Use Chou’s 5-Steps Rule to Predict Remote Homology Proteins by Merging Grey Incidence Analysis and Domain Similarity Analysis — Oak Academic Publishing
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Use Chou’s 5-Steps Rule to Predict Remote Homology Proteins by Merging Grey Incidence Analysis and Domain Similarity Analysis
Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen, China
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Gordon Life Science Institute, Boston, MA 02478, USA
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Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen, China
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Gordon Life Science Institute, Boston, MA 02478, USA
1 Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen, China
2 Gordon Life Science Institute, Boston, MA 02478, USA
3 Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen, China
4 Gordon Life Science Institute, Boston, MA 02478, USA
Detecting remote hom ology proteins is a challenging problem for both basic research and drug development. Although there are a couple of methods to deal with this problem, the benchmark datasets based on which the existing methods were trained and tested contain many high homologous samples as reflected by the fact that the cutoff threshold was set at 95%. In this study, we reconstructed the benchmark dataset by setting the threshold at 40%, meaning none of the proteins included in the benchmark dataset has more than 40% pairwise sequence identity with any other in the same subset. Using the new benchmark dataset, we proposed a new predictor called “dRHP-GreyFun” based on the grey modeling and functional domain approach. Rigorous cross-validations have indicated that the new predictor is superior to its counterparts in both enhancing success rates and reducing computational cost. The predictor can be downloaded from https://github.com/jcilwz/dRHP-GreyFun .
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Chou, K.C. and Cai, Y.D. (2003) Prediction and Classification of Protein Subcellular Location: Sequence-Order Effect and Pseudo Amino Acid Composition. Journal of Cellular Biochemistry, 90, 1250-1260. https://doi.org/10.1002/jcb.10719
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
Chou, K.C. (2005) Using Amphiphilic Pseudo Amino Acid Composition to Predict Enzyme Subfamily Classes. Bioinformatics, 21, 10-19. https://doi.org/10.1093/bioinformatics/bth466
Kabir, M. and Hayat, M. (2016) iRSpot-GAEnsC: Identifying Recombination Spots via Ensemble Classifier and Extending the Concept of Chou’s PseAAC to Formulate DNA Samples. Molecular Genetics and Genomics, 291, 285-296. https://doi.org/10.1007/s00438-015-1108-5
Meher, P.K., Sahu, T.K., Saini, V. and Rao, A.R. (2017) Predicting Antimicrobial Peptides with Improved Accuracy by Incorporating the Compositional, Physico-Chemical and Structural Features into Chou’s General PseAAC. Scientific Reports, 7, Article ID: 42362. https://doi.org/10.1038/srep42362
Ju, Z. and He, J.J. (2017) Prediction of Lysine Propionylation Sites Using Biased SVM and Incorporating Four Different Sequence Features into Chou’s PseAAC. Journal of Molecular Graphics and Modelling, 76, 356-363. https://doi.org/10.1016/j.jmgm.2017.07.022
Yu, B., Li, S., Qiu, W.Y., Chen, C., Chen, R.X., Wang, L., Wang, M.H. and Zhang, Y. (2017) Accurate Prediction of Subcellular Location of Apoptosis Proteins Combining Chou’s PseAAC and PsePSSM Based on Wavelet Denoising. Oncotarget, 8, 107640-107665. https://doi.org/10.18632/oncotarget.22585
Chou, 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/10.2174/1568026617666170414145508
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
Du, P., Wang, X., Xu, C. and Gao, Y. (2012) PseAAC-Builder: A Cross-Platform Stand-Alone Program for Generating Various Special Chou’s Pseudo AMINO Acid Compositions. Analytical Biochemistry, 425, 117-119. https://doi.org/10.1016/j.ab.2012.03.015
Cao, D.S., Xu, Q.S. and Liang, Y.Z. (2013) Propy: A Tool to Generate Various Modes of Chou’s PseAAC. Bioinformatics, 29, 960-962. https://doi.org/10.1093/bioinformatics/btt072
Du, P., Gu, S. and Jiao, Y. (2014) PseAAC-General: Fast Building Various Modes of General Form of Chou’s Pseudo Amino Acid Composition for Large-Scale Protein Datasets. International Journal of Molecular Sciences, 15, 3495-3506. https://doi.org/10.3390/ijms15033495
Chou, K.C. (2009) Pseudo Amino Acid Composition and Its Applications in Bioinformatics, Proteomics and System Biology. Current Proteomics, 6, 262-274. https://doi.org/10.2174/157016409789973707
Chen, W., Lin, H. and Chou, K.C. (2015) Pseudo Nucleotide Composition or PseKNC: An Effective Formulation for Analyzing Genomic Sequences. Molecular BioSystems, 11, 2620-2634. https://doi.org/10.1039/C5MB00155B
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
Liu, B., Liu, F., Wang, X., Chen, J., Fang, L. and Chou, K.C. (2015) Pse-in-One: A Web Server for Generating Various Modes of Pseudo Components of DNA, RNA, and Protein Sequences. Nucleic Acids Research, 43, W65-W71. https://doi.org/10.1093/nar/gkv458
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
Lin, W.Z., Xiao, X. and Chou, K.C. (2009) GPCR-GIA: A Web-Server for Identifying G-Protein Coupled Receptors and Their Families with Grey Incidence Analysis. Protein Engineering, Design and Selection (PEDS), 22, 699-705. https://doi.org/10.1093/protein/gzp057
Chou, K.C., Liu, W., Maggiora, G.M. and Zhang, C.T. (1998) Prediction and Classification of Domain Structural Classes. Proteins: Structure, Function and Genetics, 31, 97-103. https://doi.org/10.1002/(SICI)1097-0134(19980401)31:1 3.0.CO;2-E
Chou, K.C. and Maggiora, G.M. (1998) Domain Structural Class Prediction. Protein Engineering, 11, 523-538. https://doi.org/10.1093/protein/11.7.523
Chou, K.C. and Cai, Y.D. (2002) Using Functional Domain Composition and Support Vector Machines for Prediction of Protein Subcellular Location. The Journal of Biological Chemistry, 277, 45765-45769. https://doi.org/10.1074/jbc.M204161200
Chou, K.C. and Cai, Y.D. (2004) Predicting Protein Structural Class by Functional Domain Composition. Biochemical and Biophysical Research Communications (BBRC), 321, 1007-1009. https://doi.org/10.1016/j.bbrc.2004.07.059
Chou, K.C. and Cai, Y.D. (2004) Predicting Subcellular Localization of Proteins by Hybridizing Functional Domain Composition and Pseudo Amino Acid Composition. Journal of Cellular Biochemistry, 91, 1197-1203. https://doi.org/10.1002/jcb.10790
Cai, Y.D. and Chou, K.C. (2005) Using Functional Domain Composition to Predict Enzyme Family Classes. Journal of Proteome Research, 4, 109-111. https://doi.org/10.1021/pr049835p
Finn, R.D., Clements, J. and Eddy, S.R. (2011) HMMER Web Server: Interactive Sequence Similarity Searching. Nucleic Acids Research, 39, W29-W37. https://doi.org/10.1093/nar/gkr367
Finn, R.D., Clements, J., Arndt, W., Miller, B.L., Wheeler, T.J., Schreiber, F., Bateman, A. and Eddy, S.R. (2015) HMMER Web Server: 2015 Update. Nucleic Acids Research, 43, W30-W38. https://doi.org/10.1093/nar/gkv397
Potter, S.C., Luciani, A., Eddy, S.R., Park, Y., Lopez, R. and Finn, R.D. (2018) HMMER Web Server: 2018 Update. Nucleic Acids Research, 46, W200-W204. https://doi.org/10.1093/nar/gky448
Liu, S.F., Fang, Z.G. and Lin, Y. (2006) A New Definition for the Degree of Grey Incidence. Scientific Inquiry, 7, 111-124.
Chou, K.C. and Zhang, C.T. (1995) Review: Prediction of Protein Structural Classes. Critical Reviews in Biochemistry and Molecular Biology, 30, 275-349. https://doi.org/10.3109/10409239509083488
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
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
Mohabatkar, H. (2010) Prediction of Cyclin Proteins Using Chou’s Pseudo Amino Acid Composition. Protein & Peptide Letters, 17, 1207-1214. https://doi.org/10.2174/092986610792231564
Sahu, S.S. and Panda, G. (2010) A Novel Feature Representation Method Based on Chou’s Pseudo Amino Acid Composition for Protein Structural Class Prediction. Computational Biology and Chemistry, 34, 320-327. https://doi.org/10.1016/j.compbiolchem.2010.09.002
Zia-ur-Rehman and Khan, A. (2012) Identifying GPCRs and Their Types with Chou’s Pseudo Amino Acid Composition: An Approach from Multi-Scale Energy Representation and Position Specific Scoring Matrix. Protein & Peptide Letters, 19, 890-903. https://doi.org/10.2174/092986612801619589
Fan, G.L. and Li, Q.Z. (2013) Discriminating Bioluminescent Proteins by Incorporating Average Chemical Shift and Evolutionary Information into the General form of Chou’s Pseudo Amino Acid Composition. Journal of Theoretical Biology, 334, 45-51. https://doi.org/10.1016/j.jtbi.2013.06.003
Chou, 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.2009.12011
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
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. https://doi.org/10.1016/j.gene.2017.07.036
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
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
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
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, 331-349. https://doi.org/10.4236/ns.2017.99032
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
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
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. https://doi.org/10.1093/bioinformatics/btx387
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
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
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
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
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
Chou, K.C., Cheng, X. and Xiao, X. (2019) pLoc_bal-mEuk: Predict Subcellular Localization of Eukaryotic Proteins by General PseAAC and Quasi-Balancing Training Dataset. Medicinal Chemistry, 15, 472-485. https://doi.org/10.2174/1573406415666181218102517
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
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
Chou, K.C., Cheng, X. and Xiao, X. (2019) pLoc_bal-mHum: Predict Subcellular Localization of Human Proteins by PseAAC and Quasi-Balancing Training Dataset. Genomics, 111, 1274-1282. https://doi.org/10.1016/j.ygeno.2018.08.007
Xiao, X., Cheng, X., Chen, G., Mao, Q. and Chou, K.C. (2019) pLoc_bal-mGpos: Predict Subcellular Localization of Gram-Positive Bacterial Proteins by Quasi-Balancing Training Dataset and PseAAC. Genomics, 111, 886-892. https://doi.org/10.1016/j.ygeno.2018.05.017
Cheng, X., Lin, W.Z., Xiao, X. and Chou, K.C. (2019) pLoc_bal-mAnimal: Predict Subcellular Localization of Animal Proteins by Balancing Training Dataset and PseAAC. Bioinformatics, 35, 398-406. https://doi.org/10.1093/bioinformatics/bty628
Chou, K.C., Jiang, S.P., Liu, W.M. and Fee, C.H. (1979) Graph Theory of Enzyme Kinetics: 1. Steady-State Reaction System. Scientia Sinica, 22, 341-358.
Chou, K.C. and Forsen, S. (1980) Graphical Rules for Enzyme-Catalyzed Rate Laws. Biochemical Journal, 187, 829-835. https://doi.org/10.1042/bj1870829
Chou, K.C., Forsen, S. and Zhou, G.Q. (1980) Three Schematic Rules for Deriving Apparent Rate Constants. Chemica Scripta, 16, 109-113.
Chou, K.C., Carter, R.E. and Forsen, S. (1981) A New Graphical Method for Deriving Rate Equations for Complicated Mechanisms. Chemica Scripta, 18, 82-86.
Chou, K.C. and Forsen, S. (1981) Graphical Rules of Steady-State Reaction Systems. Canadian Journal of Chemistry, 59, 737-755. https://doi.org/10.1139/v81-107
Zhou, G.P. and Deng, M.H. (1984) An Extension of Chou’s Graphic Rules for Deriving Enzyme Kinetic Equations to Systems Involving Parallel Reaction Pathways. Biochemical Journal, 222, 169-176. https://doi.org/10.1042/bj2220169
Chou, K.C. (1989) Graphic Rules in Steady and Non-Steady Enzyme Kinetics. The Journal of Biological Chemistry, 264, 12074-12079.
Althaus, I.W., Chou, J.J., Gonzales, A.J., Diebel, M.R., Chou, K.C., Kezdy, F.J., Romero, D.L., Aristoff, P.A., Tarpley, W.G. and Reusser, F. (1993) Steady-State Kinetic Studies with the Non-Nucleoside HIV-1 Reverse Transcriptase Inhibitor U-87201E. The Journal of Biological Chemistry, 268, 6119-6124.
Chou, K.C. (1990) Review: Applications of Graph Theory to Enzyme Kinetics and Protein Folding Kinetics. Steady and Non-Steady State Systems. Biophysical Chemistry, 35, 1-24. https://doi.org/10.1016/0301-4622(90)80056-D
Althaus, I.W., Gonzales, A.J., Chou, J.J., Diebel, M.R., Chou, K.C., Kezdy, F.J., Romero, D.L., Aristoff, P.A., Tarpley, W.G. and Reusser, F. (1993) The Quinoline U-78036 Is a Potent Inhibitor of HIV-1 Reverse Transcriptase. The Journal of Biological Chemistry, 268, 14875-14880.
Chou, K.C. (2010) Graphic Rule for Drug Metabolism Systems. Current Drug Metabolism, 11, 369-378. https://doi.org/10.2174/138920010791514261
Zhou, G.P. (2011) The Disposition of the LZCC Protein Residues in Wenxiang Diagram Provides New Insights into the Protein-Protein Interaction Mechanism. Journal of Theoretical Biology, 284, 142-148. https://doi.org/10.1016/j.jtbi.2011.06.006
Althaus, I.W., Chou, J.J., Gonzales, A.J., Diebel, M.R., Chou, K.C., Kezdy, F.J., Romero, D.L., Aristoff, P.A., Tarpley, W.G. and Reusser, F. (1993) Kinetic Studies with the Nonnucleoside HIV-1 Reverse Transcriptase Inhibitor U-88204E. Biochemistry, 32, 6548-6554. https://doi.org/10.1021/bi00077a008
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
Chou, K.C. and Forsen, S. (1980) Diffusion-Controlled Effects in Reversible Enzymatic Fast Reaction System: Critical Spherical Shell and Proximity Rate Constants. Biophysical Chemistry, 12, 255-263. https://doi.org/10.1016/0301-4622(80)80002-0
Chou, K.C., Li, T.T. and Forsen, S. (1980) The Critical Spherical Shell in Enzymatic Fast Reaction Systems. Biophysical Chemistry, 12, 265-269. https://doi.org/10.1016/0301-4622(80)80003-2
Shen, H.B., Song, J.N. and Chou, K.C. (2009) Prediction of Protein Folding Rates from Primary Sequence by Fusing Multiple Sequential Features. Journal of Biomedical Science and Engineering, 2, 136-143. https://doi.org/10.4236/jbise.2009.23024
Chou, K.C., Chen, N.Y. and Forsen, S. (1981) The Biological Functions of Low-Frequency Phonons: 2. Cooperative Effects. Chemica Scripta, 18, 126-132.
Chou, K.C. (1988) Review: Low-Frequency Collective Motion in Biomacromolecules and Its Biological Functions. Biophysical Chemistry, 30, 3-48. https://doi.org/10.1016/0301-4622(88)85002-6
Chou, K.C. (2019) The Cradle of Gordon Life Science Institute and Its Development and Driving Force. Int J Biol Genetics, 1, 1-28.
Chou, K.C. (2019) Showcase to Illustrate How the Web-Server iDNA6mA-PseKNC Is Working. Journal of Pathology Research Reviews & Reports, 1, 1-15.
Chou, K.C. (2019) The pLoc_bal-mPlant Is a Powerful Artificial Intelligence Tool for Predicting the Subcellular Localization of Plant Proteins Purely Based on Their Sequence Information. International Journal of Nutrition Sciences, 4, 1-4.
Chou, K.C. (2019) Showcase to Illustrate How the Web-Server iNitro-Tyr Is Working. Glo J of Com Sci and Infor Tec., 2, 1-16.
Chou, K.C. (2019) Gordon Life Science Institute: Its Philosophy, Achievements, and Perspective. Annals of Cancer Therapy and Pharmacology, 2, 1-26. https://onomyscience.com/onomy/cancer_archive_volume2_issue2.html
Chou, K.C. (2019) The pLoc_bal-mAnimal Is a Powerful Artificial Intelligence Tool for Predicting the Subcellular Localization of Animal Proteins Based on Their Sequence Information Alone. Scientific Journal of Biometrics & Biostatistics, 2, 1-13.
Chou, K.C. (2020) Showcase to Illustrate How the Webserver pLoc_bal-mEuk Is Working. Biomedical Journal of Scientific & Technical Research. https://doi.org/10.18483/ijSci.2247
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. ijSci, 9, 27-34. https://doi.org/10.18483/ijSci.2248
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.
Chou, K.-C. (2020) Showcase to Illustrate How the Web-Server iKcr-PseEns Is Working. Journal of Medical Care Research and Review, 3, 331-347. https://doi.org/10.18483/ijSci.2247
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.
Chou, K.C. (2019) How the Artificial Intelligence Tool iSNO-PseAAC Is Working in Predicting the Cysteine s-Nitrosylation Sites in Proteins. Journal of Stem Cell Research and Medicine, 4, 1-9.
Chou, K.C. (2020) Showcase to Illustrate How the Web-Server iRNA-Methyl Is Working. Journal of Molecular Genetics, 3, 1-7.
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, 24.
Chou, K.C. (2020) Showcase to Illustrate How the Web-Server iSNO-AAPair Is Working. Journal of Genetics and Genomics, 4. https://doi.org/10.18483/ijSci.2247
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.
Chou, K.C. (2020) Showcase to Illustrate How the Web-Server iPTM-mLys Is Working. Infotext Journal of Infectious Diseases and Therapy, 1, 1-16.
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. Glo J of Com Sci and Infor Tec, 2, 1-13.
Chou, K.C. (2020) Showcase to Illustrate How the Web-Server iPreny-PseAAC Is Working. Glo J of Com Sci and Infor Tec., 2, 1-15.
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
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
Chou, K.C. (2020) The Most Important Ethical Concerns in Science. Natural Science, 12, 35-36. https://doi.org/10.4236/ns.2020.122005
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
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
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