In this short review paper, the significant and profound impacts of the Gordon Life Science Institute have been briefly presented with crystal clear convincingness.
Internet Institute Gordon Life Science Institute COVID-19 Significant Impacts Profound ImpactConflicts of Interest
The author declares no conflicts of interest regarding the publication of this paper.
Cite this paper
Chou, K.-C. (2021). The Significant and Profound Impacts of Gordon Life Science Institute. Voice of the Publisher, 7, 6-31. https://doi.org/10.4236/vp.2021.71002
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https://doi.org/10.1002/jcc.20732Liu, D.Q., Liu, H., Shen, H.B., Yang, J. and Chou, K.C. (2007) Predicting Secretory Protein Signal Sequence Cleavage Sites by Fusing the Marks of Global Alignments. Amino Acids, 32, 493-496. https://doi.org/10.1007/s00726-006-0466-zDu, Q.S., Wei, Y.T., Pang, Z.W., Chou, K.C. and Huang, R.B. (2007) Predicting the Affinity of Epitope-Peptides with Class I MHC Molecule HLA-A*0201: An Application of Amino Acid-Based Peptide Prediction. Protein Engineering, Design & Selection, 20, 417-423. https://doi.org/10.1093/protein/gzm036Chen, J., Liu, H., Yang, J. and Chou, K.C. (2007) Prediction of Linear B-Cell Epitopes Using Amino Acid Pair Antigenicity Scale. Amino Acids, 33, 423-428.
https://doi.org/10.1007/s00726-006-0485-9Ding, Y.S., Zhang, T.L. and Chou, K.C. (2007) Prediction of Protein Structure Classes with Pseudo Amino Acid Composition and Fuzzy Support Vector Machine Network. Protein & Peptide Letters, 14, 811-815.
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https://doi.org/10.1016/j.ab.2007.07.006Sirois, S., Touaibia, M., Chou, K.C. and Roy, R. (2007) Review: 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/10.2174/092986707782793826Shen, H.B., Yang, J. and Chou, K.C. (2007) Review: Methodology Development for Predicting Subcellular Localization and Other Attributes of Proteins. Expert Review of Proteomics, 4, 453-463. https://doi.org/10.1586/14789450.4.4.453Chou, K.C. and Shen, H.B. (2007) Signal-CF: A Subsite-Coupled and Window-Fusing Approach for Predicting Signal Peptides. Biochemical and Biophysical Research Communications (BBRC), 357, 633-640. https://doi.org/10.1016/j.bbrc.2007.03.162Shen, 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/s00726-006-0439-2Wang, 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/s00726-006-0403-1Chou, 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.2007.494Guo, 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/s00726-007-0611-3Aguero-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/ci8001809Yang, 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/10.2174/1875036200802010090Zhang, 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.
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https://doi.org/10.2174/1875036200802010072Du, 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.20776Wang, 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/10.2174/092986608785849308Xiao, 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.2008.06.016Zhang, 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.2007.09.014Chou, 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.2008.08.125Shen, 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.2007.10.012Wang, 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/10.2174/156802608786786633Wang, 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/10.2174/156802608786786534Du, 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/10.2174/138920308784534005Cruz-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.2008.09.070Chou, 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.ch5Gong, 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/10.2174/157340609788185936Wang, 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/10.2174/157340609788185954Du, 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.2009.03.003Du, 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.21056Shen, 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/10.2174/092986609789839322Shen, 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.2008.10.020Wei, 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/10.2174/157340609788681430Wang, 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.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.2008.08.028Gu, 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/10.2174/157340609788185909Shen, 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.2008.10.007Xiao, 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/S0021889809002751Chou, K.C. (2009) Pseudo Amino Acid Composition and Its Applications in Bioinformatics, Proteomics and System Biology. Current Proteomics, 6, 262-274.
https://doi.org/10.2174/157016409789973707Chou, K.C. and Shen, H.B. (2009) Recent Advances in Developing Web-Servers for Predicting Protein Attributes. Natural Science, 1, 63-92.
https://doi.org/10.4236/ns.2009.12011Shen, 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.ch12Wang, J.F., Zhang, C.C., Chou, K.C. and Wei, D.Q. (2009) Review: Structure of Cytochrome P450s and Personalized Drug. Current Medicinal Chemistry, 16, 232-244.
https://doi.org/10.2174/092986709787002727Madkan, 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.2009.13020Chou, 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_1Shen, 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.2009.07.046Ding, Y.S., Zhang, T.L., Gu, Q., Zhao, P.Y. and Chou, K.C. (2009) Using Maximum Entropy Model to Predict Protein Secondary Structure with Single Sequence. Protein & Peptide Letters, 16, 552-560. https://doi.org/10.2174/092986609788167833Du, Q.S., Huang, R.B. and Chou, K.C. (2010) Advances in Visual Representation of Molecular Potentials. Expert Opinion on Drug Discovery, 5, 513-527.
https://doi.org/10.1517/17460441.2010.484837Chen, 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/molecules15118177Chou, 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.2010.210136Qi, 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.2010.22021Du, 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.2009.12.037Chou, 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.2019.23.003978Du, Q.S., Huang, R.B., Wang, S.Q. and Chou, K.C. (2010) Designing Inhibitors of M2 Proton Channel against H1N1 Swine Influenza Virus. PLoS ONE, 5, e9388.
https://doi.org/10.1371/journal.pone.0009388Shen, 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.2010.01.018Chou, K.C. (2010) Graphic Rule for Drug Metabolism Systems. Current Drug Metabolism, 11, 369-378. https://doi.org/10.2174/138920010791514261Ren, 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.395Xiao, 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/10.2174/138920311796957720Huang, T., Chen, L., Cai, Y.D. and Chou, K.C. (2011) Classification and Analysis of Regulatory Pathways Using Graph Property, Biochemical and Physicochemical Property, and Functional Property. PLoS ONE, 6, e25297.
https://doi.org/10.1371/journal.pone.0025297Xiao, 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/C0MB00170HWan, 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/10.2174/157489311795222374Cai, 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/10.2174/157340611794859343Lin, 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.0024756Wang, 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.0018414Hu, 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.0029491Hu, 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.0014556Hu, 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.2010.10.022Du, 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/10.1517/17460441.2011.571671Xiao, 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/s11030-010-9227-8Wen, 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/10.2174/092986611794578369Chou, 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.2010.12.024Xiao, 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/10.2174/1574893611106020251Chou, 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.2011.310111Ma, 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.0038546Chou, 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/C1MB05420AXiao, 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.0030869Wang, 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.0031048Chen, 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.0047843Xiao, 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/10.2174/157489312800604426Liu, 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/btt709Ding, 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/10.1155/2014/286419Liu, 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.0106691Xu, 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.0105018Fan, 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/ijms15034915Guo, 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/gku1019Qiu, 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/ijms15021746Chen, 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., 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., 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.2014.04.001Liu, 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/10.2174/1573406410666141224115516Xu, 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/10.1080/07391102.2014.968624Liu, B., Fang, L., Liu, F., Wang, X., Chen, J. and Chou, K.C. (2015) Identification of Real microRNA Precursors with a Pseudo Structure Status Composition Approach. PLoS ONE, 10, e0121501. https://doi.org/10.1371/journal.pone.0121501Liu, 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.2014.12.009Xiao, 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/10.1080/07391102.2014.998710Chou, K.C. (2015) Impacts of Bioinformatics to Medicinal Chemistry. Medicinal Chemistry, 11, 218-234. https://doi.org/10.2174/1573406411666141229162834Jia, 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.2015.04.011Liu, 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/btu820Chou, 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-b040Liu, 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.S42113Xiao, 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.2013.01.019Min, 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/10.1155/2013/701317Xiao, 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.0072234Feng, 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.2013.05.024Lin, 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/c3mb25466fChen, 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/gks1450Xu, 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.171Li, 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/bby077Li, 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/bty522Song, 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/btx670Song, 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.2018.01.023Cheng, 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/btx711Cheng, 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.2017.10.002Cheng, 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.2017.08.005Cheng, 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/10.2174/1381612824666181119145030Cheng, 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.2018.09.005Ehsan, 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-yChen, 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/bby089Yang, 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.24616Liu, 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/bty312Chen, 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. Molecular Therapy: Nucleic Acid, 11, 468-474.
https://doi.org/10.1016/j.omtn.2018.03.012Song, 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/bby028Liu, 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/btx579Khan, 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-zKhan, 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.2018.04.021Cai, 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.2018.05.026Su, 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/bty508Qiu, 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.2017.10.008Chen, 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/bty140Liu, 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/bty458Zhang, 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/bby079Wang, 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/bty155Wang, 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/bty914Chou, K.C. (2017) An Unprecedented Revolution in Medicinal Chemistry Driven by the Progress of Biological Science. Current Topics in Medicinal Chemistry, 17, 2337-2358. https://doi.org/10.2174/1568026617666170414145508Niu, 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.19169Liu, 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.2017.94007Liu, 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.14524Su, 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.17210Wang, 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/btx302Cheng, 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.2017.10.042Cheng, 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/C7MB00267JXiao, 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.2017.99032Cheng, 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/btx476Liu, 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/btw539Qiu, 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.17104Feng, 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.2017.03.006Chen, 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.13758Qiu, 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/10.2174/1573406413666170623082245Xu, 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/10.2174/1573406413666170419150052Qiu, 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.201600010Liu, 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/10.2174/1573406413666170515120507Cheng, 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/btx387Chen, 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.17028Zhang, 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/10.2174/1573406413666170510102024Liu, 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.2017.04.008Du, 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.19757Chen, 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.2015.12.005Liu, 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/s00438-015-1078-7Xu, 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/10.2174/1568026615666150819110421Jia, 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/btw387Jia, 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.2016.01.020Liu, 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.2015.12.017Vaseghi, 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.ch14Cai, 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/10.2174/1568026615666150819105033Jia, 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.2015.12.009Xiao, 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.9057Chen, W., Tang, H., Ye, J., Lin, H. and Chou, K.C. (2016) iRNA-PseU: Identifying RNA Pseudouridine Sites. Molecular Therapy—Nucleic Acids, 5, e332.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/btw380Jia, 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/molecules21010095Qiu, 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.9987Zhang, 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.11975Cai, 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/srep36540Qiu, 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.10027Liu, 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/btv604Liu, 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/btw186Jia, 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/10.1080/07391102.2015.1095116Jia, 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.9148Chen, 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, 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/srep32333Zhu, 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/10.2174/1568026616666150923152745Chou, K.C. (2013) Some Remarks on Predicting Multi-Label Attributes in Molecular Biosystems. Molecular Biosystems, 9, 1092-1100.
https://doi.org/10.1039/c3mb25555gXiao, 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/10.2174/15680266113139990006Xiao, 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/10.2174/15680266113139990113Xiao, 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/10.2174/15680266113139990121Wang, 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/10.2174/15680266113139990011Ji, 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/10.2174/1574893611308040013Xu, 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.0055844Chou, 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.2020.123008Akmal, 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.2020.2968441Lin, 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.2020.123016Zhou, 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/10.2174/156802662004200304124625Chou, 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-zChou, 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.2020.24.004033Chou, 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.2020.24.004033Chou, 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.1000310Chou K.C. ,et al. (2020)Showcase to Illustrate How the Web-Server pLoc_Deep-mPlant Is Working Integrative Molecular Biology and Biotechnology 1, 1-2.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.2020.117020Lu, 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.2020.135005Chou, 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)105Chou, 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.2020.117019Chou K.C. ,et al. (2020)Showcase to Illustrate How the Web-Server pLoc_Deep-mAnimal Is Working American Journal of Virology & Disease 2, 1-2.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.2020.128047Chou, 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.1000310Chou, 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.1000310Chou K.C. ,et al. (2020)Showcase to Illustrate How the Web-Server iPTM-mLys Is Working Infotext Journal of Infectious Diseases and Therapy [IJID] 1, 1-16.Chou K.C. ,et al. (2020)Showcase to Illustrate How the Web-Server iPreny-PseAAC Is Working Global Journal of Computer Science and Information Technology 2, 1-15.Chou K.C. ,et al. (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.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.2020.123011Chou, 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/s10989-019-09893-5Chou, 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.2020.122006Khan, 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.2020.104056Shao, 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.2020.126033Shao, 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.2020.125021Shao, 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.2020.127042Liu, 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.2020.115011Shao, 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.2020.126034Shao, 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.2020.125024Chou, 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.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.03.01.06Chou K.C. ,et al. (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.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.2248Chou, K.C. (2020) The Pandemic Pestilences and Internet Institutes. Natural Science, 12, 495-515. https://doi.org/10.4236/ns.2020.127039Chou, K.C. (2020) Other Mountain Stones Can Attack Jade: The 5-Steps Rule. Natural Science, 12, 59-64. https://doi.org/10.4236/ns.2020.123011Chou, K.C. (2020) Noah’s Ark and Internet Institutes: When and Why? Natural Science, 12, 470-481. https://doi.org/10.4236/ns.2020.127037Pugalenthi, 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/10.2174/0929866526666191002111404Chou, K.C. (2020) The Most Important Ethical Concerns in Science. Natural Science, 12, 35-36. https://doi.org/10.4236/ns.2020.122005Khan, 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.2019.113477Chou, 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.2020.127036Chou, 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/s00726-020-02828-1Chou, 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.2020.127038Lu, 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.2020.115012Chou, 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)102Chou, 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)102Chou, 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.2020.24.004016Chou K.C. ,et al. (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.Chou K.C. ,et al. (2020)How the Artificial Intelligence Tool iPGK-PseAAC Is Working in Predicting Lysine Phosphoglycerylation Sites in Proteins ES Journal of Microbiology 1, 1003-1004.Chou K.C. ,et al. (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.Chou K.C. ,et al. (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.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.2020.123013Niu, 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.2019.05.024Chou, 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.2020.128043Chou, K.C. (2020) Distorted Key Theory and Its Implication for Drug Development. Current Genomics. http://www.eurekaselect.com/175823/articleChou, 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.2020.124018Chou, K.C. (2020) Coronavirus and Gordon Life Science Institute. Natural Science, 12, 429-440. https://doi.org/10.4236/ns.2020.127035Khan, 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/s10989-019-09887-3Chou, K.C. (2019) Two Kinds of Metrics for Computational Biology. Genomics.
https://doi.org/10.1016/j.ygeno.2019.08.008Hussain, 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.2019.02.007Hussain, 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.2018.12.019Chou, 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.1000310Chou K.C. ,et al. (2019)Showcase to Illustrate How the Web-Server iNitro-Tyr Is Working Global Journal of Computer Science and Information Technology 2, 1-16.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)105Chou, K.C. (2019) Recent Progresses in Predicting Protein Subcellular Localization with Artificial Intelligence Tools Developed via the 5-Steps Rule. Medicinal Chemistry.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.phpKhan, 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