Advances in Predicting Subcellular Localization of Multi-label Proteins and its Implication for Developing Multi-target Drugs

Kuo-Chen        Chou
Abstract

The smallest unit of life is a cell, which contains numerous protein molecules. Most of the functions critical to the cell’s survival are performed by these proteins located in its different organelles, usually called ‘‘subcellular locations”. Information of subcellular localization for a protein can provide useful clues about its function. To reveal the intricate pathways at the cellular level, knowledge of the subcellular localization of proteins in a cell is prerequisite. Therefore, one of the fundamental goals in molecular cell biology and proteomics is to determine the subcellular locations of proteins in an entire cell. It is also indispensable for prioritizing and selecting the right targets for drug development. Unfortunately, it is both timeconsuming and costly to determine the subcellular locations of proteins purely based on experiments. With the avalanche of protein sequences generated in the post-genomic age, it is highly desired to develop computational methods for rapidly and effectively identifying the subcellular locations of uncharacterized proteins based on their sequences information alone. Actually, considerable progresses have been achieved in this regard. This review is focused on those methods, which have the capacity to deal with multi-label proteins that may simultaneously exist in two or more subcellular location sites. Protein molecules with this kind of characteristic are vitally important for finding multi-target drugs, a current hot trend in drug development. Focused in this review are also those methods that have use-friendly web-servers established so that the majority of experimental scientists can use them to get the desired results without the need to go through the detailed mathematics involved.
Keywords: 5-step rules, multi-label proteins, multi-target drugs, global accuracy and metrics, local accuracy and metrics, absolute true rate, web-server.
« Previous Next »
[1] 
Chou, K.C.; Shen, H.B. Recent progress in protein subcellular location prediction. Anal. Biochem.,  2007, 370(1), 1-16.
[http://dx.doi.org/10.1016/j.ab.2007.07.006] [PMID:  17698024] 
[2] 
Chou, K.C. Impacts of bioinformatics to medicinal chemistry. Med. Chem.,  2015, 11(3), 218-234.
[http://dx.doi.org/10.2174/1573406411666141229162834] [PMID:  25548930] 
[3] 
Andrade, M.A.; O’Donoghue, S.I.; Rost, B. Adaptation of protein surfaces to subcellular location. J. Mol. Biol.,  1998, 276(2), 517-525.
[http://dx.doi.org/10.1006/jmbi.1997.1498] [PMID:  9512720] 
[4] 
Chou, K.C.; Elrod, D.W. Using discriminant function for prediction of subcellular location of prokaryotic proteins. Biochem. Biophys. Res. Commun.,  1998, 252(1), 63-68. [BBRC]
[http://dx.doi.org/10.1006/bbrc.1998.9498] [PMID: 9813147] 
[5] 
Reinhardt, A.; Hubbard, T. Using neural networks for prediction of the subcellular location of proteins. Nucleic Acids Res.,  1998, 26(9), 2230-2236.
[http://dx.doi.org/10.1093/nar/26.9.2230] [PMID:  9547285] 
[6] 
Chou, K.C.; Elrod, D.W. Protein subcellular location prediction. Protein Eng.,  1999, 12(2), 107-118.
[http://dx.doi.org/10.1093/protein/12.2.107] [PMID:  10195282] 
[7] 
Chou, K.C.; Elrod, D.W. Prediction of membrane protein types and subcellular locations. Proteins,  1999, 34(1), 137-153.
[http://dx.doi.org/10.1002/(SICI)1097-0134(19990101)34:1<137:AID-PROT11>3.0.CO;2-O] [PMID:  10336379] 
[8] 
Yuan, Z. Prediction of protein subcellular locations using Markov chain models. FEBS Lett.,  1999, 451(1), 23-26.
[http://dx.doi.org/10.1016/S0014-5793(99)00506-2] [PMID:  10356977] 
[9] 
Cai, Y.D.; Chou, K.C. Using neural networks for prediction of subcellular location of prokaryotic and eukaryotic proteins. Mol. Cell Biol. Res. Commun.,  2000, 4(3), 172-173.
[http://dx.doi.org/10.1006/mcbr.2001.0269] [PMID:  11281732] 
[10] 
Emanuelsson, O.; Nielsen, H.; Brunak, S.; von Heijne, G. Predicting subcellular localization of proteins based on their N-terminal amino acid sequence. J. Mol. Biol.,  2000, 300(4), 1005-1016.
[http://dx.doi.org/10.1006/jmbi.2000.3903] [PMID:  10891285] 
[11] 
Cai, Y.D.; Liu, X.J.; Xu, X.B.; Chou, K.C. Support vector machines for prediction of protein subcellular location. Mol. Cell Biol. Res. Commun.,  2000, 4(4), 230-233.
[http://dx.doi.org/10.1006/mcbr.2001.0285] [PMID:  11409917] 
[12] 
Chou, K.C. Prediction of protein structural classes and subcellular locations. Curr. Protein Pept. Sci.,  2000, 1(2), 171-208.
[http://dx.doi.org/10.2174/1389203003381379] [PMID:  12369916] 
[13] 
Chou, K.C. Prediction of protein subcellular locations by incorporating quasi-sequence-order effect. Biochem. Biophys. Res. Commun.,  2000, 278(2), 477-483. [BBRC]
[http://dx.doi.org/10.1006/bbrc.2000.3815] [PMID:  11097861] 
[14] 
Murphy, R.F.; Boland, M.V.; Velliste, M. Towards a systematics for protein subcelluar location: quantitative description of protein localization patterns and automated analysis of fluorescence microscope images. Proc. Int. Conf. Intell. Syst. Mol. Biol.,  2000, 8, 251-259.
[PMID:  10977086] 
[15] 
Feng, Z.P. Prediction of the subcellular location of prokaryotic proteins based on a new representation of the amino acid composition. Biopolymers,  2001, 58(5), 491-499.
[http://dx.doi.org/10.1002/1097-0282(20010415)58:5<491:AID-BIP1024>3.0.CO;2-I] [PMID:  11241220] 
[16] 
Feng, Z.P.; Zhang, C.T. Prediction of the subcellular location of prokaryotic proteins based on the hydrophobicity index of amino acids. Int. J. Biol. Macromol.,  2001, 28(3), 255-261.
[http://dx.doi.org/10.1016/S0141-8130(01)00121-0] [PMID:  11251233] 
[17] 
Nair, R.; Rost, B. Sequence conserved for subcellular localization. Protein Sci.,  2002, 11(12), 2836-2847.
[http://dx.doi.org/10.1110/ps.0207402] [PMID:  12441382] 
[18] 
Cai, Y.D.; Liu, X.J.; Xu, X.B.; Chou, K.C. Support vector machines for prediction of protein subcellular location by incorporating quasi-sequence-order effect. J. Cell. Biochem.,  2002, 84(2), 343-348.
[http://dx.doi.org/10.1002/jcb.10030] [PMID:  11787063] 
[19] 
Chou, K.C.; Cai, Y.D. Using functional domain composition and support vector machines for prediction of protein subcellular location. J. Biol. Chem.,  2002, 277(48), 45765-45769.
[http://dx.doi.org/10.1074/jbc.M204161200] [PMID:  12186861] 
[20] 
Feng, Z.P. An overview on predicting the subcellular location of a protein. In: Silico Biol. (Gedrukt); , 2002.  2(3), 291- 303.
[PMID: 12542414] 
[21] 
Cai, Y.D.; Chou, K.C. Nearest neighbour algorithm for predicting protein subcellular location by combining functional domain composition and pseudo-amino acid composition. Biochem. Biophys. Res. Commun.,  2003, 305(2), 407-411. [BBRC]
[http://dx.doi.org/10.1016/S0006-291X(03)00775-7] [PMID:  12745090] 
[22] 
Chou, K.C.; Cai, Y.D. A new hybrid approach to predict subcellular localization of proteins by incorporating gene ontology. Biochem. Biophys. Res. Commun.,  2003, 311(3), 743-747. [BBRC]
[http://dx.doi.org/10.1016/j.bbrc.2003.10.062] [PMID:  14623335] 
[23] 
Gardy, J.L.; Spencer, C.; Wang, K.; Ester, M.; Tusnády, G.E.; Simon, I.; Hua, S.; deFays, K.; Lambert, C.; Nakai, K.; Brinkman, F.S. PSORT-B: Improving protein subcellular localization prediction for Gram-negative bacteria. Nucleic Acids Res.,  2003, 31(13), 3613-3617.
[http://dx.doi.org/10.1093/nar/gkg602] [PMID:  12824378] 
[24] 
Chou, K.C.; Cai, Y.D. Prediction and classification of protein
subcellular location: sequence-order effect and pseudo
amino acid composition. J. Cell. Biochem. (Addendum, ibid.
2004, 91, 1085),  2003, 90,, 1250-1260.
[25] 
Pan, Y.X.; Zhang, Z.Z.; Guo, Z.M.; Feng, G.Y.; Huang, Z.D.; He, L. Application of pseudo amino acid composition for predicting protein subcellular location: stochastic signal processing approach. J. Protein Chem.,  2003, 22(4), 395-402.
[http://dx.doi.org/10.1023/A:1025350409648] [PMID:  13678304] 
[26] 
Park, K.J.; Kanehisa, M. Prediction of protein subcellular locations by support vector machines using compositions of amino acids and amino acid pairs. Bioinformatics,  2003, 19(13), 1656-1663.
[http://dx.doi.org/10.1093/bioinformatics/btg222] [PMID:  12967962] 
[27] 
Zhou, G.P.; Doctor, K. Subcellular location prediction of apoptosis proteins. Proteins,  2003, 50(1), 44-48.
[http://dx.doi.org/10.1002/prot.10251] [PMID:  12471598] 
[28] 
Cai, Y.D.; Chou, K.C. Predicting subcellular localization of proteins in a hybridization space. Bioinformatics,  2004, 20(7), 1151-1156.
[http://dx.doi.org/10.1093/bioinformatics/bth054] [PMID:  14764553] 
[29] 
Chou, K.C.; Cai, Y.D. Prediction of protein subcellular locations by GO-FunD-PseAA predictor. Biochem. Biophys. Res. Commun.,  2004, 320(4), 1236-1239. [BBRC]
[http://dx.doi.org/10.1016/j.bbrc.2004.06.073] [PMID:  15249222] 
[30] 
Chou, K.C.; Cai, Y.D. Predicting subcellular localization of proteins by hybridizing functional domain composition and pseudo-amino acid composition. J. Cell. Biochem.,  2004, 91(6), 1197-1203.
[http://dx.doi.org/10.1002/jcb.10790] [PMID:  15048874] 
[31] 
Dönnes, P.; Höglund, A. Predicting protein subcellular localization: past, present, and future. Genomics Proteomics Bioinformatics,  2004, 2(4), 209-215.
[http://dx.doi.org/10.1016/S1672-0229(04)02027-3] [PMID:  15901249] 
[32] 
Bhasin, M.; Raghava, G.P. ESLpred: SVM-based method for subcellular localization of eukaryotic proteins using dipeptide composition and PSI-BLAST. Nucleic Acids Res, 2004. 32(Web Server issue)W414-9
[http://dx.doi.org/10.1093/nar/gkh350] [PMID: 15215421] 
[33] 
Huang, Y.; Li, Y. Prediction of protein subcellular locations using fuzzy k-NN method. Bioinformatics,  2004, 20(1), 21-28.
[http://dx.doi.org/10.1093/bioinformatics/btg366] [PMID:  14693804] 
[34] 
Gao, Q.B.; Wang, Z.Z.; Yan, C.; Du, Y.H. Prediction of protein subcellular location using a combined feature of sequence. FEBS Lett.,  2005, 579(16), 3444-3448.
[http://dx.doi.org/10.1016/j.febslet.2005.05.021] [PMID:  15949806] 
[35] 
Shen, H.B.; Chou, K.C. Predicting protein subnuclear location with optimized evidence-theoretic K-nearest classifier and pseudo amino acid composition. Biochem. Biophys. Res. Commun.,  2005, 337(3), 752-756. [BBRC]
[http://dx.doi.org/10.1016/j.bbrc.2005.09.117] [PMID:  16213466] 
[36] 
Chou, K.C.; Cai, Y.D. Predicting protein localization in budding yeast. Bioinformatics,  2005, 21(7), 944-950.
[http://dx.doi.org/10.1093/bioinformatics/bti104] [PMID:  15513989] 
[37] 
Gao, Y.; Shao, S.; Xiao, X.; Ding, Y.; Huang, Y.; Huang, Z.; Chou, K.C. Using pseudo amino acid composition to predict protein subcellular location: Approached with Lyapunov index, Bessel function, and Chebyshev filter. Amino Acids,  2005, 28(4), 373-376.
[http://dx.doi.org/10.1007/s00726-005-0206-9] [PMID:  15889221] 
[38] 
Matsuda, S.; Vert, J.P.; Saigo, H.; Ueda, N.; Toh, H.; Akutsu, T. A novel representation of protein sequences for prediction of subcellular location using support vector machines. Protein Sci.,  2005, 14(11), 2804-2813.
[http://dx.doi.org/10.1110/ps.051597405] [PMID:  16251364] 
[39] 
Xiao, X.; Shao, S.; Ding, Y.; Huang, Z.; Huang, Y.; Chou, K.C. Using complexity measure factor to predict protein subcellular location. Amino Acids,  2005, 28(1), 57-61.
[http://dx.doi.org/10.1007/s00726-004-0148-7] [PMID:  15611847] 
[40] 
Gardy, J.L.; Laird, M.R.; Chen, F.; Rey, S.; Walsh, C.J.; Ester, M.; Brinkman, F.S. PSORTb v.2.0: expanded prediction of bacterial protein subcellular localization and insights gained from comparative proteome analysis. Bioinformatics,  2005, 21(5), 617-623.
[http://dx.doi.org/10.1093/bioinformatics/bti057] [PMID:  15501914] 
[41] 
Garg, A.; Bhasin, M.; Raghava, G.P. Support vector machine-based method for subcellular localization of human proteins using amino acid compositions, their order, and similarity search. J. Biol. Chem.,  2005, 280(15), 14427-14432.
[http://dx.doi.org/10.1074/jbc.M411789200] [PMID:  15647269] 
[42] 
Sarda, D.; Chua, G.H.; Li, K.B.; Krishnan, A. pSLIP: SVM based protein subcellular localization prediction using multiple physicochemical properties. BMC Bioinformatics,  2005, 6, 152.
[http://dx.doi.org/10.1186/1471-2105-6-152] [PMID:  15963230] 
[43] 
Chou, K.C.; Shen, H.B. Hum-PLoc: a novel ensemble classifier for predicting human protein subcellular localization. Biochem. Biophys. Res. Commun.,  2006, 347(1), 150-157. [BBRC[
[http://dx.doi.org/10.1016/j.bbrc.2006.06.059] [PMID:  16808903] 
[44] 
Chou, K.C.; Shen, H.B. Predicting protein subcellular location by fusing multiple classifiers. J. Cell. Biochem.,  2006, 99(2), 517-527.
[http://dx.doi.org/10.1002/jcb.20879] [PMID:  16639720] 
[45] 
Chou, K.C.; Shen, H.B. Predicting eukaryotic protein subcellular location by fusing optimized evidence-theoretic K-Nearest Neighbor classifiers. J. Proteome Res.,  2006, 5(8), 1888-1897.
[http://dx.doi.org/10.1021/pr060167c] [PMID:  16889410] 
[46] 
Pierleoni, A.; Martelli, P.L.; Fariselli, P.; Casadio, R. BaCelLo: a balanced subcellular localization predictor. Bioinformatics,  2006, 22(14), e408-e416.
[http://dx.doi.org/10.1093/bioinformatics/btl222] [PMID:  16873501] 
[47] 
Chou, K.C.; Shen, H.B. Large-scale predictions of gram-negative bacterial protein subcellular locations. J. Proteome Res.,  2006, 5(12), 3420-3428.
[http://dx.doi.org/10.1021/pr060404b] [PMID:  17137343] 
[48] 
Guo, J.; Lin, Y.; Liu, X. GNBSL: A new integrative system to predict the subcellular location for Gram-negative bacteria proteins. Proteomics,  2006, 6(19), 5099-5105.
[http://dx.doi.org/10.1002/pmic.200600064] [PMID:  16955516] 
[49] 
Xiao, X.; Shao, S.; Ding, Y.; Huang, Z.; Chou, K.C. Using cellular automata images and pseudo amino acid composition to predict protein subcellular location. Amino Acids,  2006, 30(1), 49-54.
[http://dx.doi.org/10.1007/s00726-005-0225-6] [PMID:  16044193] 
[50] 
Zhang, T.; Ding, Y.; Chou, K.C. Prediction of protein subcellular location using hydrophobic patterns of amino acid sequence. Comput. Biol. Chem.,  2006, 30(5), 367-371.
[http://dx.doi.org/10.1016/j.compbiolchem.2006.08.003] [PMID:  16963318] 
[51] 
Chen, Y.L.; Li, Q.Z. Prediction of apoptosis protein subcellular location using improved hybrid approach and pseudo-amino acid composition. J. Theor. Biol.,  2007, 248(2), 377-381.
[http://dx.doi.org/10.1016/j.jtbi.2007.05.019] [PMID:  17572445] 
[52] 
Shen, H.B.; Chou, K.C. Gpos-PLoc: an ensemble classifier for predicting subcellular localization of Gram-positive bacterial proteins. Protein Eng. Des. Sel.,  2007, 20(1), 39-46.
[http://dx.doi.org/10.1093/protein/gzl053] [PMID:  17244638] 
[53] 
Chen, Y.L.; Li, Q.Z. Prediction of the subcellular location of apoptosis proteins. J. Theor. Biol.,  2007, 245(4), 775-783.
[http://dx.doi.org/10.1016/j.jtbi.2006.11.010] [PMID:  17189644] 
[54] 
Shen, H.B.; Chou, K.C. Virus-PLoc: a fusion classifier for predicting the subcellular localization of viral proteins within host and virus-infected cells. Biopolymers,  2007, 85(3), 233-240.
[http://dx.doi.org/10.1002/bip.20640] [PMID:  17120237] 
[55] 
Chou, K.C.; Shen, H.B. Large-scale plant protein subcellular location prediction. J. Cell. Biochem.,  2007, 100(3), 665-678.
[http://dx.doi.org/10.1002/jcb.21096] [PMID:  16983686] 
[56] 
Shen, H.B.; Chou, K.C. Nuc-PLoc: a new web-server for predicting protein subnuclear localization by fusing PseAA composition and PsePSSM. Protein Eng. Des. Sel.,  2007, 20(11), 561-567.
[http://dx.doi.org/10.1093/protein/gzm057] [PMID:  17993650] 
[57] 
Nakai, K. Protein sorting signals and prediction of subcellular localization. Adv. Protein Chem.,  2000, 54, 277-344.
[http://dx.doi.org/10.1016/S0065-3233(00)54009-1] [PMID:  10829231] 
[58] 
Chou, K.C.; Shen, H.B. Euk-mPLoc: a fusion classifier for large-scale eukaryotic protein subcellular location prediction by incorporating multiple sites. J. Proteome Res.,  2007, 6(5), 1728-1734.
[http://dx.doi.org/10.1021/pr060635i] [PMID:  17397210] 
[59] 
Shen, H.B.; Chou, K.C. Hum-mPLoc: an ensemble classifier for large-scale human protein subcellular location prediction by incorporating samples with multiple sites. Biochem. Biophys. Res. Commun.,  2007, 355(4), 1006-1011. [BBRC]
[http://dx.doi.org/10.1016/j.bbrc.2007.02.071] [PMID:  17346678] 
[60] 
Chou, K.C.; Shen, H.B. Cell-PLoc: A package of Web servers for predicting subcellular localization of proteins in various organisms. Nat. Protoc.,  2008, 3(2), 153-162.
[http://dx.doi.org/10.1038/nprot.2007.494] [PMID:  18274516] 
[61] 
Chou, K.C.; Shen, H.B. A new method for predicting the subcellular localization of eukaryotic proteins with both single and multiple sites: Euk-mPLoc 2.0. PLoS One,  2010, 5(4)e9931
[http://dx.doi.org/10.1371/journal.pone.0009931] [PMID:  20368981] 
[62] 
Chou, K.C.; Shen, H.B. Cell-PLoc 2.0: An improved package of web-servers for predicting subcellular localization of proteins in various organisms. Nat. Sci.,  2010, 2, 1090-1103.
[http://dx.doi.org/10.4236/ns.2010.210136] 
[63] 
Shen, H.B.; Chou, K.C. Virus-mPLoc: a fusion classifier for viral protein subcellular location prediction by incorporating multiple sites. J. Biomol. Struct. Dyn.,  2010, 28(2), 175-186. [JBSD]
[http://dx.doi.org/10.1080/07391102.2010.10507351] [PMID:  20645651] 
[64] 
Chou, K.C.; Wu, Z.C.; Xiao, X. iLoc-Euk: a multi-label classifier for predicting the subcellular localization of singleplex and multiplex eukaryotic proteins. PLoS One,  2011, 6(3)e18258
[http://dx.doi.org/10.1371/journal.pone.0018258] [PMID:  21483473] 
[65] 
Wan, S.B.; Hu, L.L.; Niu, S.; Wang, K.; Cai, Y.D. Identification of multiple subcellular locations for proteins in budding yeast. Curr. Bioinform.,  2011, 6, 71-80.
[http://dx.doi.org/10.2174/157489311795222374] 
[66] 
Wu, Z.C.; Xiao, X.; Chou, K.C. iLoc-Plant: a multi-label classifier for predicting the subcellular localization of plant proteins with both single and multiple sites. Mol. Biosyst.,  2011, 7(12), 3287-3297.
[http://dx.doi.org/10.1039/c1mb05232b] [PMID:  21984117] 
[67] 
Xiao, X.; Wu, Z.C.; Chou, K.C. A multi-label classifier for predicting the subcellular localization of gram-negative bacterial proteins with both single and multiple sites. PLoS One,  2011, 6(6)e20592
[http://dx.doi.org/10.1371/journal.pone.0020592] [PMID:  21698097] 
[68] 
Xiao, X.; Wu, Z.C.; Chou, K.C. iLoc-Virus: a multi-label learning classifier for identifying the subcellular localization of virus proteins with both single and multiple sites. J. Theor. Biol.,  2011, 284(1), 42-51.
[http://dx.doi.org/10.1016/j.jtbi.2011.06.005] [PMID:  21684290] 
[69] 
Chou, K.C.; Wu, Z.C.; Xiao, X. iLoc-Hum: using the accumulation-label scale to predict subcellular locations of human proteins with both single and multiple sites. Mol. Biosyst.,  2012, 8(2), 629-641.
[http://dx.doi.org/10.1039/C1MB05420A] [PMID:  22134333] 
[70] 
Mei, S. Multi-kernel transfer learning based on Chou’s PseAAC formulation for protein submitochondria localization. J. Theor. Biol.,  2012, 293, 121-130.
[http://dx.doi.org/10.1016/j.jtbi.2011.10.015] [PMID:  22037046] 
[71] 
Mei, S. Predicting plant protein subcellular multi-localization by Chou’s PseAAC formulation based multi-label homolog knowledge transfer learning. J. Theor. Biol.,  2012, 310, 80-87.
[http://dx.doi.org/10.1016/j.jtbi.2012.06.028] [PMID:  22750634] 
[72] 
Wu, Z.C.; Xiao, X.; Chou, K.C. iLoc-Gpos: a multi-layer classifier for predicting the subcellular localization of singleplex and multiplex Gram-positive bacterial proteins. Protein Pept. Lett.,  2012, 19(1), 4-14.
[http://dx.doi.org/10.2174/092986612798472839] [PMID:  21919865] 
[73] 
Huang, C.; Yuan, J. Using radial basis function on the general form of Chou’s pseudo amino acid composition and PSSM to predict subcellular locations of proteins with both single and multiple sites. Biosystems,  2013, 113(1), 50-57.
[http://dx.doi.org/10.1016/j.biosystems.2013.04.005] [PMID:  23669601] 
[74] 
Wang, X.; Li, G.Z.; Lu, W.C. Virus-ECC-mPLoc: A multi-label predictor for predicting the subcellular localization of virus proteins with both single and multiple sites based on a general form of Chou’s pseudo amino acid composition. Protein Pept. Lett.,  2013, 20(3), 309-317.
[PMID:  22591474] 
[75] 
Huang, C.; Yuan, J.Q. Predicting protein subchloroplast locations with both single and multiple sites via three different modes of Chou’s pseudo amino acid compositions. J. Theor. Biol.,  2013, 335, 205-212.
[http://dx.doi.org/10.1016/j.jtbi.2013.06.034] [PMID:  23850480] 
[76] 
Pacharawongsakda, E.; Theeramunkong, T. Predict subcellular locations of singleplex and multiplex proteins by semi-supervised learning and dimension-reducing general mode of Chou’s PseAAC. IEEE Trans. Nanobioscience,  2013, 12(4), 311-320.
[http://dx.doi.org/10.1109/TNB.2013.2272014] [PMID:  23864226] 
[77] 
Mandal, M.; Mukhopadhyay, A.; Maulik, U. Prediction of protein subcellular localization by incorporating multiobjective PSO-based feature subset selection into the general form of Chou’s PseAAC. Med. Biol. Eng. Comput.,  2015, 53(4), 331-344.
[http://dx.doi.org/10.1007/s11517-014-1238-7] [PMID:  25564182] 
[78] 
Cheng, X.; Xiao, X.; Chou, K.C. pLoc-mPlant: Predict subcellular localization of multi-location plant proteins by incorporating the optimal GO information into general PseAAC. Mol. Biosyst.,  2017, 13(9), 1722-1727.
[http://dx.doi.org/10.1039/C7MB00267J] [PMID:  28702580] 
[79] 
Cheng, X.; Xiao, X.; Chou, K.C. pLoc-mGneg: Predict subcellular localization of Gram-negative bacterial proteins by deep gene ontology learning via general PseAAC. Genomics,  2017, 110, 231-239.
[http://dx.doi.org/10.1016/j.ygeno.2017.10.002] [PMID:  28989035] 
[80] 
Cheng, X.; Zhao, S.G.; Lin, W.Z.; Xiao, X.; Chou, K.C. pLoc-mAnimal: predict subcellular localization of animal proteins with both single and multiple sites. Bioinformatics,  2017, 33(22), 3524-3531.
[http://dx.doi.org/10.1093/bioinformatics/btx476] [PMID:  29036535] 
[81] 
Cheng, X.; Xiao, X.; Chou, K.C. pLoc-mEuk: Predict subcellular localization of multi-label eukaryotic proteins by extracting the key GO information into general PseAAC. Genomics,  2018, 110(1), 50-58.
[http://dx.doi.org/10.1016/j.ygeno.2017.08.005] [PMID:  28818512] 
[82] 
Cheng, X.; Xiao, X.; Chou, K.C. pLoc-mHum: predict subcellular localization of multi-location human proteins via general PseAAC to winnow out the crucial GO information. Bioinformatics,  2018, 34(9), 1448-1456.
[http://dx.doi.org/10.1093/bioinformatics/btx711] [PMID:  29106451] 
[83] 
Zhong, W.Z.; Zhou, S.F. Molecular science for drug development and biomedicine. Int. J. Mol. Sci.,  2014, 15(11), 20072-20078.
[http://dx.doi.org/10.3390/ijms151120072] [PMID:  25375190] 
[84] 
Du, Q.S.; Huang, R.B.; Wang, S.Q.; Chou, K.C. Designing inhibitors of M2 proton channel against H1N1 swine influenza virus. PLoS One,  2010, 5(2)e9388
[http://dx.doi.org/10.1371/journal.pone.0009388] [PMID:  20186344] 
[85] 
Wang, S.Q.; Cheng, X.C.; Dong, W.L.; Wang, R.L.; Chou, K.C. Three new powerful oseltamivir derivatives for inhibiting the neuraminidase of influenza virus. Biochem. Biophys. Res. Commun.,  2010, 401(2), 188-191. [BBRC]
[http://dx.doi.org/10.1016/j.bbrc.2010.09.020] [PMID:  20849817] 
[86] 
Li, X.B.; Wang, S.Q.; Xu, W.R.; Wang, R.L.; Chou, K.C. Novel inhibitor design for hemagglutinin against H1N1 influenza virus by core hopping method. PLoS One,  2011, 6(11)e28111
[http://dx.doi.org/10.1371/journal.pone.0028111] [PMID:  22140516] 
[87] 
Ma, Y.; Wang, S.Q.; Xu, W.R.; Wang, R.L.; Chou, K.C. Design novel dual agonists for treating type-2 diabetes by targeting peroxisome proliferator-activated receptors with core hopping approach. PLoS One,  2012, 7(6)e38546
[http://dx.doi.org/10.1371/journal.pone.0038546] [PMID:  22685582] 
[88] 
Liu, L.; Ma, Y.; Wang, R.L.; Xu, W.R.; Wang, S.Q.; Chou, K.C. Find novel dual-agonist drugs for treating type 2 diabetes by means of cheminformatics. Drug Des. Devel. Ther.,  2013, 7, 279-288.
[PMID:  23630413] 
[89] 
Chou, K.C. Some remarks on protein attribute prediction and pseudo amino acid composition. J. Theor. Biol.,  2011, 273(1), 236-247.
[http://dx.doi.org/10.1016/j.jtbi.2010.12.024] [PMID:  21168420] 
[90] 
Chou, K.C.; Shen, H.B. Signal-CF: a subsite-coupled and window-fusing approach for predicting signal peptides. Biochem. Biophys. Res. Commun.,  2007, 357(3), 633-640. [BBRC]
[http://dx.doi.org/10.1016/j.bbrc.2007.03.162] [PMID:  17434148] 
[91] 
Shen, H.B.; Chou, K.C. Signal-3L: A 3-layer approach for predicting signal peptides. Biochem. Biophys. Res. Commun.,  2007, 363(2), 297-303. [BBRC]
[http://dx.doi.org/10.1016/j.bbrc.2007.08.140] [PMID:  17880924] 
[92] 
Chou, K.C.; Shen, H.B. MemType-2L: a web server for predicting membrane proteins and their types by incorporating evolution information through Pse-PSSM. Biochem. Biophys. Res. Commun.,  2007, 360(2), 339-345. [BBRC]
[http://dx.doi.org/10.1016/j.bbrc.2007.06.027] [PMID:  17586467] 
[93] 
Chou, K.C.; Shen, H.B. ProtIdent: a web server for identifying proteases and their types by fusing functional domain and sequential evolution information. Biochem. Biophys. Res. Commun.,  2008, 376(2), 321-325. [BBRC]
[http://dx.doi.org/10.1016/j.bbrc.2008.08.125] [PMID:  18774775] 
[94] 
Shen, H.B.; Chou, K.C. QuatIdent: a web server for identifying protein quaternary structural attribute by fusing functional domain and sequential evolution information. J. Proteome Res.,  2009, 8(3), 1577-1584.
[http://dx.doi.org/10.1021/pr800957q] [PMID:  19226167] 
[95] 
Xiao, X.; Wang, P.; Chou, K.C. GPCR-2L: predicting G protein-coupled receptors and their types by hybridizing two different modes of pseudo amino acid compositions. Mol. Biosyst.,  2011, 7(3), 911-919.
[http://dx.doi.org/10.1039/C0MB00170H] [PMID:  21180772] 
[96] 
Wang, P.; Xiao, X.; Chou, K.C. NR-2L: a two-level predictor for identifying nuclear receptor subfamilies based on sequence-derived features. PLoS One,  2011, 6(8)e23505
[http://dx.doi.org/10.1371/journal.pone.0023505] [PMID:  21858146] 
[97] 
Xiao, X.; Wang, P.; Chou, K.C. Quat-2L: a web-server for predicting protein quaternary structural attributes. Mol. Divers.,  2011, 15(1), 149-155.
[http://dx.doi.org/10.1007/s11030-010-9227-8] [PMID:  20148364] 
[98] 
Chen, W.; Lin, H.; Feng, P.M.; Ding, C.; Zuo, Y.C.; Chou, K.C. iNuc-PhysChem: a sequence-based predictor for identifying nucleosomes via physicochemical properties. PLoS One,  2012, 7(10)e47843
[http://dx.doi.org/10.1371/journal.pone.0047843] [PMID:  23144709] 
[99] 
Xiao, X.; Wang, P.; Chou, K.C. iNR-PhysChem: a sequence-based predictor for identifying nuclear receptors and their subfamilies via physical-chemical property matrix. PLoS One,  2012, 7(2)e30869
[http://dx.doi.org/10.1371/journal.pone.0030869] [PMID:  22363503] 
[100] 
Feng, P.M.; Chen, W.; Lin, H.; Chou, K.C. iHSP-PseRAAAC: Identifying the heat shock protein families using pseudo reduced amino acid alphabet composition. Anal. Biochem.,  2013, 442(1), 118-125.
[http://dx.doi.org/10.1016/j.ab.2013.05.024] [PMID:  23756733] 
[101] 
Min, J.L.; Xiao, X.; Chou, K.C. iEzy-drug: a web server for identifying the interaction between enzymes and drugs in cellular networking. BioMed Res. Int., 2013.2013701317 [BMRI]
[http://dx.doi.org/10.1155/2013/701317] [PMID:  24371828] 
[102] 
Xiao, X.; Min, J.L.; Wang, P.; Chou, K.C. iGPCR-drug: a web server for predicting interaction between GPCRs and drugs in cellular networking. PLoS One,  2013, 8(8)e72234
[http://dx.doi.org/10.1371/journal.pone.0072234] [PMID:  24015221] 
[103] 
Xiao, X.; Min, J.L.; Wang, P.; Chou, K.C. iCDI-PseFpt: identify the channel-drug interaction in cellular networking with PseAAC and molecular fingerprints. J. Theor. Biol.,  2013, 337, 71-79.
[http://dx.doi.org/10.1016/j.jtbi.2013.08.013] [PMID:  23988798] 
[104] 
Xu, Y.; Ding, J.; Wu, L.Y.; Chou, K.C. iSNO-PseAAC: predict cysteine S-nitrosylation sites in proteins by incorporating position specific amino acid propensity into pseudo amino acid composition. PLoS One,  2013, 8(2)e55844
[http://dx.doi.org/10.1371/journal.pone.0055844] [PMID:  23409062] 
[105] 
Xiao, X.; Wang, P.; Lin, W.Z.; Jia, J.H.; Chou, K.C. iAMP-2L: a two-level multi-label classifier for identifying antimicrobial peptides and their functional types. Anal. Biochem.,  2013, 436(2), 168-177.
[http://dx.doi.org/10.1016/j.ab.2013.01.019] [PMID:  23395824] 
[106] 
Xu, Y.; Shao, X.J.; Wu, L.Y.; Deng, N.Y.; Chou, K.C. iSNO-AAPair: incorporating amino acid pairwise coupling
into PseAAC for predicting cysteine S-nitrosylation sites in
proteins. PeerJ, 2013. 1e171
[http://dx.doi.org/10.7717/peerj.171] [PMID: 24109555] 
[107] 
Ding, H.; Deng, E.Z.; Yuan, L.F.; Liu, L.; Lin, H.; Chen, W.; Chou, K.C. iCTX-type: a sequence-based predictor for identifying the types of conotoxins in targeting ion channels. BioMed Res. Int., 2014.2014286419 [BMRI]
[http://dx.doi.org/10.1155/2014/286419] [PMID:  24991545] 
[108] 
Liu, B.; Xu, J.; Lan, X.; Xu, R.; Zhou, J.; Wang, X.; Chou, K.C. 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,  2014, 9(9)e106691
[http://dx.doi.org/10.1371/journal.pone.0106691] [PMID:  25184541] 
[109] 
Xu, Y.; Wen, X.; Shao, X.J.; Deng, N.Y.; Chou, K.C. iHyd-PseAAC: predicting hydroxyproline and hydroxylysine in proteins by incorporating dipeptide position-specific propensity into pseudo amino acid composition. Int. J. Mol. Sci.,  2014, 15(5), 7594-7610.
[http://dx.doi.org/10.3390/ijms15057594] [PMID:  24857907] 
[110] 
Qiu, W.R.; Xiao, X.; Lin, W.Z.; Chou, K.C. iMethyl-PseAAC: identification of protein methylation sites via a pseudo amino acid composition approach. BioMed Res. Int.,  2014, •••2014947416 [BMRI]
[http://dx.doi.org/10.1155/2014/947416] [PMID:  24977164] 
[111] 
Xu, Y.; Wen, X.; Wen, L.S.; Wu, L.Y.; Deng, N.Y.; Chou, K.C. iNitro-Tyr: prediction of nitrotyrosine sites in proteins with general pseudo amino acid composition. PLoS One,  2014, 9(8)e105018
[http://dx.doi.org/10.1371/journal.pone.0105018] [PMID:  25121969] 
[112] 
Fan, Y.N.; Xiao, X.; Min, J.L.; Chou, K.C. iNR-Drug: predicting the interaction of drugs with nuclear receptors in cellular networking. Int. J. Mol. Sci.,  2014, 15(3), 4915-4937. [IJMS]
[http://dx.doi.org/10.3390/ijms15034915] [PMID:  24651462] 
[113] 
Guo, S.H.; Deng, E.Z.; Xu, L.Q.; Ding, H.; Lin, H.; Chen, W.; Chou, K.C. iNuc-PseKNC: a sequence-based predictor for predicting nucleosome positioning in genomes with pseudo k-tuple nucleotide composition. Bioinformatics,  2014, 30(11), 1522-1529.
[http://dx.doi.org/10.1093/bioinformatics/btu083] [PMID:  24504871] 
[114] 
Lin, H.; Deng, E.Z.; Ding, H.; Chen, W.; Chou, K.C. iPro54-PseKNC: a sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition. Nucleic Acids Res.,  2014, 42(21), 12961-12972.
[http://dx.doi.org/10.1093/nar/gku1019] [PMID:  25361964] 
[115] 
Qiu, W.R.; Xiao, X.; Chou, K.C. iRSpot-TNCPseAAC: identify recombination spots with trinucleotide composition and pseudo amino acid components. Int. J. Mol. Sci.,  2014, 15(2), 1746-1766. [IJMS]
[http://dx.doi.org/10.3390/ijms15021746] [PMID:  24469313] 
[116] 
Chen, W.; Feng, P.M.; Lin, H.; Chou, K.C. iSS-PseDNC: identifying splicing sites using pseudo dinucleotide composition. BioMed Res. Int., 2014.2014623149 [BMRI]
[http://dx.doi.org/10.1155/2014/623149] [PMID:  24967386] 
[117] 
Chen, W.; Feng, P.M.; Deng, E.Z.; Lin, H.; Chou, K.C. iTIS-PseTNC: a sequence-based predictor for identifying translation initiation site in human genes using pseudo trinucleotide composition. Anal. Biochem.,  2014, 462, 76-83.
[http://dx.doi.org/10.1016/j.ab.2014.06.022] [PMID:  25016190] 
[118] 
Qiu, W.R.; Xiao, X.; Lin, W.Z.; Chou, K.C. iUbiq-Lys: prediction of lysine ubiquitination sites in proteins by extracting sequence evolution information via a gray system model. J. Biomol. Struct. Dyn.,  2015, 33(8), 1731-1742. [JBSD]
[http://dx.doi.org/10.1080/07391102.2014.968875] [PMID:  25248923] 
[119] 
Chen, W.; Feng, P.; Ding, H.; Lin, H.; Chou, K.C. iRNA-Methyl: Identifying N(6)-methyladenosine sites using pseudo nucleotide composition. Anal. Biochem.,  2015, 490, 26-33.
[http://dx.doi.org/10.1016/j.ab.2015.08.021] [PMID:  26314792] 
[120] 
Jia, J.; Liu, Z.; Xiao, X.; Liu, B.; Chou, K.C. iPPI-Esml: An ensemble classifier for identifying the interactions of proteins by incorporating their physicochemical properties and wavelet transforms into PseAAC. J. Theor. Biol.,  2015, 377, 47-56.
[http://dx.doi.org/10.1016/j.jtbi.2015.04.011] [PMID:  25908206] 
[121] 
Xiao, X.; Min, J.L.; Lin, W.Z.; Liu, Z.; Cheng, X.; Chou, K.C. iDrug-Target: predicting the interactions between drug compounds and target proteins in cellular networking via benchmark dataset optimization approach. J. Biomol. Struct. Dyn.,  2015, 33(10), 2221-2233. [JBSD]
[http://dx.doi.org/10.1080/07391102.2014.998710] [PMID:  25513722] 
[122] 
Liu, Z.; Xiao, X.; Qiu, W.R.; Chou, K.C. iDNA-Methyl: identifying DNA methylation sites via pseudo trinucleotide composition. Anal. Biochem.,  2015, 474, 69-77.
[http://dx.doi.org/10.1016/j.ab.2014.12.009] [PMID:  25596338] 
[123] 
Liu, B.; Fang, L.; Liu, F.; Wang, X.; Chen, J.; Chou, K.C. Identification of real microRNA precursors with a pseudo structure status composition approach. PLoS One,  2015, 10(3)e0121501
[http://dx.doi.org/10.1371/journal.pone.0121501] [PMID:  25821974] 
[124] 
Liu, B.; Fang, L.; Wang, S.; Wang, X.; Li, H.; Chou, K.C. Identification of microRNA precursor with the degenerate K-tuple or Kmer strategy. J. Theor. Biol.,  2015, 385, 153-159.
[http://dx.doi.org/10.1016/j.jtbi.2015.08.025] [PMID:  26362104] 
[125] 
Chen, J.; Long, R.; Wang, X.L.; Liu, B.; Chou, K.C. dRHP-PseRA: detecting remote homology proteins using profile-based pseudo protein sequence and rank aggregation. Sci. Rep.,  2016, 6, 32333.
[http://dx.doi.org/10.1038/srep32333] [PMID:  27581095] 
[126] 
Chen, W.; Ding, H.; Feng, P.; Lin, H.; Chou, K.C. iACP: a sequence-based tool for identifying anticancer peptides. Oncotarget,  2016, 7(13), 16895-16909.
[http://dx.doi.org/10.18632/oncotarget.7815] [PMID:  26942877] 
[127] 
Jia, J.; Liu, Z.; Xiao, X.; Liu, B.; Chou, K.C. iCar-PseCp: identify carbonylation sites in proteins by Monte Carlo sampling and incorporating sequence coupled effects into general PseAAC. Oncotarget,  2016, 7(23), 34558-34570.
[http://dx.doi.org/10.18632/oncotarget.9148] [PMID:  27153555] 
[128] 
Jia, J.; Liu, Z.; Xiao, X.; Liu, B.; Chou, K.C. Identification of protein-protein binding sites by incorporating the physicochemical properties and stationary wavelet transforms into pseudo amino acid composition. J. Biomol. Struct. Dyn.,  2016, 34(9), 1946-1961. [JBSD]
[http://dx.doi.org/10.1080/07391102.2015.1095116] [PMID:  26375780] 
[129] 
Liu, B.; Long, R.; Chou, K.C. iDHS-EL: identifying DNase I hypersensitive sites by fusing three different modes of pseudo nucleotide composition into an ensemble learning framework. Bioinformatics,  2016, 32(16), 2411-2418.
[http://dx.doi.org/10.1093/bioinformatics/btw186] [PMID:  27153623] 
[130] 
Liu, B.; Fang, L.; Long, R.; Lan, X.; Chou, K.C. iEnhancer-2L: a two-layer predictor for identifying enhancers and their strength by pseudo k-tuple nucleotide composition. Bioinformatics,  2016, 32(3), 362-369.
[http://dx.doi.org/10.1093/bioinformatics/btv604] [PMID:  26476782] 
[131] 
Qiu, W.R.; Sun, B.Q.; Xiao, X.; Xu, Z.C.; Chou, K.C. iHyd-PseCp: Identify hydroxyproline and hydroxylysine in proteins by incorporating sequence-coupled effects into general PseAAC. Oncotarget,  2016, 7(28), 44310-44321.
[http://dx.doi.org/10.18632/oncotarget.10027] [PMID:  27322424] 
[132] 
Liu, B.; Fang, L.; Liu, F.; Wang, X.; Chou, K.C. iMiRNA-PseDPC: microRNA precursor identification with a pseudo distance-pair composition approach. J. Biomol. Struct. Dyn.,  2016, 34(1), 223-235. [JBSD]
[http://dx.doi.org/10.1080/07391102.2015.1014422] [PMID:  25645238] 
[133] 
Zhang, C.J.; Tang, H.; Li, W.C.; Lin, H.; Chen, W.; Chou, K.C. iOri-Human: identify human origin of replication by incorporating dinucleotide physicochemical properties into pseudo nucleotide composition. Oncotarget,  2016, 7(43), 69783-69793.
[http://dx.doi.org/10.18632/oncotarget.11975] [PMID:  27626500] 
[134] 
Qiu, W.R.; Xiao, X.; Xu, Z.C.; Chou, K.C. iPhos-PseEn: identifying phosphorylation sites in proteins by fusing different pseudo components into an ensemble classifier. Oncotarget,  2016, 7(32), 51270-51283.
[http://dx.doi.org/10.18632/oncotarget.9987] [PMID:  27323404] 
[135] 
Jia, J.; Liu, Z.; Xiao, X.; Liu, B.; Chou, K.C. iPPBS-Opt: A Sequence-Based Ensemble Classifier for Identifying Protein-Protein Binding Sites by Optimizing Imbalanced Training Datasets. Molecules,  2016, 21(1)E95
[http://dx.doi.org/10.3390/molecules21010095] [PMID:  26797600] 
[136] 
Qiu, W.R.; Sun, B.Q.; Xiao, X.; Xu, Z.C.; Chou, K.C. iPTM-mLys: identifying multiple lysine PTM sites and their different types. Bioinformatics,  2016, 32(20), 3116-3123.
[http://dx.doi.org/10.1093/bioinformatics/btw380] [PMID:  27334473] 
[137] 
Chen, W.; Tang, H.; Ye, J.; Lin, H.; Chou, K.C. iRNAPseU: Identifying RNA pseudouridine sites. Mol. Ther. Nucleic Acids, 2016. 5e332
[PMID: 28427142] 
[138] 
Xiao, X.; Ye, H.X.; Liu, Z.; Jia, J.H.; Chou, K.C. iROS-gPseKNC: Predicting replication origin sites in DNA by incorporating dinucleotide position-specific propensity into general pseudo nucleotide composition. Oncotarget,  2016, 7(23), 34180-34189.
[http://dx.doi.org/10.18632/oncotarget.9057] [PMID:  27147572] 
[139] 
Jia, J.; Liu, Z.; Xiao, X.; Liu, B.; Chou, K.C. iSuc-PseOpt: Identifying lysine succinylation sites in proteins by incorporating sequence-coupling effects into pseudo components and optimizing imbalanced training dataset. Anal. Biochem.,  2016, 497, 48-56.
[http://dx.doi.org/10.1016/j.ab.2015.12.009] [PMID:  26723495] 
[140] 
Liu, Z.; Xiao, X.; Yu, D.J.; Jia, J.; Qiu, W.R.; Chou, K.C. pRNAm-PC: Predicting N(6)-methyladenosine sites in RNA sequences via physical-chemical properties. Anal. Biochem.,  2016, 497, 60-67.
[http://dx.doi.org/10.1016/j.ab.2015.12.017] [PMID:  26748145] 
[141] 
Jia, J.; Liu, Z.; Xiao, X.; Liu, B.; Chou, K.C. pSuc-Lys: Predict lysine succinylation sites in proteins with PseAAC and ensemble random forest approach. J. Theor. Biol.,  2016, 394, 223-230.
[http://dx.doi.org/10.1016/j.jtbi.2016.01.020] [PMID:  26807806] 
[142] 
Jia, J.; Zhang, L.; Liu, Z.; Xiao, X.; Chou, K.C. pSumo-CD: predicting sumoylation sites in proteins with covariance discriminant algorithm by incorporating sequence-coupled effects into general PseAAC. Bioinformatics,  2016, 32(20), 3133-3141.
[http://dx.doi.org/10.1093/bioinformatics/btw387] [PMID:  27354696] 
[143] 
Liu, B.; Wang, S.; Long, R.; Chou, K.C. iRSpot-EL: identify recombination spots with an ensemble learning approach. Bioinformatics,  2017, 33(1), 35-41.
[http://dx.doi.org/10.1093/bioinformatics/btw539] [PMID:  27531102] 
[144] 
Qiu, W.R.; Jiang, S.Y.; Xu, Z.C.; Xiao, X.; Chou, K.C. iRNAm5C-PseDNC: identifying RNA 5-methylcytosine sites by incorporating physical-chemical properties into pseudo dinucleotide composition. Oncotarget,  2017, 8(25), 41178-41188.
[http://dx.doi.org/10.18632/oncotarget.17104] [PMID:  28476023] 
[145] 
Feng, P.; Ding, H.; Yang, H.; Chen, W.; Lin, H.; Chou, K.C. iRNA-PseColl: Identifying the occurrence sites of different RNA modifications by incorporating collective effects of nucleotides into PseKNC. Mol. Ther. Nucleic Acids,  2017, 7, 155-163.
[http://dx.doi.org/10.1016/j.omtn.2017.03.006] [PMID:  28624191] 
[146] 
Chen, W.; Feng, P.; Yang, H.; Ding, H.; Lin, H.; Chou, K.C. iRNA-AI: identifying the adenosine to inosine editing sites in RNA sequences. Oncotarget,  2017, 8(3), 4208-4217.
[http://dx.doi.org/10.18632/oncotarget.13758] [PMID:  27926534] 
[147] 
Qiu, W.R.; Jiang, S.Y.; Sun, B.Q.; Xiao, X.; Cheng, X.; Chou, K.C. iRNA-2methyl: identify RNA 2′-O-methylation sites by incorporating sequence-coupled effects into general PseKNC and ensemble classifier. Med. Chem.,  2017, 13(8), 734-743.
[http://dx.doi.org/10.2174/1573406413666170623082245] [PMID:  28641529] 
[148] 
Xu, Y.; Wang, Z.; Li, C.; Chou, K.C. iPreny-PseAAC: identify C-terminal cysteine prenylation sites in proteins by incorporating two tiers of sequence couplings into PseAAC. Med. Chem.,  2017, 13(6), 544-551.
[http://dx.doi.org/10.2174/1573406413666170419150052] [PMID:  28425870] 
[149] 
Qiu, W.R.; Sun, B.Q.; Xiao, X.; Xu, D. iPhos-PseEvo: Identifying human phosphorylated proteins by incorporating evolutionary information into general PseAAC via grey system theory. Molecular Informatics,  2017, 36 UNSP 1600010.
[150] 
Liu, L.M.; Xu, Y.; Chou, K.C. iPGK-PseAAC: identify lysine phosphoglycerylation sites in proteins by incorporating four different tiers of amino acid pairwise coupling information into the general PseAAC. Med. Chem.,  2017, 13(6), 552-559.
[http://dx.doi.org/10.2174/1573406413666170515120507] [PMID:  28521678] 
[151] 
Qiu, W.R.; Sun, B.Q.; Xiao, X.; Xu, Z.C.; Jia, J.H.; Chou, K.C. iKcr-PseEns: Identify lysine crotonylation sites in histone proteins with pseudo components and ensemble classifier. Genomics,  2018, 110(5), 239-246.
[http://dx.doi.org/10.1016/j.ygeno.2017.10.008] [PMID:  29107015] 
[152] 
Cheng, X.; Zhao, S.G.; Xiao, X. iATC-mISF: a multi-label classifier for predicting the classes of anatomical therapeutic chemicals. Bioinformatics (Corrigendum, ibid., 2017, Vol.33, 2610),  2017, 33, 341-346.
[153] 
Cheng, X.; Zhao, S.G.; Xiao, X.; Chou, K.C. iATC-mHyb: a hybrid multi-label classifier for predicting the classification of anatomical therapeutic chemicals. Oncotarget,  2017, 8(35), 58494-58503.
[http://dx.doi.org/10.18632/oncotarget.17028] [PMID:  28938573] 
[154] 
Liu, B.; Yang, F.; Chou, K.C. 2L-piRNA: A two-layer ensemble classifier for identifying piwi-interacting RNAs and their function. Mol. Ther. Nucleic Acids,  2017, 7, 267-277.
[http://dx.doi.org/10.1016/j.omtn.2017.04.008] [PMID:  28624202] 
[155] 
Feng, P.; Yang, H.; Ding, H.; Lin, H.; Chen, W.; Chou, K.C. iDNA6mA-PseKNC: Identifying DNA N6-methyladenosine sites by incorporating nucleotide physicochemical properties into PseKNC. Genomics,  2019, 111(1), 96-102.
[http://dx.doi.org/10.1016/j.ygeno.2018.01.005] [PMID:  29360500] 
[156] 
Liu, B.; Li, K.; Huang, D.S.; Chou, K.C. iEnhancer-EL: identifying enhancers and their strength with ensemble learning approach. Bioinformatics,  2018, 34(22), 3835-3842.
[http://dx.doi.org/10.1093/bioinformatics/bty458] [PMID:  29878118] 
[157] 
Su, Z.D.; Huang, Y.; Zhang, Z.Y.; Zhao, Y.W.; Wang, D.; Chen, W.; Chou, K.C.; Lin, H. iLoc-lncRNA: predict the subcellular location of lncRNAs by incorporating octamer composition into general PseKNC. Bioinformatics,  2018, 34(24), 4196-4204.
[http://dx.doi.org/10.1093/bioinformatics/bty508] [PMID:  29931187] 
[158] 
Liu, B.; Yang, F.; Huang, D.S.; Chou, K.C. iPromoter-2L: a two-layer predictor for identifying promoters and their types by multi-window-based PseKNC. Bioinformatics,  2018, 34(1), 33-40.
[http://dx.doi.org/10.1093/bioinformatics/btx579] [PMID:  28968797] 
[159] 
Song, J.; Wang, Y.; Li, F.; Akutsu, T.; Rawlings, N.D.; Webb, G.I. iProt-Sub: a comprehensive package for accurately mapping and predicting protease-specific substrates and cleavage sites. Brief. Bioinform., 2018.
[http://dx.doi.org/10.1093/bib/bby028] [PMID:  29897410] 
[160] 
Chen, W.; Feng, P.; Yang, H.; Ding, H.; Lin, H.; Chou, K.C. iRNA-3typeA: identifying 3-types of modification at RNA’s adenosine sites. Mol. Ther. Nucleic Acids,  2018, 11, 468-474.
[http://dx.doi.org/10.1016/j.omtn.2018.03.012] [PMID:  29858081] 
[161] 
Liu, B.; Weng, F.; Huang, D.S.; Chou, K.C. iRO-3wPseKNC: identify DNA replication origins by three-window-based PseKNC. Bioinformatics,  2018, 34(18), 3086-3093.
[http://dx.doi.org/10.1093/bioinformatics/bty312] [PMID:  29684124] 
[162] 
Yang, H.; Qiu, W.R.; Liu, G.; Guo, F.B.; Chen, W.; Chou, K.C.; Lin, H. iRSpot-Pse6NC: Identifying recombination spots in Saccharomyces cerevisiae by incorporating hexamer composition into general PseKNC. Int. J. Biol. Sci.,  2018, 14(8), 883-891.
[http://dx.doi.org/10.7150/ijbs.24616] [PMID:  29989083] 
[163] 
Song, J.; Li, F.; Takemoto, K.; Haffari, G.; Akutsu, T.; Chou, K.C.; Webb, G.I. PREvaIL, an integrative approach for inferring catalytic residues using sequence, structural, and network features in a machine-learning framework. J. Theor. Biol.,  2018, 443, 125-137.
[http://dx.doi.org/10.1016/j.jtbi.2018.01.023] [PMID:  29408627] 
[164] 
Li, F.; Li, C.; Marquez-Lago, T.T.; Leier, A.; Akutsu, T.; Purcell, A.W.; Ian Smith, A.; Lithgow, T.; Daly, R.J.; Song, J.; Chou, K.C. Quokka: a comprehensive tool for rapid and accurate prediction of kinase family-specific phosphorylation sites in the human proteome. Bioinformatics,  2018, 34(24), 4223-4231.
[http://dx.doi.org/10.1093/bioinformatics/bty522] [PMID:  29947803] 
[165] 
Cai, L.; Huang, T.; Su, J.; Zhang, X.; Chen, W.; Zhang, F.; He, L.; Chou, K.C. Implications of newly identified brain eQTL genes and their interactors in Schizophrenia. Mol. Ther. Nucleic Acids,  2018, 12, 433-442.
[http://dx.doi.org/10.1016/j.omtn.2018.05.026] [PMID:  30195780] 
[166] 
(a)Ehsan, A.; Mahmood, M.K.; Khan, Y.D.; Barukab, O.M.; Khan, S.A. iHyd-PseAAC (EPSV): Identify hydroxylation sites in proteins by extracting enhanced position and sequence variant feature via Chou’s 5-step rule and general pseudo amino acid composition. Curr. Genomics,  2019, 20(2), 124-133.
(b)Niu, B.; Liang, C.; Lu, Y.; Zhao, M.; Chen, Q.; Zhang, Y.; Zheng, L. Glioma stages prediction based on machine learning algorithm combined with protein-protein interaction networks. Genomics, 2019. S0888-7543(19), 30174-0.
[http://dx.doi.org/10.1016/j.ygeno.2019.05.024] 
(c)Awais, M.; Hussain, W.; Khan, Y.D.; Rasool, N.; Khan, S.A.; Chou, K.C. iPhosH-PseAAC: Identify phosphohistidine sites in proteins by blending statistical moments and position relative features according to the Chou’s 5-step rule and general pseudo amino acid composition. IEEE/ACM Trans. Comput. Biol. Bioinformatics, 2019.
[http://dx.doi.org/10.1109/TCBB.2019.2919025] [PMID:  31144645] 
(d)Zhang, Y.; Xie, R.; Wang, J.; Leier, A.; Marquez-Lago, T.T.; Akutsu, T.; Webb, G.I.; Chou, K.C.; Song, J. Computational analysis and prediction of lysine malonylation sites by exploiting informative features in an integrative machine-learning framework. Brief. Bioinform., 2018.
[http://dx.doi.org/10.1093/bib/bby079] [PMID:  30351377] 
[167] 
Chen, W.; Feng, P.M.; Lin, H.; Chou, K.C. iRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition. Nucleic Acids Res.,  2013, 41(6)e68
[http://dx.doi.org/10.1093/nar/gks1450] [PMID:  23303794] 
[168] 
Chou, K.C.; Elrod, D.W. Bioinformatical analysis of G-protein-coupled receptors. J. Proteome Res.,  2002, 1(5), 429-433.
[http://dx.doi.org/10.1021/pr025527k] [PMID:  12645914] 
[169] 
Chou, K.C.; Cai, Y.D. Prediction of protease types in a hybridization space. Biochem. Biophys. Res. Commun.,  2006, 339(3), 1015-1020. [BBRC]
[http://dx.doi.org/10.1016/j.bbrc.2005.10.196] [PMID:  16325146] 
[170] 
Lin, W.Z.; Fang, J.A.; Xiao, X.; Chou, K.C. iDNA-Prot: identification of DNA binding proteins using random forest with grey model. PLoS One,  2011, 6(9)e24756
[http://dx.doi.org/10.1371/journal.pone.0024756] [PMID:  21935457] 
[171] 
Kandaswamy, K.K.; Chou, K.C.; Martinetz, T.; Möller, S.; Suganthan, P.N.; Sridharan, S.; Pugalenthi, G. AFP-Pred: A random forest approach for predicting antifreeze proteins from sequence-derived properties. J. Theor. Biol.,  2011, 270(1), 56-62.
[http://dx.doi.org/10.1016/j.jtbi.2010.10.037] [PMID:  21056045] 
[172] 
Chou, K.C. Prediction of protein cellular attributes using
pseudo amino acid composition. PROTEINS: Structure,
Function, and Genetics (Erratum: ibid., 2001, Vol.44, 60),  2001, 43, 246-255.
[173] 
Chou, K.C. Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes. Bioinformatics,  2005, 21(1), 10-19.
[http://dx.doi.org/10.1093/bioinformatics/bth466] [PMID:  15308540] 
[174] 
Xiao, X.; Shao, S.; Ding, Y.; Huang, Z.; Chen, X.; Chou, K.C. Using cellular automata to generate image representation for biological sequences. Amino Acids,  2005, 28(1), 29-35.
[http://dx.doi.org/10.1007/s00726-004-0154-9] [PMID:  15700108] 
[175] 
Zhou, X.B.; Chen, C.; Li, Z.C.; Zou, X.Y. Using Chou’s amphiphilic pseudo-amino acid composition and support vector machine for prediction of enzyme subfamily classes. J. Theor. Biol.,  2007, 248(3), 546-551.
[http://dx.doi.org/10.1016/j.jtbi.2007.06.001] [PMID:  17628605] 
[176] 
Nanni, L.; Lumini, A. Genetic programming for creating Chou’s pseudo amino acid based features for submitochondria localization. Amino Acids,  2008, 34(4), 653-660.
[http://dx.doi.org/10.1007/s00726-007-0018-1] [PMID:  18175047] 
[177] 
Lin, H. The modified Mahalanobis Discriminant for predicting outer membrane proteins by using Chou’s pseudo amino acid composition. J. Theor. Biol.,  2008, 252(2), 350-356.
[http://dx.doi.org/10.1016/j.jtbi.2008.02.004] [PMID:  18355838] 
[178] 
Fang, Y.; Guo, Y.; Feng, Y.; Li, M. Predicting DNA-binding proteins: approached from Chou’s pseudo amino acid composition and other specific sequence features. Amino Acids,  2008, 34(1), 103-109.
[http://dx.doi.org/10.1007/s00726-007-0568-2] [PMID:  17624492] 
[179] 
Zhang, G.Y.; Li, H.C.; Gao, J.Q.; Fang, B.S. Predicting lipase types by improved Chou’s pseudo-amino acid composition. Protein Pept. Lett.,  2008, 15(10), 1132-1137.
[http://dx.doi.org/10.2174/092986608786071184] [PMID:  19075826] 
[180] 
Li, F.M.; Li, Q.Z. Predicting protein subcellular location using Chou’s pseudo amino acid composition and improved hybrid approach. Protein Pept. Lett.,  2008, 15(6), 612-616.
[http://dx.doi.org/10.2174/092986608784966930] [PMID:  18680458] 
[181] 
Jiang, X.; Wei, R.; Zhao, Y.; Zhang, T. Using Chou’s pseudo amino acid composition based on approximate entropy and an ensemble of AdaBoost classifiers to predict protein subnuclear location. Amino Acids,  2008, 34(4), 669-675.
[http://dx.doi.org/10.1007/s00726-008-0034-9] [PMID:  18256886] 
[182] 
Zhang, S.W.; Chen, W.; Yang, F.; Pan, Q. Using Chou’s pseudo amino acid composition to predict protein quaternary structure: a sequence-segmented PseAAC approach. Amino Acids,  2008, 35(3), 591-598.
[http://dx.doi.org/10.1007/s00726-008-0086-x] [PMID:  18427713] 
[183] 
Georgiou, D.N.; Karakasidis, T.E.; Nieto, J.J.; Torres, A. Use of fuzzy clustering technique and matrices to classify amino acids and its impact to Chou’s pseudo amino acid composition. J. Theor. Biol.,  2009, 257(1), 17-26.
[http://dx.doi.org/10.1016/j.jtbi.2008.11.003] [PMID:  19056401] 
[184] 
Qiu, J.D.; Huang, J.H.; Liang, R.P.; Lu, X.Q. Prediction of G-protein-coupled receptor classes based on the concept of Chou’s pseudo amino acid composition: an approach from discrete wavelet transform. Anal. Biochem.,  2009, 390(1), 68-73.
[http://dx.doi.org/10.1016/j.ab.2009.04.009] [PMID:  19364489] 
[185] 
Zeng, Y.H.; Guo, Y.Z.; Xiao, R.Q.; Yang, L.; Yu, L.Z.; Li, M.L. Using the augmented Chou’s pseudo amino acid composition for predicting protein submitochondria locations based on auto covariance approach. J. Theor. Biol.,  2009, 259(2), 366-372.
[http://dx.doi.org/10.1016/j.jtbi.2009.03.028] [PMID:  19341746] 
[186] 
Ding, H.; Luo, L.; Lin, H. Prediction of cell wall lytic enzymes using Chou’s amphiphilic pseudo amino acid composition. Protein Pept. Lett.,  2009, 16(4), 351-355.
[http://dx.doi.org/10.2174/092986609787848045] [PMID:  19356130] 
[187] 
Chou, K.C. Pseudo amino acid composition and its applications in bioinformatics, proteomics and system biology. Curr. Proteomics,  2009, 6, 262-274.
[http://dx.doi.org/10.2174/157016409789973707] 
[188] 
Lin, H.; Wang, H.; Ding, H.; Chen, Y.L.; Li, Q.Z. Prediction of subcellular localization of apoptosis protein using Chou’s pseudo amino acid composition. Acta Biotheor.,  2009, 57(3), 321-330.
[http://dx.doi.org/10.1007/s10441-008-9067-4] [PMID:  19169652] 
[189] 
Sahu, S.S.; Panda, G. A novel feature representation method based on Chou’s pseudo amino acid composition for protein structural class prediction. Comput. Biol. Chem.,  2010, 34(5-6), 320-327.
[http://dx.doi.org/10.1016/j.compbiolchem.2010.09.002] [PMID:  21106461] 
[190] 
Mohabatkar, H. Prediction of cyclin proteins using Chou’s pseudo amino acid composition. Protein Pept. Lett.,  2010, 17(10), 1207-1214.
[http://dx.doi.org/10.2174/092986610792231564] [PMID:  20450487] 
[191] 
Gu, Q.; Ding, Y.S.; Zhang, T.L. Prediction of G-protein-coupled receptor classes in low homology using Chou’s pseudo amino acid composition with approximate entropy and hydrophobicity patterns. Protein Pept. Lett.,  2010, 17(5), 559-567.
[http://dx.doi.org/10.2174/092986610791112693] [PMID:  19594431] 
[192] 
Yu, L.; Guo, Y.; Li, Y.; Li, G.; Li, M.; Luo, J.; Xiong, W.; Qin, W.; Secret, P. SecretP: identifying bacterial secreted proteins by fusing new features into Chou’s pseudo-amino acid composition. J. Theor. Biol.,  2010, 267(1), 1-6.
[http://dx.doi.org/10.1016/j.jtbi.2010.08.001] [PMID:  20691704] 
[193] 
Esmaeili, M.; Mohabatkar, H.; Mohsenzadeh, S. Using the concept of Chou’s pseudo amino acid composition for risk type prediction of human papillomaviruses. J. Theor. Biol.,  2010, 263(2), 203-209.
[http://dx.doi.org/10.1016/j.jtbi.2009.11.016] [PMID:  19961864] 
[194] 
Qiu, J.D.; Huang, J.H.; Shi, S.P.; Liang, R.P. Using the concept of Chou’s pseudo amino acid composition to predict enzyme family classes: an approach with support vector machine based on discrete wavelet transform. Protein Pept. Lett.,  2010, 17(6), 715-722.
[http://dx.doi.org/10.2174/092986610791190372] [PMID:  19961429] 
[195] 
Mohabatkar, H.; Mohammad Beigi, M.; Esmaeili, A. Prediction of GABAA receptor proteins using the concept of Chou’s pseudo-amino acid composition and support vector machine. J. Theor. Biol.,  2011, 281(1), 18-23.
[http://dx.doi.org/10.1016/j.jtbi.2011.04.017] [PMID:  21536049] 
[196] 
Guo, J.; Rao, N.; Liu, G.; Yang, Y.; Wang, G. Predicting protein folding rates using the concept of Chou’s pseudo amino acid composition. J. Comput. Chem.,  2011, 32(8), 1612-1617.
[http://dx.doi.org/10.1002/jcc.21740] [PMID:  21328402] 
[197] 
Lin, J.; Wang, Y. Using a novel AdaBoost algorithm and Chou’s Pseudo amino acid composition for predicting protein subcellular localization. Protein Pept. Lett.,  2011, 18(12), 1219-1225.
[http://dx.doi.org/10.2174/092986611797642797] [PMID:  21728988] 
[198] 
Mohammad Beigi, M.; Behjati, M.; Mohabatkar, H. Prediction of metalloproteinase family based on the concept of Chou’s pseudo amino acid composition using a machine learning approach. J. Struct. Funct. Genomics,  2011, 12(4), 191-197.
[http://dx.doi.org/10.1007/s10969-011-9120-4] [PMID:  22143437] 
[199] 
Zou, D.; He, Z.; He, J.; Xia, Y. Supersecondary structure prediction using Chou’s pseudo amino acid composition. J. Comput. Chem.,  2011, 32(2), 271-278.
[http://dx.doi.org/10.1002/jcc.21616] [PMID:  20652881] 
[200] 
Qiu, J.D.; Suo, S.B.; Sun, X.Y.; Shi, S.P.; Liang, R.P. OligoPred: a web-server for predicting homo-oligomeric proteins by incorporating discrete wavelet transform into Chou’s pseudo amino acid composition. J. Mol. Graph. Model.,  2011, 30, 129-134.
[http://dx.doi.org/10.1016/j.jmgm.2011.06.014] [PMID:  21802968] 
[201] 
Nanni, L.; Lumini, A.; Gupta, D.; Garg, A. Identifying bacterial virulent proteins by fusing a set of classifiers based on variants of Chou’s pseudo amino acid composition and on evolutionary information. IEEE/ACM Trans. Comput. Biol. Bioinformatics,  2012, 9(2), 467-475.
[http://dx.doi.org/10.1109/TCBB.2011.117] [PMID:  21860064] 
[202] 
Hayat, M.; Khan, A. Discriminating outer membrane proteins with Fuzzy K-nearest Neighbor algorithms based on the general form of Chou’s PseAAC. Protein Pept. Lett.,  2012, 19(4), 411-421.
[http://dx.doi.org/10.2174/092986612799789387] [PMID:  22185508] 
[203] 
Chen, C.; Shen, Z.B.; Zou, X.Y. Dual-layer wavelet SVM for predicting protein structural class via the general form of Chou’s pseudo amino acid composition. Protein Pept. Lett.,  2012, 19(4), 422-429.
[http://dx.doi.org/10.2174/092986612799789332] [PMID:  22185506] 
[204] 
Zia-Ur-Rehman. Khan, A. 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 Pept. Lett.,  2012, 19(8), 890-903.
[http://dx.doi.org/10.2174/092986612801619589] [PMID:  22316312] 
[205] 
Sun, X.Y.; Shi, S.P.; Qiu, J.D.; Suo, S.B.; Huang, S.Y.; Liang, R.P. Identifying protein quaternary structural attributes by incorporating physicochemical properties into the general form of Chou’s PseAAC via discrete wavelet transform. Mol. Biosyst.,  2012, 8(12), 3178-3184.
[http://dx.doi.org/10.1039/c2mb25280e] [PMID:  22990717] 
[206] 
Fan, G.L.; Li, Q.Z. Predict mycobacterial proteins subcellular locations by incorporating pseudo-average chemical shift into the general form of Chou’s pseudo amino acid composition. J. Theor. Biol.,  2012, 304, 88-95.
[http://dx.doi.org/10.1016/j.jtbi.2012.03.017] [PMID:  22459701] 
[207] 
Nanni, L.; Brahnam, S.; Lumini, A. Wavelet images and Chou’s pseudo amino acid composition for protein classification. Amino Acids,  2012, 43(2), 657-665.
[http://dx.doi.org/10.1007/s00726-011-1114-9] [PMID:  21993538] 
[208] 
Cao, J.Z.; Liu, W.Q.; Gu, H. Predicting viral protein subcellular localization with Chou’s pseudo amino acid composition and imbalance-weighted multi-label K-nearest neighbor algorithm. Protein Pept. Lett.,  2012, 19(11), 1163-1169.
[http://dx.doi.org/10.2174/092986612803216999] [PMID:  22185509] 
[209] 
Niu, X.H.; Hu, X.H.; Shi, F.; Xia, J.B. Predicting protein solubility by the general form of Chou’s pseudo amino acid composition: approached from chaos game representation and fractal dimension. Protein Pept. Lett.,  2012, 19(9), 940-948.
[http://dx.doi.org/10.2174/092986612802084492] [PMID:  22486614] 
[210] 
Gupta, M.K.; Niyogi, R.; Misra, M. An alignment-free method to find similarity among protein sequences via the general form of Chou’s pseudo amino acid composition. SAR QSAR Environ. Res.,  2013, 24(7), 597-609.
[http://dx.doi.org/10.1080/1062936X.2013.773378] [PMID:  23710804] 
[211] 
Fan, G.L.; Li, Q.Z. Discriminating bioluminescent proteins by incorporating average chemical shift and evolutionary information into the general form of Chou’s pseudo amino acid composition. J. Theor. Biol.,  2013, 334, 45-51.
[http://dx.doi.org/10.1016/j.jtbi.2013.06.003] [PMID:  23770403] 
[212] 
Wan, S.; Mak, M.W.; Kung, S.Y. GOASVM: a subcellular location predictor by incorporating term-frequency gene ontology into the general form of Chou’s pseudo-amino acid composition. J. Theor. Biol.,  2013, 323, 40-48.
[http://dx.doi.org/10.1016/j.jtbi.2013.01.012] [PMID:  23376577] 
[213] 
Qin, Y.F.; Zheng, L.; Huang, J. Locating apoptosis proteins by incorporating the signal peptide cleavage sites into the general form of Chou’s Pseudo amino acid composition. Int. J. Quantum Chem.,  2013, 113, 1660-1667.
[http://dx.doi.org/10.1002/qua.24383] 
[214] 
Huang, C.; Yuan, J.Q. A multilabel model based on Chou’s pseudo-amino acid composition for identifying membrane proteins with both single and multiple functional types. J. Membr. Biol.,  2013, 246(4), 327-334.
[http://dx.doi.org/10.1007/s00232-013-9536-9] [PMID:  23546013] 
[215] 
Khosravian, M.; Faramarzi, F.K.; Beigi, M.M.; Behbahani, M.; Mohabatkar, H. Predicting antibacterial peptides by the concept of Chou’s pseudo-amino acid composition and machine learning methods. Protein Pept. Lett.,  2013, 20(2), 180-186.
[http://dx.doi.org/10.2174/092986613804725307] [PMID:  22894156] 
[216] 
Chen, Y.K.; Li, K.B. Predicting membrane protein types by incorporating protein topology, domains, signal peptides, and physicochemical properties into the general form of Chou’s pseudo amino acid composition. J. Theor. Biol.,  2013, 318, 1-12.
[http://dx.doi.org/10.1016/j.jtbi.2012.10.033] [PMID:  23137835] 
[217] 
Lin, H.; Ding, C.; Yuan, L-F.; Chen, W.; Ding, H.; Li, Z-Q.; Guo, F-B.; Huang, J.; Rao, N-N. Predicting subchloroplast locations of proteins based on the general form of Chou’s pseudo amino acid composition: Approached from optimal tripeptide composition. Int. J. Biomath., 2013.61350003
[http://dx.doi.org/10.1142/S1793524513500034] 
[218] 
Mohabatkar, H.; Beigi, M.M.; Abdolahi, K.; Mohsenzadeh, S. Prediction of allergenic proteins by means of the concept of Chou’s pseudo amino acid composition and a machine learning approach. Med. Chem.,  2013, 9(1), 133-137.
[http://dx.doi.org/10.2174/157340613804488341] [PMID:  22931491] 
[219] 
Sarangi, A.N.; Lohani, M.; Aggarwal, R. Prediction of essential proteins in prokaryotes by incorporating various physico-chemical features into the general form of Chou’s pseudo amino acid composition. Protein Pept. Lett.,  2013, 20(7), 781-795.
[http://dx.doi.org/10.2174/0929866511320070008] [PMID:  23276224] 
[220] 
Georgiou, D.N.; Karakasidis, T.E.; Megaritis, A.C. A short survey on genetic sequences, Chou’s pseudo amino acid composition and its combination with fuzzy set theory. Open Bioinform. J.,  2013, 7, 41-48.
[http://dx.doi.org/10.2174/1875036201307010041] 
[221] 
Hajisharifi, Z.; Piryaiee, M.; Mohammad Beigi, M.; Behbahani, M.; Mohabatkar, H. Predicting anticancer peptides with Chou’s pseudo amino acid composition and investigating their mutagenicity via Ames test. J. Theor. Biol.,  2014, 341, 34-40.
[http://dx.doi.org/10.1016/j.jtbi.2013.08.037] [PMID:  24035842] 
[222] 
Mondal, S.; Pai, P.P. Chou’s pseudo amino acid composition improves sequence-based antifreeze protein prediction. J. Theor. Biol.,  2014, 356, 30-35.
[http://dx.doi.org/10.1016/j.jtbi.2014.04.006] [PMID:  24732262] 
[223] 
Zuo, Y.C.; Peng, Y.; Liu, L.; Chen, W.; Yang, L.; Fan, G.L. Predicting peroxidase subcellular location by hybridizing different descriptors of Chou’ pseudo amino acid patterns. Anal. Biochem.,  2014, 458, 14-19.
[http://dx.doi.org/10.1016/j.ab.2014.04.032] [PMID:  24802134] 
[224] 
Li, L.; Yu, S.; Xiao, W.; Li, Y.; Li, M.; Huang, L.; Zheng, X.; Zhou, S.; Yang, H. Prediction of bacterial protein subcellular localization by incorporating various features into Chou’s PseAAC and a backward feature selection approach. Biochimie,  2014, 104, 100-107.
[http://dx.doi.org/10.1016/j.biochi.2014.06.001] [PMID:  24929100] 
[225] 
Jia, C.; Lin, X.; Wang, Z. Prediction of protein S-nitrosylation sites based on adapted normal distribution bi-profile Bayes and Chou’s pseudo amino acid composition. Int. J. Mol. Sci.,  2014, 15(6), 10410-10423.
[http://dx.doi.org/10.3390/ijms150610410] [PMID:  24918295] 
[226] 
Nanni, L.; Brahnam, S.; Lumini, A. Prediction of protein structure classes by incorporating different protein descriptors into general Chou’s pseudo amino acid composition. J. Theor. Biol.,  2014, 360, 109-116.
[http://dx.doi.org/10.1016/j.jtbi.2014.07.003] [PMID:  25026218] 
[227] 
Hayat, M.; Iqbal, N. Discriminating protein structure classes by incorporating Pseudo Average Chemical Shift to Chou’s general PseAAC and Support Vector Machine. Comput. Methods Programs Biomed.,  2014, 116(3), 184-192.
[http://dx.doi.org/10.1016/j.cmpb.2014.06.007] [PMID:  24997484] 
[228] 
Han, G.S.; Yu, Z.G.; Anh, V. A two-stage SVM method to predict membrane protein types by incorporating amino acid classifications and physicochemical properties into a general form of Chou’s PseAAC. J. Theor. Biol.,  2014, 344, 31-39.
[http://dx.doi.org/10.1016/j.jtbi.2013.11.017] [PMID:  24316387] 
[229] 
Khan, Z.U.; Hayat, M.; Khan, M.A. Discrimination of acidic and alkaline enzyme using Chou’s pseudo amino acid composition in conjunction with probabilistic neural network model. J. Theor. Biol.,  2015, 365, 197-203.
[http://dx.doi.org/10.1016/j.jtbi.2014.10.014] [PMID:  25452135] 
[230] 
Liu, B.; Xu, J.; Fan, S.; Xu, R.; Zhou, J.; Wang, X.J.; Wang, X. PseDNA-Pro: DNA-binding protein identification by combining Chou’s PseAAC and physicochemical distance transformation. Mol. Inform.,  2015, 34(1), 8-17.
[http://dx.doi.org/10.1002/minf.201400025] [PMID:  27490858] 
[231] 
Kumar, R.; Srivastava, A.; Kumari, B.; Kumar, M. Prediction of β-lactamase and its class by Chou’s pseudo-amino acid composition and support vector machine. J. Theor. Biol.,  2015, 365, 96-103.
[http://dx.doi.org/10.1016/j.jtbi.2014.10.008] [PMID:  25454009] 
[232] 
Liu, B.; Chen, J.; Wang, X. Protein remote homology detection by combining Chou’s distance-pair pseudo amino acid composition and principal component analysis. Mol. Genet. Genomics,  2015, 290(5), 1919-1931.
[http://dx.doi.org/10.1007/s00438-015-1044-4] [PMID:  25896721] 
[233] 
Zhang, M.; Zhao, B.; Liu, X. Predicting industrial polymer melt index via incorporating chaotic characters into Chou’s general PseAAC. Chemom. Intell. Lab. Syst.,  2015, 146, 232-240. [CHEMOLAB]
[http://dx.doi.org/10.1016/j.chemolab.2015.05.028] 
[234] 
Sharma, R.; Dehzangi, A.; Lyons, J.; Paliwal, K.; Tsunoda, T.; Sharma, A. Predict Gram-Positive and Gram-Negative Subcellular Localization via Incorporating Evolutionary Information and Physicochemical Features Into Chou’s General PseAAC. IEEE Trans. Nanobioscience,  2015, 14(8), 915-926.
[http://dx.doi.org/10.1109/TNB.2015.2500186] [PMID:  26584499] 
[235] 
Sanchez, V.; Peinado, A.M.; Pérez-Córdoba, J.L.; Gómez, A.M. A new signal characterization and signal-based Chou’s PseAAC representation of protein sequences. J. Bioinform. Comput. Biol.,  2015, 13(5)1550024
[http://dx.doi.org/10.1142/S0219720015500249] [PMID:  26434573] 
[236] 
Wang, X.; Zhang, W.; Zhang, Q.; Li, G.Z. MultiP-SChlo: multi-label protein subchloroplast localization prediction with Chou’s pseudo amino acid composition and a novel multi-label classifier. Bioinformatics,  2015, 31(16), 2639-2645.
[http://dx.doi.org/10.1093/bioinformatics/btv212] [PMID:  25900916] 
[237] 
Ahmad, S.; Kabir, M.; Hayat, M. Identification of Heat Shock Protein families and J-protein types by incorporating Dipeptide Composition into Chou’s general PseAAC. Comput. Methods Programs Biomed.,  2015, 122(2), 165-174.
[http://dx.doi.org/10.1016/j.cmpb.2015.07.005] [PMID:  26233307] 
[238] 
Dehzangi, A.; Heffernan, R.; Sharma, A.; Lyons, J.; Paliwal, K.; Sattar, A. Gram-positive and Gram-negative protein subcellular localization by incorporating evolutionary-based descriptors into Chou׳s general PseAAC. J. Theor. Biol.,  2015, 364, 284-294.
[http://dx.doi.org/10.1016/j.jtbi.2014.09.029] [PMID:  25264267] 
[239] 
Fan, G.L.; Zhang, X.Y.; Liu, Y.L.; Nang, Y.; Wang, H. DSPMP: Discriminating secretory proteins of malaria parasite by hybridizing different descriptors of Chou’s pseudo amino acid patterns. J. Comput. Chem.,  2015, 36(31), 2317-2327.
[http://dx.doi.org/10.1002/jcc.24210] [PMID:  26484844] 
[240] 
Ali, F.; Hayat, M. Classification of membrane protein types using Voting Feature Interval in combination with Chou’s Pseudo Amino Acid Composition. J. Theor. Biol.,  2015, 384, 78-83.
[http://dx.doi.org/10.1016/j.jtbi.2015.07.034] [PMID:  26297889] 
[241] 
Zhang, S.L. Accurate prediction of protein structural classes by incorporating PSSS and PSSM into Chou’s general PseAAC. Chemom. Intell. Lab. Syst.,  2015, 142, 28-35. [CHEMOLAB]
[http://dx.doi.org/10.1016/j.chemolab.2015.01.004] 
[242] 
Ahmad, K.; Waris, M.; Hayat, M. Prediction of Protein Submitochondrial Locations by Incorporating Dipeptide Composition into Chou’s General Pseudo Amino Acid Composition. J. Membr. Biol.,  2016, 249(3), 293-304.
[http://dx.doi.org/10.1007/s00232-015-9868-8] [PMID:  26746980] 
[243] 
Behbahani, M.; Mohabatkar, H.; Nosrati, M. Analysis and comparison of lignin peroxidases between fungi and bacteria using three different modes of Chou’s general pseudo amino acid composition. J. Theor. Biol.,  2016, 411, 1-5.
[http://dx.doi.org/10.1016/j.jtbi.2016.09.001] [PMID:  27615149] 
[244] 
Fan, G.L.; Liu, Y.L.; Wang, H. Identification of thermophilic proteins by incorporating evolutionary and acid dissociation information into Chou’s general pseudo amino acid composition. J. Theor. Biol.,  2016, 407, 138-142.
[http://dx.doi.org/10.1016/j.jtbi.2016.07.010] [PMID:  27396359] 
[245] 
Jiao, Y.S.; Du, P.F. Prediction of Golgi-resident protein types using general form of Chou’s pseudo-amino acid compositions: Approaches with minimal redundancy maximal relevance feature selection. J. Theor. Biol.,  2016, 402, 38-44.
[http://dx.doi.org/10.1016/j.jtbi.2016.04.032] [PMID:  27155042] 
[246] 
Ju, Z.; Cao, J.Z.; Gu, H. Predicting lysine phosphoglycerylation with fuzzy SVM by incorporating k-spaced amino acid pairs into Chou׳s general PseAAC. J. Theor. Biol.,  2016, 397, 145-150.
[http://dx.doi.org/10.1016/j.jtbi.2016.02.020] [PMID:  26908349] 
[247] 
Kabir, M.; Hayat, M. iRSpot-GAEnsC: identifing recombination spots via ensemble classifier and extending the concept of Chou’s PseAAC to formulate DNA samples. Mol. Genet. Genomics,  2016, 291(1), 285-296.
[http://dx.doi.org/10.1007/s00438-015-1108-5] [PMID:  26319782] 
[248] 
Tahir, M.; Hayat, M. iNuc-STNC: a sequence-based predictor for identification of nucleosome positioning in genomes by extending the concept of SAAC and Chou’s PseAAC. Mol. Biosyst.,  2016, 12(8), 2587-2593.
[http://dx.doi.org/10.1039/C6MB00221H] [PMID:  27271822] 
[249] 
Tang, H.; Chen, W.; Lin, H. Identification of immunoglobulins using Chou’s pseudo amino acid composition with feature selection technique. Mol. Biosyst.,  2016, 12(4), 1269-1275.
[http://dx.doi.org/10.1039/C5MB00883B] [PMID:  26883492] 
[250] 
Tiwari, A.K. Prediction of G-protein coupled receptors and their subfamilies by incorporating various sequence features into Chou’s general PseAAC. Comput. Methods Programs Biomed.,  2016, 134, 197-213.
[http://dx.doi.org/10.1016/j.cmpb.2016.07.004] [PMID:  27480744] 
[251] 
Xu, C.; Sun, D.; Liu, S.; Zhang, Y. Protein sequence analysis by incorporating modified chaos game and physicochemical properties into Chou’s general pseudo amino acid composition. J. Theor. Biol.,  2016, 406, 105-115.
[http://dx.doi.org/10.1016/j.jtbi.2016.06.034] [PMID:  27375218] 
[252] 
Zou, H.L.; Xiao, X. Predicting the Functional Types of Singleplex and Multiplex Eukaryotic Membrane Proteins via Different Models of Chou’s Pseudo Amino Acid Compositions. J. Membr. Biol.,  2016, 249(1-2), 23-29.
[http://dx.doi.org/10.1007/s00232-015-9830-9] [PMID:  26458844] 
[253] 
Zou, H.L.; Xiao, X. Classifying Multifunctional Enzymes by Incorporating Three Different Models into Chou's General Pseudo Amino Acid Composition (doi:10.1007/s00232-016-9904-3). J. Membr. Biol,  2016, 249, 561-567.
[http://dx.doi.org/10.1007/s00232-016-9904-3] 
[254] 
Tripathi, P.; Pandey, P.N. A novel alignment-free method to classify protein folding types by combining spectral graph clustering with Chou’s pseudo amino acid composition. J. Theor. Biol.,  2017, 424, 49-54.
[http://dx.doi.org/10.1016/j.jtbi.2017.04.027] [PMID:  28476562] 
[255] 
Khan, M.; Hayat, M.; Khan, S.A.; Iqbal, N. Unb-DPC: Identify mycobacterial membrane protein types by incorporating un-biased dipeptide composition into Chou’s general PseAAC. J. Theor. Biol.,  2017, 415, 13-19.
[http://dx.doi.org/10.1016/j.jtbi.2016.12.004] [PMID:  27939596] 
[256] 
Tahir, M.; Hayat, M.; Kabir, M. Sequence based predictor for discrimination of enhancer and their types by applying general form of Chou’s trinucleotide composition. Comput. Methods Programs Biomed.,  2017, 146, 69-75.
[http://dx.doi.org/10.1016/j.cmpb.2017.05.008] [PMID:  28688491] 
[257] 
Xu, C.; Ge, L.; Zhang, Y.; Dehmer, M.; Gutman, I. Prediction of therapeutic peptides by incorporating q-Wiener index into Chou’s general PseAAC. J. Biomed. Inform.,  2017, 75, 63-69.
[http://dx.doi.org/10.1016/j.jbi.2017.09.011] [PMID:  28958485] 
[258] 
Yu, B.; Lou, L.; Li, S.; Zhang, Y.; Qiu, W.; Wu, X.; Wang, M.; Tian, B. Prediction of protein structural class for low-similarity sequences using Chou’s pseudo amino acid composition and wavelet denoising. J. Mol. Graph. Model.,  2017, 76, 260-273.
[http://dx.doi.org/10.1016/j.jmgm.2017.07.012] [PMID:  28743071] 
[259] 
Huo, H.; Li, T.; Wang, S.; Lv, Y.; Zuo, Y.; Yang, L. Prediction of presynaptic and postsynaptic neurotoxins by combining various Chou’s pseudo components. Sci. Rep.,  2017, 7(1), 5827.
[http://dx.doi.org/10.1038/s41598-017-06195-y] [PMID:  28724993] 
[260] 
Ju, Z.; He, J.J. Prediction of lysine crotonylation sites by incorporating the composition of k-spaced amino acid pairs into Chou’s general PseAAC. J. Mol. Graph. Model.,  2017, 77, 200-204.
[http://dx.doi.org/10.1016/j.jmgm.2017.08.020] [PMID:  28886434] 
[261] 
Jiao, Y.S.; Du, P.F. Predicting protein submitochondrial locations by incorporating the positional-specific physicochemical properties into Chou’s general pseudo-amino acid compositions. J. Theor. Biol.,  2017, 416, 81-87.
[http://dx.doi.org/10.1016/j.jtbi.2016.12.026] [PMID:  28077336] 
[262] 
Meher, P.K.; Sahu, T.K.; Saini, V.; Rao, A.R. Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou’s general PseAAC. Sci. Rep.,  2017, 7, 42362.
[http://dx.doi.org/10.1038/srep42362] [PMID:  28205576] 
[263] 
Liang, Y.; Zhang, S. Predict protein structural class by incorporating two different modes of evolutionary information into Chou’s general pseudo amino acid composition. J. Mol. Graph. Model.,  2017, 78, 110-117.
[http://dx.doi.org/10.1016/j.jmgm.2017.10.003] [PMID:  29055184] 
[264] 
Rahimi, M.; Bakhtiarizadeh, M.R.; Mohammadi-Sangcheshmeh, A. OOgenesis_Pred: A sequence-based method for predicting oogenesis proteins by six different modes of Chou’s pseudo amino acid composition. J. Theor. Biol.,  2017, 414, 128-136.
[http://dx.doi.org/10.1016/j.jtbi.2016.11.028] [PMID:  27916703] 
[265] 
Yu, B.; Li, S.; Qiu, W.Y.; Chen, C.; Chen, R.X.; Wang, L.; Wang, M.H.; Zhang, Y. Accurate prediction of subcellular location of apoptosis proteins combining Chou’s PseAAC and PsePSSM based on wavelet denoising. Oncotarget,  2017, 8(64), 107640-107665.
[http://dx.doi.org/10.18632/oncotarget.22585] [PMID:  29296195] 
[266] 
Akbar, S.; Hayat, M. iMethyl-STTNC: Identification of N6-methyladenosine sites by extending the idea of SAAC into Chou’s PseAAC to formulate RNA sequences. J. Theor. Biol.,  2018, 455, 205-211.
[http://dx.doi.org/10.1016/j.jtbi.2018.07.018] [PMID:  30031793] 
[267] 
Arif, M.; Hayat, M.; Jan, Z. iMem-2LSAAC: A two-level model for discrimination of membrane proteins and their types by extending the notion of SAAC into chou’s pseudo amino acid composition. J. Theor. Biol.,  2018, 442, 11-21.
[http://dx.doi.org/10.1016/j.jtbi.2018.01.008] [PMID:  29337263] 
[268] 
Contreras-Torres, E. Predicting structural classes of proteins by incorporating their global and local physicochemical and conformational properties into general Chou’s PseAAC. J. Theor. Biol.,  2018, 454, 139-145.
[http://dx.doi.org/10.1016/j.jtbi.2018.05.033] [PMID:  29870696] 
[269] 
Ju, Z.; Wang, S.Y. Prediction of citrullination sites by incorporating k-spaced amino acid pairs into Chou’s general pseudo amino acid composition. Gene,  2018, 664, 78-83.
[http://dx.doi.org/10.1016/j.gene.2018.04.055] [PMID:  29694908] 
[270] 
Muthu Krishnan, S. Using Chou’s general PseAAC to analyze the evolutionary relationship of receptor associated proteins (RAP) with various folding patterns of protein domains. J. Theor. Biol.,  2018, 445, 62-74.
[http://dx.doi.org/10.1016/j.jtbi.2018.02.008] [PMID:  29476832] 
[271] 
Liang, Y.; Zhang, S. Identify Gram-negative bacterial secreted protein types by incorporating different modes of PSSM into Chou’s general PseAAC via Kullback-Leibler divergence. J. Theor. Biol.,  2018, 454, 22-29.
[http://dx.doi.org/10.1016/j.jtbi.2018.05.035] [PMID:  29857085] 
[272] 
Mei, J.; Fu, Y.; Zhao, J. Analysis and prediction of ion channel inhibitors by using feature selection and Chou’s general pseudo amino acid composition. J. Theor. Biol.,  2018, 456, 41-48.
[http://dx.doi.org/10.1016/j.jtbi.2018.07.040] [PMID:  30075172] 
[273] 
Mei, J.; Zhao, J. Prediction of HIV-1 and HIV-2 proteins by using Chou’s pseudo amino acid compositions and different classifiers. Sci. Rep.,  2018, 8(1), 2359.
[http://dx.doi.org/10.1038/s41598-018-20819-x] [PMID:  29402983] 
[274] 
Mei, J.; Zhao, J. Analysis and prediction of presynaptic and postsynaptic neurotoxins by Chou’s general pseudo amino acid composition and motif features. J. Theor. Biol.,  2018, 447, 147-153.
[http://dx.doi.org/10.1016/j.jtbi.2018.03.034] [PMID:  29596863] 
[275] 
Mousavizadegan, M.; Mohabatkar, H. Computational prediction of antifungal peptides via Chou’s PseAAC and SVM. J. Bioinform. Comput. Biol.,  2018, 16(4)1850016
[http://dx.doi.org/10.1142/S0219720018500166] [PMID:  30105927] 
[276] 
Qiu, W.; Li, S.; Cui, X.; Yu, Z.; Wang, M.; Du, J.; Peng, Y.; Yu, B. Predicting protein submitochondrial locations by incorporating the pseudo-position specific scoring matrix into the general Chou’s pseudo-amino acid composition. J. Theor. Biol.,  2018, 450, 86-103.
[http://dx.doi.org/10.1016/j.jtbi.2018.04.026] [PMID:  29678694] 
[277] 
Rahman, M.S.; Shatabda, S.; Saha, S.; Kaykobad, M.; Rahman, M.S. DPP-PseAAC: A DNA-binding protein prediction model using Chou’s general PseAAC. J. Theor. Biol.,  2018, 452, 22-34.
[http://dx.doi.org/10.1016/j.jtbi.2018.05.006] [PMID:  29753757] 
[278] 
Sankari, E.S.; Manimegalai, D. Predicting membrane protein types by incorporating a novel feature set into Chou’s general PseAAC. J. Theor. Biol.,  2018, 455, 319-328.
[http://dx.doi.org/10.1016/j.jtbi.2018.07.032] [PMID:  30056084] 
[279] 
Zhang, S.; Duan, X. Prediction of protein subcellular localization with oversampling approach and Chou’s general PseAAC. J. Theor. Biol.,  2018, 437, 239-250.
[http://dx.doi.org/10.1016/j.jtbi.2017.10.030] [PMID:  29100918] 
[280] 
Chou, K.C. An unprecedented revolution in medicinal chemistry driven by the progress of biological science. Curr. Top. Med. Chem.,  2017, 17(21), 2337-2358.
[http://dx.doi.org/10.2174/1568026617666170414145508] [PMID:  28413951] 
[281] 
Shen, H.B.; Chou, K.C. PseAAC: a flexible web server for generating various kinds of protein pseudo amino acid composition. Anal. Biochem.,  2008, 373(2), 386-388.
[http://dx.doi.org/10.1016/j.ab.2007.10.012] [PMID:  17976365] 
[282] 
Du, P.; Wang, X.; Xu, C.; Gao, Y. PseAAC-Builder: a cross-platform stand-alone program for generating various special Chou’s pseudo-amino acid compositions. Anal. Biochem.,  2012, 425(2), 117-119.
[http://dx.doi.org/10.1016/j.ab.2012.03.015] [PMID:  22459120] 
[283] 
Cao, D.S.; Xu, Q.S.; Liang, Y.Z. propy: a tool to generate various modes of Chou’s PseAAC. Bioinformatics,  2013, 29(7), 960-962.
[http://dx.doi.org/10.1093/bioinformatics/btt072] [PMID:  23426256] 
[284] 
Du, P.; Gu, S.; Jiao, Y. PseAAC-General: fast building various modes of general form of Chou’s pseudo-amino acid composition for large-scale protein datasets. Int. J. Mol. Sci.,  2014, 15(3), 3495-3506.
[http://dx.doi.org/10.3390/ijms15033495] [PMID:  24577312] 
[285] 
Wang, J.; Yang, B.; Revote, J.; Leier, A.; Marquez-Lago, T.T.; Webb, G.; Song, J.; Chou, K.C.; Lithgow, T. POSSUM: a bioinformatics toolkit for generating numerical sequence feature descriptors based on PSSM profiles. Bioinformatics,  2017, 33(17), 2756-2758.
[http://dx.doi.org/10.1093/bioinformatics/btx302] [PMID:  28903538] 
[286] 
Chen, W.; Lei, T.Y.; Jin, D.C.; Lin, H.; Chou, K.C. PseKNC: a flexible web server for generating pseudo K-tuple nucleotide composition. Anal. Biochem.,  2014, 456, 53-60.
[http://dx.doi.org/10.1016/j.ab.2014.04.001] [PMID:  24732113] 
[287] 
Chen, W.; Lin, H.; Chou, K.C. Pseudo nucleotide composition or PseKNC: an effective formulation for analyzing genomic sequences. Mol. Biosyst.,  2015, 11(10), 2620-2634.
[http://dx.doi.org/10.1039/C5MB00155B] [PMID:  26099739] 
[288] 
Al Maruf, M.A.; Shatabda, S. iRSpot-SF: Prediction of recombination hotspots by incorporating sequence based features into Chou’s Pseudo components. Genomics,  2019, 111(4), 966-972.
[http://dx.doi.org/10.1016/j.ygeno.2018.06.003] [PMID:  29935224] 
[289] 
Sabooh, M.F.; Iqbal, N.; Khan, M.; Khan, M.; Maqbool, H.F. Identifying 5-methylcytosine sites in RNA sequence using composite encoding feature into Chou’s PseKNC. J. Theor. Biol.,  2018, 452, 1-9.
[http://dx.doi.org/10.1016/j.jtbi.2018.04.037] [PMID:  29727634] 
[290] 
Zhang, L.; Kong, L. iRSpot-ADPM: Identify recombination spots by incorporating the associated dinucleotide product model into Chou’s pseudo components. J. Theor. Biol.,  2018, 441, 1-8.
[http://dx.doi.org/10.1016/j.jtbi.2017.12.025] [PMID:  29305179] 
[291] 
Zhang, L.; Kong, L. iRSpot-PDI: Identification of recombination spots by incorporating dinucleotide property diversity information into Chou’s pseudo components. Genomics,  2019, 111(3), 457-464.
[http://dx.doi.org/10.1016/j.ygeno.2018.11.031] [PMID:  29548799] 
[292] 
Shen, H.B.; Chou, K.C. A top-down approach to enhance the power of predicting human protein subcellular localization: Hum-mPLoc 2.0. Anal. Biochem.,  2009, 394(2), 269-274.
[http://dx.doi.org/10.1016/j.ab.2009.07.046] [PMID:  19651102] 
[293] 
Shen, H.B.; Chou, K.C. Gpos-mPLoc: a top-down approach to improve the quality of predicting subcellular localization of Gram-positive bacterial proteins. Protein Pept. Lett.,  2009, 16(12), 1478-1484.
[http://dx.doi.org/10.2174/092986609789839322] [PMID:  20001911] 
[294] 
Chou, K.C.; Shen, H.B. Plant-mPLoc: a top-down strategy to augment the power for predicting plant protein subcellular localization. PLoS One,  2010, 5(6)e11335
[http://dx.doi.org/10.1371/journal.pone.0011335] [PMID:  20596258] 
[295] 
Shen, H.B.; Chou, K.C. Gneg-mPLoc: a top-down strategy to enhance the quality of predicting subcellular localization of Gram-negative bacterial proteins. J. Theor. Biol.,  2010, 264(2), 326-333.
[http://dx.doi.org/10.1016/j.jtbi.2010.01.018] [PMID:  20093124] 
[296] 
Lin, W.Z.; Fang, J.A.; Xiao, X.; Chou, K.C. iLoc-Animal: a multi-label learning classifier for predicting subcellular localization of animal proteins. Mol. Biosyst.,  2013, 9(4), 634-644.
[http://dx.doi.org/10.1039/c3mb25466f] [PMID:  23370050] 
[297] 
Cheng, X.; Xiao, X. pLoc-mVirus: predict subcellular localization
of multi-location virus proteins via incorporating the optimal GO information into general PseAAC. Gene (Erratum:
ibid., 2018, Vol.644, 156-156),  2017, 628, 315-321.
[298] 
Xiao, X.; Cheng, X.; Su, S.; Nao, Q. pLoc-mGpos: Incorporate key gene ontology information into general PseAAC for predicting subcellular localization of Gram-positive bacterial proteins. Nat. Sci.,  2017, 9, 331-349.
[http://dx.doi.org/10.4236/ns.2017.99032] 
[299] 
Chou, K.C. Some remarks on predicting multi-label attributes in molecular biosystems. Mol. Biosyst.,  2013, 9(6), 1092-1100.
[http://dx.doi.org/10.1039/c3mb25555g] [PMID:  23536215] 
[300] 
Shen, H.; Chou, K.C. Using optimized evidence-theoretic K-nearest neighbor classifier and pseudo-amino acid composition to predict membrane protein types. Biochem. Biophys. Res. Commun.,  2005, 334(1), 288-292. [BBRC]
[http://dx.doi.org/10.1016/j.bbrc.2005.06.087] [PMID:  16002049] 
[301] 
Chou, K.C. Using subsite coupling to predict signal peptides. Protein Eng.,  2001, 14(2), 75-79.
[http://dx.doi.org/10.1093/protein/14.2.75] [PMID:  11297664] 
[302] 
Chou, K.C. Prediction of signal peptides using scaled window. Peptides,  2001, 22(12), 1973-1979.
[http://dx.doi.org/10.1016/S0196-9781(01)00540-X] [PMID:  11786179] 
[303] 
Chen, W.; Feng, P.; Ding, H.; Lin, H.; Chou, K.C. Using deformation energy to analyze nucleosome positioning in genomes. Genomics,  2016, 107(2-3), 69-75.
[http://dx.doi.org/10.1016/j.ygeno.2015.12.005] [PMID:  26724497] 
[304] 
Chou, K.C.; Zhang, C.T. Prediction of protein structural classes. Crit. Rev. Biochem. Mol. Biol.,  1995, 30(4), 275-349.
[http://dx.doi.org/10.3109/10409239509083488] [PMID:  7587280] 
[305] 
Chou, K.C.; Shen, H.B. Recent advances in developing web-servers for predicting protein attributes. Nat. Sci.,  2009, 1, 63-92.
[http://dx.doi.org/10.4236/ns.2009.12011] 
[306] 
Liu, B.; Wu, H.; Zhang, D.; Wang, X.; Chou, K.C. Pse-Analysis: a python package for DNA/RNA and protein/peptide sequence analysis based on pseudo components and kernel methods. Oncotarget,  2017, 8(8), 13338-13343.
[http://dx.doi.org/10.18632/oncotarget.14524] [PMID:  28076851] 
[307] 
Wang, J.; Yang, B.; Leier, A.; Marquez-Lago, T.T.; Hayashida, M.; Rocker, A.; Zhang, Y.; Akutsu, T.; Chou, K.C.; Strugnell, R.A.; Song, J.; Lithgow, T. Bastion6: a bioinformatics approach for accurate prediction of type VI secreted effectors. Bioinformatics,  2018, 34(15), 2546-2555.
[http://dx.doi.org/10.1093/bioinformatics/bty155] [PMID:  29547915] 
[308] 
Chen, Z.; Zhao, P.; Li, F.; Leier, A.; Marquez-Lago, T.T.; Wang, Y.; Webb, G.I.; Smith, A.I.; Daly, R.J.; Chou, K.C.; Song, J. iFeature: a Python package and web server for features extraction and selection from protein and peptide sequences. Bioinformatics,  2018, 34(14), 2499-2502.
[http://dx.doi.org/10.1093/bioinformatics/bty140] [PMID:  29528364] 
[309] 
Song, J.; Li, F.; Leier, A.; Marquez-Lago, T.T.; Akutsu, T.; Haffari, G.; Chou, K.C.; Webb, G.I.; Pike, R.N.; Hancock, J. PROSPERous: high-throughput prediction of substrate cleavage sites for 90 proteases with improved accuracy. Bioinformatics,  2018, 34(4), 684-687.
[http://dx.doi.org/10.1093/bioinformatics/btx670] [PMID:  29069280] 
[310] 
Lu, J.J.; Pan, W.; Hu, Y.J.; Wang, Y.T. Multi-target drugs: the trend of drug research and development. PLoS One,  2012, 7(6)e40262
[http://dx.doi.org/10.1371/journal.pone.0040262] [PMID:  22768266] 
[311] 
Xiao, X.; Cheng, X.; Chen, G.; Mao, Q. pLoc_bal-mGpos: predict subcellular localization of Gram-positive bacterial proteins by quasi-balancing training dataset and PseAAC. Genomics, 2018.
[http://dx.doi.org/10.1016/j.ygeno.2018.05.017] [PMID:  29842950] 
[312] 
Cheng, X.; Lin, W.Z.; Xiao, X.; Chou, K.C. pLoc_bal-mAnimal: predict subcellular localization of animal proteins by balancing training dataset and PseAAC. Bioinformatics,  2019, 35(3), 398-406.
[http://dx.doi.org/10.1093/bioinformatics/bty628] [PMID:  30010789] 
[313] 
Chou, K.C.; Cheng, X.; Xiao, X. pLoc_bal-mHum: Predict
subcellular localization of human proteins by PseAAC and
quasi-balancing training dataset. Genomics, 2018. S0888-
7543(18), 30276-3.
[http://dx.doi.org/10.1016/j.ygeno.2018.08.007] [PMID: 30179658] 
[314] 
(a)Cheng, X.; Xiao, X.; Chou, K.C. pLoc_bal-mGneg: Predict subcellular localization of Gram-negative bacterial proteins by quasi-balancing training dataset and general PseAAC. J. Theor. Biol.,  2018, 458, 92-102.
[http://dx.doi.org/10.1016/j.jtbi.2018.09.005] [PMID: 30201434] 
(b)Xiao, X.; Cheng, X.; Chen, G.; Mao, Q.; Chou, K.C. pLoc_bal-mVirus: predict subcellular localization of multi-label virus proteins by PseAAC and IHTS treatment to balance training dataset. Med. Chem.,  2019, 15(5), 496-509.
[http://dx.doi.org/10.2174/1573406415666181217114710] [PMID: 30556503] 
(c)Chou, K.C.; Cheng, X.; Xiao, X. pLoc_bal-mEuk: predict subcellular localization of eukaryotic proteins by general PseAAC and quasi-balancing training dataset. Med. Chem.,  2019, 15(5), 472-485.
[http://dx.doi.org/10.2174/1573406415666181218102517] [PMID: 30569871] 
(d)Xiao, X.; Cheng, X.; Chen, G.; Mao, Q. pLoc_bal-mGpos: predict subcellular localization of Gram-positive bacterial proteins by quasi-balancing training dataset and PseAAC. Genomics,  2019, 111(4), 886-892.
[http://dx.doi.org/10.1016/j.ygeno.2018.05.017] [PMID: 29842950] 
(e)Cheng, X.; Xiao, X.; Chou, K.C. pLoc_bal-mPlant: predict subcellular localization of plant proteins by general PseAAC and balancing training dataset. Curr. Pharm. Des.,  2018, 24(34), 4013-4022.
[http://dx.doi.org/10.2174/1381612824666181119145030] [PMID:  30451108] 
[315] 
Chou, K.C.; Jiang, S.P.; Liu, W.M.; Fee, C.H. Graph theory of enzyme kinetics: 1. Steady-state reaction system. Sci. Sin.,  1979, 22, 341-358.
[316] 
Chou, K.C.; Forsén, S. Graphical rules for enzyme-catalysed rate laws. Biochem. J.,  1980, 187(3), 829-835.
[http://dx.doi.org/10.1042/bj1870829] [PMID:  7188428] 
[317] 
Chou, K.C.; Forsen, S.; Zhou, G.Q. Three schematic rules for deriving apparent rate constants. Chem. Scr.,  1980, 16, 109-113.
[318] 
Chou, K.C.; Carter, R.E.; Forsen, S. A new graphical method for deriving rate equations for complicated mechanisms. Chem. Scr.,  1981, 18, 82-86.
[319] 
Chou, K.C.; Forsen, S. Graphical rules of steady-state reaction systems. Can. J. Chem.,  1981, 59, 737-755.
[http://dx.doi.org/10.1139/v81-107] 
[320] 
Zhou, G.P.; Deng, M.H. An extension of Chou’s graphic rules for deriving enzyme kinetic equations to systems involving parallel reaction pathways. Biochem. J.,  1984, 222(1), 169-176.
[http://dx.doi.org/10.1042/bj2220169] [PMID:  6477507] 
[321] 
Chou, K.C. Graphic rules in steady and non-steady state enzyme kinetics. J. Biol. Chem.,  1989, 264(20), 12074-12079.
[PMID:  2745429] 
[322] 
Althaus, I.W.; Chou, J.J.; Gonzales, A.J.; Deibel, M.R.; Chou, K.C.; Kezdy, F.J.; Romero, D.L.; Aristoff, P.A.; Tarpley, W.G.; Reusser, F. Steady-state kinetic studies with the non-nucleoside HIV-1 reverse transcriptase inhibitor U-87201E. J. Biol. Chem.,  1993, 268(9), 6119-6124.
[PMID:  7681060] 
[323] 
Chou, K.C. Applications of graph theory to enzyme kinetics and protein folding kinetics. Steady and non-steady-state systems. Biophys. Chem.,  1990, 35(1), 1-24.
[http://dx.doi.org/10.1016/0301-4622(90)80056-D] [PMID:  2183882] 
[324] 
Althaus, I.W.; Gonzales, A.J.; Chou, J.J.; Romero, D.L.; Deibel, M.R.; Chou, K.C.; Kezdy, F.J.; Resnick, L.; Busso, M.E.; So, A.G. The quinoline U-78036 is a potent inhibitor of HIV-1 reverse transcriptase. J. Biol. Chem.,  1993, 268(20), 14875-14880.
[PMID:  7686907] 
[325] 
Chou, K.C. Graphic rule for drug metabolism systems. Curr. Drug Metab.,  2010, 11(4), 369-378.
[http://dx.doi.org/10.2174/138920010791514261] [PMID:  20446902] 
[326] 
Zhou, G.P. The disposition of the LZCC protein residues in wenxiang diagram provides new insights into the protein-protein interaction mechanism. J. Theor. Biol.,  2011, 284(1), 142-148.
[http://dx.doi.org/10.1016/j.jtbi.2011.06.006] [PMID:  21718705] 
[327] 
Althaus, I.W.; Chou, J.J.; Gonzales, A.J.; Deibel, M.R.; Chou, K.C.; Kezdy, F.J.; Romero, D.L.; Palmer, J.R.; Thomas, R.C.; Aristoff, P.A. Kinetic studies with the non-nucleoside HIV-1 reverse transcriptase inhibitor U-88204E. Biochemistry,  1993, 32(26), 6548-6554.
[http://dx.doi.org/10.1021/bi00077a008] [PMID:  7687145] 
[328] 
Chou, K.C.; Lin, W.Z.; Xiao, X. Wenxiang: a web-server for drawing wenxiang diagrams. Nat. Sci.,  2011, 3, 862-865.
[http://dx.doi.org/10.4236/ns.2011.310111] 
[329] 
(a)Chou, K.C.; Forsén, S. Diffusion-controlled effects in reversible enzymatic fast reaction systems--critical spherical shell and proximity rate constant. Biophys. Chem.,  1980, 12(3-4), 255-263.
[http://dx.doi.org/10.1016/0301-4622(80)80002-0] [PMID: 7225518] 
(b)Li, T.T.; Chou, K.C.; Forsen, S. The flow of substrate molecules in fast enzyme-catalyzed reaction systems. Chem. Scr.,  1980, 16, 192-196.
[330] 
Chou, K.C.; Li, T.T.; Forsén, S. The critical spherical shell in enzymatic fast reaction systems. Biophys. Chem.,  1980, 12(3-4), 265-269.
[http://dx.doi.org/10.1016/0301-4622(80)80003-2] [PMID:  7225519] 
[331] 
Shen, H.B.; Song, J.N. Prediction of protein folding rates from primary sequence by fusing multiple sequential features. J. Biomed. Sci. Eng.,  2009, 2, 136-143. [JBiSE]
[http://dx.doi.org/10.4236/jbise.2009.23024] 
[332] 
Chou, K.C.; Chen, N.Y.; Forsen, S. The biological functions of low-frequency phonons: 2. Cooperative effects. Chem. Scr.,  1981, 18, 126-132.
[333] 
Chou, K.C. Low-frequency collective motion in biomacromolecules and its biological functions. Biophys. Chem.,  1988, 30(1), 3-48.
[http://dx.doi.org/10.1016/0301-4622(88)85002-6] [PMID:  3046672] 
Rights & Permissions Print Cite
Article Metrics
69
8
Journal Information
For Authors
For Editors
For Reviewers
Explore Articles
Open Access
Open Access Articles
For Visitors
DOI https://dx.doi.org/10.2174/0929867326666190507082559	Print ISSN 0929-8673
Publisher Name Bentham Science Publisher	Online ISSN 1875-533X
Current Medicinal Chemistry

Advances in Predicting Subcellular Localization of Multi-label Proteins and its Implication for Developing Multi-target Drugs

Abstract

Advances in Medicinal Chemistry: From Cancer to Chronic Diseases.

Approaches to the treatment of chronic inflammation

Cellular and Molecular Mechanisms of Non-Infectious Inflammatory Diseases: Focus on Clinical Implications

Chalcogen-modified nucleic acid analogues

Current Medicinal Chemistry

Advances in Predicting Subcellular Localization of Multi-label Proteins and its Implication for Developing Multi-target Drugs

Abstract

Call for Papers in Thematic Issues

Advances in Medicinal Chemistry: From Cancer to Chronic Diseases.

Approaches to the treatment of chronic inflammation

Cellular and Molecular Mechanisms of Non-Infectious Inflammatory Diseases: Focus on Clinical Implications

Chalcogen-modified nucleic acid analogues

Related Journals

Related Books
