into an Ensemble Learning Framework. Bioinformatics, 32, 2411-2418. https://doi.org/10.1093/bioinformatics/btw186
[205]	Liu, B., Fang, L., Long, R., Lan, X. and Chou, K.C. (2016) iEnhancer-2L: A Two-Layer Predictor for Identifying Enhancers and Their Strength by Pseudo k-Tuple Nucleotide Composition. Bioinformatics, 32, 362-369. https://doi.org/10.1093/bioinformatics/btv604
[206]	Qiu, W.R., Sun, B.Q., Xiao, X., Xu, Z.C. and Chou, K.C. (2016) iHyd-PseCp: Identify Hydroxyproline and Hydroxylysine in Proteins by Incorporating Sequence-Coupled Effects into General PseAAC. Oncotarget, 7, 44310-44321. https://doi.org/10.18632/oncotarget.10027
[207]	Cai, L., Yuan, W., Zhang, Z., He, L. and Chou, K.C. (2016) In-Depth Comparison of Somatic Point Mutation Callers Based on Different Tumor Next-Generation Sequencing Depth Data. Scientific Reports, 6, Article No. 36540. https://doi.org/10.1038/srep36540
[208]	Zhang, C.J., Tang, H., Li, W.C., Lin, H., Chen, W., 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.11975
[209]	Qiu, W.R., Xiao, X., Xu, Z.C. and Chou, K.C. (2016) iPhos-PseEn: Identifying Phosphorylation Sites in Proteins by Fusing Different Pseudo Components into an Ensemble Classifier. Oncotarget, 7, 51270-51283. https://doi.org/10.18632/oncotarget.9987
[210]	Jia, J., Liu, Z., Xiao, X., Liu, B. and Chou, K.C. (2016), iPPBS-Opt: A Sequence-Based Ensemble Classifier for Identifying Protein-Protein Binding Sites by Optimizing Imbalanced Training Datasets. Molecules, 21, E95. https://doi.org/10.3390/molecules21010095
[211]	Qiu, W.R., Sun, B.Q., Xiao, X., Xu, Z.C. and Chou, K.C. (2016) iPTM-mLys: Identifying Multiple Lysine PTM Sites and Their Different Types. Bioinformatics, 32, 3116-3123. https://doi.org/10.1093/bioinformatics/btw380
[212]	Chen, W., Tang, H., Ye, J., Lin, H. and Chou, K.C. (2016) iRNA-PseU: Identifying RNA Pseudouridine Sites. Molecular Therapy—Nucleic Acids, 5, e332.
[213]	Xiao, X., Ye, H.X., Liu, Z., Jia, J.H. and Chou, K.C. (2016) iROS-gPseKNC: Predicting Replication Origin Sites in DNA by Incorporating Dinucleotide Position-Specific Propensity into General Pseudo Nucleotide Composition. Oncotarget, 7, 34180-34189. https://doi.org/10.18632/oncotarget.9057
[214]	Jia, J., Liu, Z., Xiao, X., Liu, B. and Chou, K.C. (2016) iSuc-PseOpt: Identifying Lysine Succinylation Sites in Proteins by Incorporating Sequence-Coupling Effects into Pseudo Components and Optimizing Imbalanced Training Dataset. Analytical Biochemistry, 497, 48-56. https://doi.org/10.1016/j.ab.2015.12.009
[215]	Cai, L., Yang, Y.H., He, L. and Chou, K.C. (2016) Modulation of Cytokine Network in the Comorbidity of Schizophrenia and Tuberculosis. Current Topics in Medicinal Chemistry, 16, 655-665. https://doi.org/10.2174/1568026615666150819105033
[216]	Vaseghi, A., Faridounnia, M., Shokrollahzade, S., Jahandideh, S. and Chou, K.C. (2016) Prediction of Protein Quaternary Structures in Pattern Recognition. In: Elloumi, M., Iliopoulos, C.S., Wang, J.T.L. and Zomaya, A.Y., Eds., Computational Molecular Biology: Techniques and Approaches, John Wiley & Sons, Hoboken, Chap. 14. https://doi.org/10.1002/9781119078845.ch14
[217]	Liu, Z., Xiao, X., Yu, D.J., Jia, J., Qiu, W.R. and Chou, K.C. (2016) pRNAm-PC: Predicting N-methyladenosine Sites in RNA Sequences via Physical-Chemical Properties. Analytical Biochemistry, 497, 60-67. https://doi.org/10.1016/j.ab.2015.12.017
[218]	Jia, J., Liu, Z., Xiao, X., Liu, B. and Chou, K.C. (2016) pSuc-Lys: Predict Lysine Succinylation Sites in Proteins with PseAAC and Ensemble Random Forest Approach. Journal of Theoretical Biology, 394, 223-230. https://doi.org/10.1016/j.jtbi.2016.01.020
[219]	Jia, J., Zhang, L., Liu, Z., Xiao, X. and Chou, K.C. (2016) pSumo-CD: Predicting Sumoylation Sites in Proteins with Covariance Discriminant Algorithm by Incorporating Sequence-Coupled Effects into General PseAAC. Bioinformatics, 32, 3133-3141. https://doi.org/10.1093/bioinformatics/btw387
[220]	Xu, 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/10.2174/1568026615666150819110421
[221]	Liu, B., Liu, F., Fang, L., Wang, X. and Chou, K.C. (2016) repRNA: A Web Server for Generating Various Feature Vectors of RNA Sequences. Molecular Genetics and Genomics, 291, 473-481. https://doi.org/10.1007/s00438-015-1078-7
[222]	Chen, 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.2015.12.005
[223]	Du, Q.S., Wang, S.Q., Xie, N.Z., Wang, Q.Y., Huang, R.B. and Chou, K.C. (2017) 2L-PCA: A Two-Level Principal Component Analyzer for Quantitative Drug Design and Its Applications. Oncotarget, 8, 70564-70578. https://doi.org/10.18632/oncotarget.19757
[224]	Liu, B., Yang, F. and Chou, K.C. (2017) 2L-piRNA: A Two-Layer Ensemble Classifier for Identifying piwi-Interacting RNAs and Their Function. Molecular Therapy—Nucleic Acids, 7, 267-277. https://doi.org/10.1016/j.omtn.2017.04.008
[225]	Zhang, Z.D., Liang, K., Li, K., Wang, G.Q., Zhang, K.W., Cai, L., Zha, S.T. and Chou, K.C. (2017) Chlorella vulgaris Induces Apoptosis of Human Non-Small Cell Lung Carcinoma (NSCLC) Cells. Medicinal Chemistry, 13, 560-568. https://doi.org/10.2174/1573406413666170510102024
[226]	Chen, X., Zhao, S.G., Xiao, X. and Chou, K.C. (2017) iATC-mHyb: A Hybrid Multi-Label Classifier for Predicting the Classification of Anatomical Therapeutic Chemicals. Oncotarget, 8, 58494-58503. https://doi.org/10.18632/oncotarget.17028
[227]	Cheng, X., Zhao, S.G., Xiao, X. and Chou, K.C. (2017) iATC-mISF: A Multi-Label Classifier for Predicting the Classes of Anatomical Therapeutic Chemicals. Bioinformatics, 33, 341-346. (Corrigendum, ibid., 2017, Vol. 33, 2610) https://doi.org/10.1093/bioinformatics/btx387
[228]	Liu, 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/10.2174/1573406413666170515120507
[229]	Qiu, W.R., Sun, B.Q., Xiao, X., Xu, D. and Chou, K.C. (2017) iPhos-PseEvo: Identifying Human Phosphorylated Proteins by Incorporating Evolutionary Information into General PseAAC via Grey System Theory. Molecular Informatics, 36, UNSP 1600010. https://doi.org/10.1002/minf.201600010
[230]	Xu, 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/10.2174/1573406413666170419150052
[231]	Qiu, W.R., Jiang, S.Y., Sun, B.Q., Xiao, X., Cheng, X. and Chou, K.C. (2017) iRNA-2methyl: Identify RNA 2’-O-methylation Sites by Incorporating Sequence-Coupled Effects into General PseKNC and Ensemble Classifier. Medicinal Chemistry, 13, 734-743. https://doi.org/10.2174/1573406413666170623082245
[232]	Chen, 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.13758
[233]	Feng, 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.2017.03.006
[234]	Qiu, W.R., Jiang, S.Y., Xu, Z.C., Xiao, X. and Chou, K.C. (2017) iRNAm5C-PseDNC: Identifying RNA 5-Methylcytosine Sites by Incorporating Physical-Chemical Properties into Pseudo Dinucleotide Composition. Oncotarget, 8, 41178-41188. https://doi.org/10.18632/oncotarget.17104
[235]	Liu, B., Wang, S., Long, R. and Chou, K.C. (2017) iRSpot-EL: Identify Recombination Spots with an Ensemble Learning Approach. Bioinformatics, 33, 35-41. https://doi.org/10.1093/bioinformatics/btw539
[236]	Cheng, X., Zhao, S.G., Lin, W.Z., Xiao, X. and Chou, K.C. (2017) pLoc-mAnimal: Predict Subcellular Localization of Animal Proteins with Both Single and Multiple Sites. Bioinformatics, 33, 3524-3531. https://doi.org/10.1093/bioinformatics/btx476
[237]	Xiao, X., Cheng, X., Su, S., Nao, Q. and Chou, K.C. (2017) pLoc-mGpos: Incorporate Key Gene Ontology Information into General PseAAC for Predicting Subcellular Localization of Gram-Positive Bacterial Proteins. Natural Science, 9, 330-349. https://doi.org/10.4236/ns.2017.99032
[238]	Cheng, X., Xiao, X. and Chou, K.C. (2017) pLoc-mPlant: Predict Subcellular Localization of Multi-Location Plant Proteins via Incorporating the Optimal GO Information into General PseAAC. Molecular Biosystems, 13, 1722-1727. https://doi.org/10.1039/C7MB00267J
[239]	Cheng, X., Xiao, X. and Chou, K.C. (2017) pLoc-mVirus: Predict Subcellular Localization of Multi-Location Virus Proteins via Incorporating the Optimal GO Information into General PseAAC. Gene, 628, 315-321. (Erratum: ibid., 2018, Vol. 644, 156-156) https://doi.org/10.1016/j.gene.2017.10.042
[240]	Wang, J., Yang, B., Revote, J., Leier, A., Marquez-Lago, T.T., Webb, G., Song, J., Chou, K.C. and Lithgow, T. (2017) POSSUM: A Bioinformatics Toolkit for Generating Numerical Sequence Feature Descriptors Based on PSSM Profiles. Bioinformatics, 33, 2756-2758. https://doi.org/10.1093/bioinformatics/btx302
[241]	Su, Q., Lu, W., Du, D., Chen, F., Niu, B. and Chou, K.C. (2017) Prediction of the Aquatic Toxicity of Aromatic Compounds to Tetrahymena Pyriformis through Support Vector Regression. Oncotarget, 8, 49359-49369. https://doi.org/10.18632/oncotarget.17210
[242]	Liu, B., Wu, H., Zhang, D., Wang, X. and Chou, K.C. (2017) Pse-Analysis: A Python Package for DNA/RNA and Protein/Peptide Sequence Analysis Based on Pseudo Components and Kernel Methods. Oncotarget, 8, 13338-13343. https://doi.org/10.18632/oncotarget.14524
[243]	Liu, B., Wu, H. and Chou, K.C. (2017) 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, 67-91. https://doi.org/10.4236/ns.2017.94007
[244]	Niu, B., Zhang, M., Du, P., Jiang, L., Qin, R., Su, Q., Chen, F., Du, D., Shu, Y. and Chou, K.C. (2017) Small Molecular Floribundiquinone B Derived from Medicinal Plants Inhibits Acetylcholinesterase Activity. Oncotarget, 8, 57149-57162. https://doi.org/10.18632/oncotarget.19169
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[246]	Wang, J., Li, J., Yang, B., Xie, R., Marquez-Lago, T.T., Leier, A., Hayashida, M., Akutsu, T., Zhang, Y., Chou, K.C., Selkrig, J., Zhou, T., Song, J. and Lithgow, T. (2018) Bastion3: A Two-Layer Approach for Identifying Type III Secreted Effectors Using Ensemble Learning. Bioinformatics, 35, 2017-2028. https://doi.org/10.1093/bioinformatics/bty914
[247]	Wang, J., Yang, B., Leier, A., Marquez-Lago, T.T., Hayashida, M., Rocker, A., Yanju, Z., Akutsu, T., Chou, K.C., Strugnell, R.A., Song, J. and Lithgow, T. (2018) Bastion6: A Bioinformatics Approach for Accurate Prediction of Type VI Secreted Effectors. Bioinformatics, 34, 2546-2555. https://doi.org/10.1093/bioinformatics/bty155
[248]	Zhang, Y., Xie, R., Wang, J., Leier, A., Marquez-Lago, T.T., Akutsu, T., Webb, G.I., Chou, K.C. and Song, J. (2018) Computational Analysis and Prediction of Lysine Malonylation Sites by Exploiting Informative Features in an Integrative Machine-Learning Framework. Brief in Bioinform, 20, 2185-2199. https://doi.org/10.1093/bib/bby079
[249]	Liu, B., Li, K., Huang, D.S. and Chou, K.C. (2018) iEnhancer-EL: Identifying Enhancers and Their Strength with Ensemble Learning Approach. Bioinformatics, 34, 3835-3842. https://doi.org/10.1093/bioinformatics/bty458
[250]	Chen, Z., Zhao, P.Y., Li, F., Leier, A., Marquez-Lago, T.T., Wang, Y., Webb, G.I., Smith, A.I., Daly, R.J., Chou, K.C. and Song, J. (2018) iFeature: A Python Package and Web Server for Features Extraction and Selection from Protein and Peptide Sequences. Bioinformatics, 34, 2499-2502. https://doi.org/10.1093/bioinformatics/bty140
[251]	Qiu, W.R., Sun, B.Q., Xiao, X., Xu, Z.C., Jia, J.H. and Chou, K.C. (2018) iKcr-PseEns: Identify Lysine Crotonylation Sites in Histone Proteins with Pseudo Components and Ensemble Classifier. Genomics, 110, 239-246. https://doi.org/10.1016/j.ygeno.2017.10.008
[252]	Su, Z.D., Huang, Y., Zhang, Z.Y., Zhao, Y.W., Wang, D., Chen, W., Chou, K.C. and Lin, H. (2018) iLoc-lncRNA: Predict the Subcellular Location of lncRNAs by Incorporating Octamer Composition into General PseKNC. Bioinformatics, 34, 4196-4204. https://doi.org/10.1093/bioinformatics/bty508
[253]	Cai, 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.2018.05.026
[254]	Khan, Y.D., Rasool, N., Hussain, W., Khan, S.A. and Chou, K.C. (2018) iPhosT-PseAAC: Identify Phosphothreonine Sites by Incorporating Sequence Statistical Moments into PseAAC. Analytical Biochemistry, 550, 109-116. https://doi.org/10.1016/j.ab.2018.04.021
[255]	Khan, Y.D., Rasool, N., Hussain, W., Khan, S.A. and Chou, K.C. (2018) iPhosY-PseAAC: Identify Phosphotyrosine Sites by Incorporating Sequence Statistical Moments into PseAAC. Molecular Biology Reports, 45, 2501-2509. https://doi.org/10.1007/s11033-018-4417-z
[256]	Liu, B., Yang, F., Huang, D.S. and Chou, K.C. (2018) iPromoter-2L: A Two-Layer Predictor for Identifying Promoters and Their Types by Multi-Window-Based PseKNC. Bioinformatics, 34, 33-40. https://doi.org/10.1093/bioinformatics/btx579
[257]	Song, J., Wang, Y., Li, F., Akutsu, T., Rawlings, N.D., Webb, G.I. and Chou, K.C. (2018) iProt-Sub: A Comprehensive Package for Accurately Mapping and Predicting Protease-Specific Substrates and Cleavage Sites. Brief in Bioinform, 20, 638-658. https://doi.org/10.1093/bib/bby028
[258]	Chen, 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. Molecular Therapy: Nucleic Acid, 11, 468-474. https://doi.org/10.1016/j.omtn.2018.03.012
[259]	Chen, 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.2018.09.002
[260]	Liu, B., Weng, F., Huang, D.S. and Chou, K.C. (2018) iRO-3wPseKNC: Identify DNA Replication Origins by Three-Window-Based PseKNC. Bioinformatics, 34, 3086-3093. https://doi.org/10.1093/bioinformatics/bty312
[261]	Yang, H., Qiu, W.R., Liu, G., Guo, F.B., Chen, W., Chou, K.C. and Lin, H. (2018) iRSpot-Pse6NC: Identifying Recombination Spots in Saccharomyces cerevisiae by Incorporating Hexamer Composition into General PseKNC. International Journal of Biological Sciences, 14, 883-891. https://doi.org/10.7150/ijbs.24616
[262]	Chen, Z., Liu, X., Li, F., Li, C., Marquez-Lago, T., Leier, A., Akutsu, T., Webb, G.I., Xu, D., Smith, A.I., Li, L., Chou, K.C. and Song, J. (2018) Large-Scale Comparative Assessment of Computational Predictors for Lysine Post-Translational Modification Sites. Brief in Bioinform, 20, 2267-2290. https://doi.org/10.1093/bib/bby089
[263]	Ehsan, A., Mahmood, K., Khan, Y.D., Khan, S.A. and Chou, K.C. (2018) A Novel Modeling in Mathematical Biology for Classification of Signal Peptides. Scientific Reports, 8, Article No. 1039. https://doi.org/10.1038/s41598-018-19491-y
[264]	Cheng, X., Xiao, X. and Chou, K.C. (2018) pLoc_bal-mGneg: Predict Subcellular Localization of Gram-Negative Bacterial Proteins by Quasi-Balancing Training Dataset and General PseAAC. Journal of Theoretical Biology, 458, 92-102. https://doi.org/10.1016/j.jtbi.2018.09.005
[265]	Cheng, X., Xiao, X. and Chou, K.C. (2018) pLoc_bal-mPlant: Predict Subcellular Localization of Plant Proteins by General PseAAC and Balancing Training Dataset. Current Pharmaceutical Design, 24, 4013-4022. https://doi.org/10.2174/1381612824666181119145030
[266]	Cheng, X., Xiao, X. and Chou, K.C. (2018) pLoc-mEuk: Predict Subcellular Localization of Multi-Label Eukaryotic Proteins by Extracting the Key GO Information into General PseAAC. Genomics, 110, 50-58. https://doi.org/10.1016/j.ygeno.2017.08.005
[267]	Cheng, X., Xiao, X. and Chou, K.C. (2018) pLoc-mGneg: Predict Subcellular Localization of Gram-Negative Bacterial Proteins by Deep Gene Ontology Learning via General PseAAC. Genomics, 110, 231-239. https://doi.org/10.1016/j.ygeno.2017.10.002
[268]	Cheng, X., Xiao, X. and Chou, K.C. (2018) pLoc-mHum: Predict Subcellular Localization of Multi-Location Human Proteins via General PseAAC to Winnow out the Crucial GO Information. Bioinformatics, 34, 1448-1456. https://doi.org/10.1093/bioinformatics/btx711
[269]	Song, J., Li, F., Takemoto, K., Haffari, G., Akutsu, T., Chou, K.C. and Webb, G.I. (2018) PREvaIL, an Integrative Approach for Inferring Catalytic Residues Using Sequence, Structural and Network Features in a Machine Learning Framework. Journal of Theoretical Biology, 443, 125-137. https://doi.org/10.1016/j.jtbi.2018.01.023
[270]	Song, J., Li, F., Leier, A., Marquez-Lago, T.T., Akutsu, T., Haffari, G., Chou, K.C., Webb, G.I. and Pike, R.N. (2018) PROSPERous: High-Throughput Prediction of Substrate Cleavage Sites for 90 Proteases with Improved Accuracy. Bioinformatics, 34, 684-687. https://doi.org/10.1093/bioinformatics/btx670
[271]	Li, F., Li, C., Marquez-Lago, T.T., Leier, A., Akutsu, T., Purcell, A.W., Smith, A.I., Lightow, T., Daly, R.J., Song, J. and Chou, K.C. (2018) Quokka: A Comprehensive Tool for Rapid and Accurate Prediction of Kinase Family-Specific Phosphorylation Sites in the Human Proteome. Bioinformatics, 34, 4223-4231. https://doi.org/10.1093/bioinformatics/bty522
[272]	Li, F., Wang, Y., Li, C., Marquez-Lago, T.T., Leier, A., Rawlings, N.D., Haffari, G., Revote, J., Akutsu, T., Chou, K.C., Purcell, A.W., Pike, R.N., Webb, G.I., Ian Smith, A., Lithgow, T., Daly, R.J., Whisstock, J.C. and Song, J. (2018) Twenty Years of Bioinformatics Research for Protease-Specific Substrate and Cleavage Site Prediction: A Comprehensive Revisit and Benchmarking of Existing Methods. Brief in Bioinform, 20, 2150-2166. https://doi.org/10.1093/bib/bby077
[273]	Khan, Y.D., Jamil, M., Hussain, W., Rasool, N., Khan, S.A. and Chou, K.C. (2019) pSSbond-PseAAC: Prediction of Disulfide Bonding Sites by Integration of PseAAC and Statistical Moments. Journal of Theoretical Biology, 463, 47-55. https://doi.org/10.1016/j.jtbi.2018.12.015
[274]	Chou, K.C. (2019) Recent Progresses in Predicting Protein Subcellular Localization with Artificial Intelligence (AI) Tools Developed via the 5-Steps Rule. Japanese Journal of Gastroenterology and Hepatology, 2, 1-4. https://www.jjgastrohepto.org/v2issue4.php
[275]	Chou, K.C. (2019) Recent Progresses in Predicting Protein Subcellular Localization with Artificial Intelligence Tools Developed via the 5-Steps Rule. Medicinal Chemistry.
[276]	Chou, K.C. (2019) Showcase to Illustrate How the Web-Server iDNA6mA-PseKNC Is Working. Journal of Pathology Research Reviews & Reports, 1, 1-15. https://doi.org/10.47363/JPR/2019(1)105
[277]	Chou, K.C. (2019) Showcase to Illustrate How the Web-Server iNitro-Tyr Is Working. Global Journal of Computer Science and Information Technology, 2, 1-16.
[278]	Chou, K.C. (2019) Showcase to Illustrate How the Web-Server pLoc_bal-mEuk Is Working. JSM Clinical Cytology and Pathology, 4, 1-2. https://doi.org/10.15761/CRT.1000310
[279]	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
[280]	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
[281]	Chou, K.C. (2019) Two Kinds of Metrics for Computational Biology. Genomics. https://doi.org/10.1016/j.ygeno.2019.08.008
[282]	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, 19, 756-799. https://doi.org/10.1007/s10989-019-09887-3
[283]	Chou, K.C. (2020) Coronavirus and Gordon Life Science Institute. Natural Science, 12, 429-440. https://doi.org/10.4236/ns.2020.127035
[284]	Chou, K.C. (2020) The Development of Gordon Life Science Institute: Its Driving Force and Accomplishments. Natural Science, 12, 202-217. https://doi.org/10.4236/ns.2020.124018
[285]	Chou, K.C. (2020) Distorted Key Theory and Its Implication for Drug Development. Current Genomics. http://www.eurekaselect.com/175823/article
[286]	Chou, K.C. (2020) The End of Our Earth Is Certainly to Come: “When” and “Why”? Natural Science, 12, 552-568. https://doi.org/10.4236/ns.2020.128043
[287]	Niu, B., Liang, C., Lu, Y., Zhao, M., Chen, Q., Zhang, Y., Zheng, L. and Chou, K.C. (2020) Glioma Stages Prediction Based on Machine Learning Algorithm Combined with Protein-Protein Ineraction Networks. Genomics, 112, 837-847. https://doi.org/10.1016/j.ygeno.2019.05.024
[288]	Chou, K.C. (2020) Gordon Life Science Institute and Its Impacts on Computational Biology and Drug Development. Natural Science, 12, 125-161. https://doi.org/10.4236/ns.2020.123013
[289]	Chou, K.C. (2020) How the Artificial Intelligence Tool iHyd-PseAAC Is Working in Predicting the Hydroxyproline and Hydroxylysine in Proteins. Peer Reviewed Journal of Forensic & Genetic Sciences, 4, 272-274.
[290]	Chou, K.C. (2020) How the Artificial Intelligence Tool iHyd-PseAAC Is Working in Predicting the Hydroxyproline and Hydroxylysine in Proteins. ES Journal of Microbiology, 1, 1004-1006.
[291]	Chou, K.C. (2020) How the Artificial Intelligence Tool iPGK-PseAAC Is Working in Predicting Lysine Phosphoglycerylation Sites in Proteins. ES Journal of Microbiology, 1, 1003-1004.
[292]	Chou, K.C. (2020) How the Artificial Intelligence Tool iRNA-2methyl Is Working for RNA 2’-Omethylation Sites. Journal of Medical Care Research and Review, 3, 348-366.
[293]	Chou, K.C. (2020) How the Artificial Intelligence Tool iRNA-PseU Is Working in Predicting the RNA Pseudouridine Sites. Biomedical Journal of Scientific & Technical Research. https://doi.org/10.26717/BJSTR.2020.24.004016
[294]	Chou, K.C. (2020) How the Artificial Intelligence Tool iSuc-PseOpt Is Working for Predicting Lysine Succinylation Sites in Proteins. Biomedical Research and Clinical Reviews, 1, 1-2. https://doi.org/10.47363/JBBR/2020(2)102
[295]	Chou, K.C. (2020) How the Artificial Intelligence Tool pSumo-CD Is Working for Predicting Sumoylation Sites in Proteins. Journal of Biotechnology & Bioinformatics Research, 1, 1-3. https://doi.org/10.47363/JBBR/2020(2)102
[296]	Lu, Z. and Chou, K.C. (2020) iATC_Deep-mISF: A Multi-Label Classifier for Predicting the Classes of Anatomical Therapeutic Chemicals by Deep Learning. Advances in Bioscience and Biotechnology (ABB), 11, 153-159. https://doi.org/10.4236/abb.2020.115012
[297]	Chou, K.C. (2020) The Implication of “I Am the Alpha and the Omega” to Internet Institutes. Natural Science, 12, 482-494. https://doi.org/10.4236/ns.2020.127038
[298]	Chou, K.C. (2020) An Insightful 20-Year Recollection since the Birth of Pseudo Amino Acid Components. Amino Acids, 52, 847. https://doi.org/10.1007/s00726-020-02828-1
[299]	Chou, K.C. (2020) An Insightful Recollection for Predicting Protein Subcellular Locations in Multi-Label Systems. Natural Science, 12, 441-469. https://doi.org/10.4236/ns.2020.127036
[300]	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
[301]	Chou, K.C. (2020) The Most Important Ethical Concerns in Science. Natural Science, 12, 35-36. https://doi.org/10.4236/ns.2020.122005
[302]	Pugalenthi, G., Nithya, V., Chou, K.C. and Archunan, G. (2020) Nglyc: A Random Forest Method for Prediction of N-Glycosylation Sites in Eukaryotic Protein Sequence. Protein & Peptide Letters, 27, 178-186. https://doi.org/10.2174/0929866526666191002111404
[303]	Chou, K.C. (2020) Noah’s Ark and Internet Institutes: When and Why? Natural Science, 12, 470-481. https://doi.org/10.4236/ns.2020.127037
[304]	Chou, K.C. (2020) Other Mountain Stones Can Attack Jade: The 5-Steps Rule. Natural Science, 12, 59-64. https://doi.org/10.4236/ns.2020.123011
[305]	Chou, K.C. (2020) The Pandemic Pestilences and Internet Institutes. Natural Science, 12, 495-515. https://doi.org/10.4236/ns.2020.127039
[306]	Chou, K.C. (2020) The pLoc_bal-mGneg Predictor Is a Powerful Web-Server for Identifying the Subcellular Localization of Gram-Negative Bacterial Proteins based on their Sequences Information Alone. International Journal of Sciences, 9, 27-34. https://doi.org/10.18483/ijSci.2248
[307]	Chou, K.C. (2020) The pLoc_bal-mGpos Is a Powerful Artificial Intelligence Tool for Predicting the Subcellular Localization of Gram-Positive Bacterial Proteins According to Their Sequence Information Alone. Global Journal of Computer Science and Information Technology, 2, 1-13.
[308]	Chou, K.C. (2020) The pLoc_bal-mHum Is a Powerful Web-Serve for Predicting the Subcellular Localization of Human Proteins Purely Based on Their Sequence Information. Advances in Bioengineering and Biomedical Science Research, 3, 1-5. https://doi.org/10.33140/ABBSR.03.01.06
[309]	Chou, K.C. (2020) The pLoc_bal-mVirus Is a Powerful Artificial Intelligence Tool for Predicting the Subcellular Localization of Virus Proteins According to Their Sequence Information Alone. Journal of Genetics and Genomics, 4.
[310]	Shao, Y.T. and Chou, K.C. (2020) pLoc_Deep-mAnimal: A Novel Deep CNN-BLSTM Network to Predict Subcellular Localization of Animal Proteins. Natural Science, 12, 281-291. https://doi.org/10.4236/ns.2020.125024
[311]	Shao, Y.T. and Chou, K.C. (2020) pLoc_Deep-mEuk: Predict Subcellular Localization of Eukaryotic Proteins by Deep Learning. Natural Science, 12, 1-29. https://doi.org/10.4236/ns.2020.126034
[312]	Liu, X.X. and Chou, K.C. (2020) pLoc_Deep-mGneg: Predict Subcellular Localization of Gram Negative Bacterial Proteins by Deep Learning. Advances in Bioscience and Biotechnology (ABB), 11, 141-152. https://doi.org/10.4236/abb.2020.115011
[313]	Shao, Y.T., Liu, X.X., Lu, Z. and Chou, K.C. (2020) pLoc_Deep-mHum: Predict Subcellular Localization of Human Proteins by Deep Learning. Natural Science, 12, 526-547. https://doi.org/10.4236/ns.2020.127042
[314]	Shao, Y.T., Liu, X.X., Lu, Z. and Chou, K.C. (2020) pLoc_Deep-mPlant: Predict Subcellular Localization of Plant Proteins by Deep Learning. Natural Science, 12, 237-247. https://doi.org/10.4236/ns.2020.125021
[315]	Shao, Y.H. and Chou, K.C. (2020) pLoc_Deep-mVirus: A CNN Model for Predicting Subcellular Localization of Virus Proteins by Deep Learning. Natural Science, 12, 1-12. https://doi.org/10.4236/ns.2020.126033
[316]	Khan, S., Khan, M., Iqbal, N., Khan, S.A. and Chou, K.C. (2020) Prediction of Pirnas and Their Function Based on Discriminative Intelligent Model Using Hybrid Features into Chou’s PseKNC. Chemometrics and Intelligent Laboratory (CHEMOLAB), 203, Article ID: 104056. https://doi.org/10.1016/j.chemolab.2020.104056
[317]	Chou, K.C. (2020) The Problem of Elsevier Series Journals Online Submission by Using Artificial Intelligence. Natural Science, 12, 37-38. https://doi.org/10.4236/ns.2020.122006
[318]	Chou, K.C. (2020) Progresses in Predicting Post-Translational Modification (2019). International Journal of Peptide Research and Therapeutics (IJPRT), 26, 873-888. https://doi.org/10.1007/s10989-019-09893-5
[319]	Chou, K.C. (2020) Proposing 5-Steps Rule Is a Notable Milestone for Studying Molecular Biology. Natural Science, 12, 74-79. https://doi.org/10.4236/ns.2020.123011
[320]	Chou, K.C. (2020) Showcase to Illustrate How the Web-Server iATC_Deep-mISF Is Working. Global Journal of Science Frontier Research: G Bio-Tech & Genetics, 20, 1-3.
[321]	Chou, K.C. (2020) Showcase to Illustrate How the Web-Server iPreny-PseAAC Is Working. Global Journal of Computer Science and Information Technology, 2, 1-15.
[322]	Chou, K.C. (2020) Showcase to Illustrate How the Web-Server iPTM-mLys Is Working. Infotext Journal of Infectious Diseases and Therapy [IJID], 1, 1-16.
[323]	Chou, K.C. (2020) Showcase to Illustrate How the Web-Server iRNA-Methyl Is Working. Journal of Molecular Genetics, 3, 1-7. https://doi.org/10.15761/CRT.1000310
[324]	Chou, K.C. (2020) Showcase to Illustrate How the Web-Server iSNO-AAPair Is Working. Journal of Genetics and Genomics, 4, 59. https://doi.org/10.15761/CRT.1000310
[325]	Chou, K.C. (2020) Showcase to Illustrate How the Web-Server iSulf_Wide-PseAAC Is Working. Natural Science, 12, 620-631. https://doi.org/10.4236/ns.2020.128047
[326]	Chou, K.C. (2020) Showcase to Illustrate How the Web-Server pLoc_Deep-mAnimal Is Working. American Journal of Virology & Disease, 2, 1-2.
[327]	Chou, K.C. (2020) Showcase to Illustrate How the Web-Server pLoc_Deep-mEuk Is Working. Advances in Bioscience and Biotecnology (ABB), 11, 257-272. https://doi.org/10.4236/abb.2020.117019
[328]	Chou, K.C. (2020) Showcase to Illustrate How the Web-Server pLoc_Deep-mGneg Is Working. Clinical Medicine. Case Reports, 1, 1-2. https://doi.org/10.47363/JPR/2019(1)105
[329]	Lu, Z. and Chou, K.C. (2020) Showcase to Illustrate How the Web-Server pLoc_Deep-mGpos Is Working. Journal of Biomedical Science and Engineering, 13, 55-65. https://doi.org/10.4236/jbise.2020.135005
[330]	Chou, K.C. (2020) Showcase to Illustrate How the Web-Server pLoc_Deep-mHum Is Working. Advances in Bioscience and Biotechnology (ABB), 11, 273-288. https://doi.org/10.4236/abb.2020.117020
[331]	Chou, K.C. (2020) Showcase to Illustrate How the Web-Server pLoc_Deep-mPlant Is Working. Integrative Molecular Biology and Biotechnology, 1, 1-2.
[332]	Chou, K.C. (2020) Showcase to Illustrate How the Web-Server pLoc_Deep-mVirus Is Working. Clinical Research and Trials, 6, 1-2. https://doi.org/10.15761/CRT.1000310
[333]	Chou, K.C. (2020) Showcase to Illustrate How the Webserver pLoc_bal-mEuk Is Working. Biomedical Journal of Scientific & Technical Research, 24, 18156-18160. https://doi.org/10.26717/BJSTR.2020.24.004033
[334]	Chou, K.C. (2020) Showcase to Illustrate How the Webserver pLoc_Deep-mGpos Is Working. Open Access Journal of Biomedical Science, 2, 345-346. https://doi.org/10.26717/BJSTR.2020.24.004033
[335]	Chou, K.C. (2020) Some Illuminating Remarks on Molecular Genetics and Genomics as Well as Drug Development. Molecular Genetics and Genomics, 295, 261-274. https://doi.org/10.1007/s00438-019-01634-z
[336]	Zhou, G.P. and Chou, K.C. (2020) Two Latest Hot Researches in Medicinal Chemistry. Current Topics in Medicinal Chemistry, 20, 1-2. https://doi.org/10.2174/156802662004200304124625
[337]	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
[338]	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, 19, 1-12. https://doi.org/10.1109/TCBB.2020.2968441
[339]	Chou, K.C. (2020) Using Similarity Software to Evaluate Scientific Paper Quality Is a Big Mistake. Natural Science, 12, 42-58. https://doi.org/10.4236/ns.2020.123008