Login Register Cart 0
Generic placeholder image

Current Genomics

Editor-in-Chief

ISSN (Print): 1389-2029
ISSN (Online): 1875-5488

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

iMethylK-PseAAC: Improving Accuracy of Lysine Methylation Sites Identification by Incorporating Statistical Moments and Position Relative Features into General PseAAC via Chou’s 5-steps Rule

Author(s): Sarah Ilyas, Waqar Hussain, Adeel Ashraf , Yaser Daanial Khan*, Sher Afzal Khan and Kuo- Chen Chou

Volume 20, Issue 4, 2019

Page: [275 - 292] Pages: 18

DOI: 10.2174/1389202920666190809095206

Price: $65

conference banner
Abstract

Background: Methylation is one of the most important post-translational modifications in the human body which usually arises on lysine amongthe most intensely modified residues. It performs a dynamic role in numerous biological procedures, such as regulation of gene expression, regulation of protein function and RNA processing. Therefore, to identify lysine methylation sites is an important challenge as some experimental procedures are time-consuming.

Objective: Herein, we propose a computational predictor named iMethylK-PseAAC to identify lysine methylation sites.

Methods: Firstly, we constructed feature vectors based on PseAAC using position and composition relative features and statistical moments. A neural network is trained based on the extracted features. The performance of the proposed method is then validated using cross-validation and jackknife testing.

Results: The objective evaluation of the predictor showed accuracy of 96.7% for self-consistency, 91.61% for 10-fold cross-validation and 93.42% for jackknife testing.

Conclusion: It is concluded that iMethylK-PseAAC outperforms the counterparts to identify lysine methylation sites such as iMethyl-PseACC, BPB-PPMS and PMeS.

Keywords: Methylation, lysine methylation, PseAAC, statistical moments, 5-steps rule, prediction.

« Previous Next »
Graphical Abstract

[1]
Paik, W.K.; Kim, S. Enzymatic methylation of protein fractions from calf thymus nuclei. Biochem. Biophys. Res. Commun., 1967, 29(1), 14-20.
[http://dx.doi.org/10.1016/0006-291X(67)90533-5] [PMID: 6055181]
[2]
Xu, Y.; Chou, K-C. Recent progress in predicting posttranslational modification sites in proteins. Curr. Top. Med. Chem., 2016, 16(6), 591-603.
[http://dx.doi.org/10.2174/1568026615666150819110421] [PMID: 26286211]
[3]
Clarke, S. Protein methylation. Curr. Opin. Cell Biol., 1993, 5(6), 977-983.
[http://dx.doi.org/10.1016/0955-0674(93)90080-A] [PMID: 8129951]
[4]
Schubert, H.L.; Blumenthal, R.M.; Cheng, X. 1 Protein methyltransferases: Their distribution among the five structural classes of AdoMet-dependent methyltransferases. The Enzymes; Elsevier, 2006, Vol. 24, pp. 3-28.
[5]
Grewal, S.I.; Rice, J.C. Regulation of heterochromatin by histone methylation and small RNAs. Curr. Opin. Cell Biol., 2004, 16(3), 230-238.
[http://dx.doi.org/10.1016/j.ceb.2004.04.002] [PMID: 15145346]
[6]
Lee, D.Y.; Teyssier, C.; Strahl, B.D.; Stallcup, M.R. Role of protein methylation in regulation of transcription. Endocr. Rev., 2005, 26(2), 147-170.
[http://dx.doi.org/10.1210/er.2004-0008] [PMID: 15479858]
[7]
Chen, X.; Niroomand, F.; Liu, Z.; Zankl, A.; Katus, H.A.; Jahn, L.; Tiefenbacher, C.P. Expression of nitric oxide related enzymes in coronary heart disease. Basic Res. Cardiol., 2006, 101(4), 346-353.
[http://dx.doi.org/10.1007/s00395-006-0592-5] [PMID: 16705470]
[8]
Mastronardi, F.G.; Wood, D.D.; Mei, J.; Raijmakers, R.; Tseveleki, V.; Dosch, H-M.; Probert, L.; Casaccia-Bonnefil, P.; Moscarello, M.A. Increased citrullination of histone H3 in multiple sclerosis brain and animal models of demyelination: A role for tumor necrosis factor-induced peptidylarginine deiminase 4 translocation. J. Neurosci., 2006, 26(44), 11387-11396.
[http://dx.doi.org/10.1523/JNEUROSCI.3349-06.2006] [PMID: 17079667]
[9]
Shukla, A.; Chaurasia, P.; Bhaumik, S.R. Histone methylation and ubiquitination with their cross-talk and roles in gene expression and stability. Cell. Mol. Life Sci., 2009, 66(8), 1419-1433.
[http://dx.doi.org/10.1007/s00018-008-8605-1] [PMID: 19370393]
[10]
Varier, R.A.; Timmers, H.M. Histone lysine methylation and demethylation pathways in cancer. Biochimica et Biophysica Acta (BBA)-. Rev. Can., 2011, 1815(1), 75-89.
[11]
Predel, R.; Brandt, W.; Kellner, R.; Rapus, J.; Nachman, R.J.; Gäde, G. Post-translational modifications of the insect sulfakinins: sulfation, pyroglutamate-formation and O-methylation of glutamic acid. Eur. J. Biochem., 1999, 263(2), 552-560.
[http://dx.doi.org/10.1046/j.1432-1327.1999.00532.x] [PMID: 10406966]
[12]
Bannister, A.J.; Kouzarides, T. Reversing histone methylation. Nature, 2005, 436(7054), 1103-1106.
[http://dx.doi.org/10.1038/nature04048] [PMID: 16121170]
[13]
Akmal, M.A.; Rasool, N.; Khan, Y.D. Prediction of N-linked glycosylation sites using position relative features and statistical moments. PLoS One, 2017, 12(8)e0181966
[http://dx.doi.org/10.1371/journal.pone.0181966] [PMID: 28797096]
[14]
Butt, A.H.; Khan, S.A.; Jamil, H.; Rasool, N.; Khan, Y.D. A prediction model for membrane proteins using moments based features. 2016, 2016, 1-7.
[http://dx.doi.org/10.1155/2016/8370132]
[15]
Butt, A.H.; Rasool, N.; Khan, Y.D. A treatise to computational approaches towards prediction of membrane protein and its subtypes. J. Membr. Biol., 2017, 250(1), 55-76.
[http://dx.doi.org/10.1007/s00232-016-9937-7] [PMID: 27866233]
[16]
Butt, A.H.; Rasool, N.; Khan, Y.D. Predicting membrane proteins and their types by extracting various sequence features into Chou’s general PseAAC. Mol. Biol. Rep., 2018, 45(6), 2295-2306.
[http://dx.doi.org/10.1007/s11033-018-4391-5] [PMID: 30238411]
[17]
Hussain, W.; Khan, Y.D.; Rasool, N.; Khan, S.A.; Chou, K-C. SPalmitoylC-PseAAC: A sequence-based model developed via Chou’s 5-steps rule and general PseAAC for identifying S-palmitoylation sites in proteins. Anal. Biochem., 2019, 568, 14-23.
[http://dx.doi.org/10.1016/j.ab.2018.12.019] [PMID: 30593778]
[18]
Khan, Y.D.; Ahmed, F.; Khan, S.A. Situation recognition using image moments and recurrent neural networks. Neural Comput. Appl., 2014, 24(7-8), 1519-1529.
[http://dx.doi.org/10.1007/s00521-013-1372-4]
[19]
Khan, Y.D.; Jamil, M.; Hussain, W.; Rasool, N.; Khan, S.A.; Chou, K-C. pSSbond-PseAAC: Prediction of disulfide bonding sites by integration of PseAAC and statistical moments. J. Theor. Biol., 2018, 463, 47-55.
[PMID: 30550863]
[20]
Khan, Y.D.; Rasool, N.; Hussain, W.; Khan, S.A.; Chou, K-C. iPhosT-PseAAC: Identify phosphothreonine sites by incorporating sequence statistical moments into PseAAC. Anal. Biochem., 2018, 550, 109-116.
[http://dx.doi.org/10.1016/j.ab.2018.04.021] [PMID: 29704476]
[21]
Khan, Y.D.; Rasool, N.; Hussain, W.; Khan, S.A.; Chou, K-C. iPhosY-PseAAC: identify phosphotyrosine sites by incorporating sequence statistical moments into PseAAC. Mol. Biol. Rep., 2018, 45(6), 2501-2509.
[http://dx.doi.org/10.1007/s11033-018-4417-z] [PMID: 30311130]
[22]
Hussain, W.; Khan, Y.D.; Rasool, N.; Khan, S.A.; Chou, K-C. SPrenylC-PseAAC: A sequence-based model developed via Chou’s 5-steps rule and general PseAAC for identifying S-prenylation sites in proteins. J. Theor. Biol., 2019, 468, 1-11.
[http://dx.doi.org/10.1016/j.jtbi.2019.02.007] [PMID: 30768975]
[23]
Ghauri, A.W.; Khan, Y.D.; Rasool, N.; Khan, S.A.; Chou, K.C. pNitro-Tyr-PseAAC: Predict nitrotyrosine sites in proteins by incorporating five features into Chou’s general PseAAC. Curr. Pharm. Des., 2018, 24(34), 4034-4043.
[http://dx.doi.org/10.2174/1381612825666181127101039] [PMID: 30479209]
[24]
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]
[25]
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]
[26]
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]
[27]
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]
[28]
Chandra, A.; Sharma, A.; Dehzangi, A.; Ranganathan, S.; Jokhan, A.; Chou, K-C.; Tsunoda, T. PhoglyStruct: Prediction of phosphoglycerylated lysine residues using structural properties of amino acids. Sci. Rep., 2018, 8(1), 17923.
[http://dx.doi.org/10.1038/s41598-018-36203-8] [PMID: 30560923]
[29]
Wang, L.; Zhang, R.; Mu, Y. Fu-SulfPred: Identification of protein s-sulfenylation sites by fusing forests via Chou’s general PseAAC. J. Theor. Biol., 2019, 461, 51-58.
[http://dx.doi.org/10.1016/j.jtbi.2018.10.046] [PMID: 30365947]
[30]
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]
[31]
Chen, W.; Ding, H.; Zhou, X.; Lin, H.; Chou, K-C. iRNA(m6A)-PseDNC: Identifying N6-methyladenosine sites using pseudo dinucleotide composition. Anal. Biochem., 2018, 561-562, 59-65.
[http://dx.doi.org/10.1016/j.ab.2018.09.002] [PMID: 30201554]
[32]
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]
[33]
Chen, W.; Feng, P.; Yang, H.; Ding, H.; Lin, H.; Chou, K-C. iRNA-3typeA: Identifying three 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]
[34]
Chen, W.; Tang, H.; Ye, J.; Lin, H.; Chou, K-C. iRNA-PseU: Identifying RNA pseudouridine sites. Mol. Ther. Nucleic Acids, 2016, 5e332
[PMID: 28427142]
[35]
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]
[36]
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, 2018, 111(1), 96-102.
[PMID: 29360500]
[37]
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]
[38]
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]
[39]
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]
[40]
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]
[41]
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]
[42]
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]
[43]
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]
[44]
Qiu, W.R.; Sun, B.Q.; Xiao, X.; Xu, D.; Chou, K.C. iPhos-PseEvo: Identifying human phosphorylated proteins by incorporating evolutionary information into general PseAAC via grey system theory. Mol. Inform., 2017, 36(5-6)
[http://dx.doi.org/10.1002/minf.201600010] [PMID: 28488814]
[45]
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]
[46]
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]
[47]
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]
[48]
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, 2014, 1-12.
[49]
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.
[http://dx.doi.org/10.1080/07391102.2014.968875] [PMID: 25248923]
[50]
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]
[51]
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]
[52]
Xie, H-L.; Fu, L.; Nie, X-D. Using ensemble SVM to identify human GPCRs N-linked glycosylation sites based on the general form of Chou’s PseAAC. Protein Eng. Des. Sel., 2013, 26(11), 735-742.
[http://dx.doi.org/10.1093/protein/gzt042] [PMID: 24048266]
[53]
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]
[54]
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]
[55]
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]
[56]
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]
[57]
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]
[58]
Zhang, J.; Zhao, X.; Sun, P.; Ma, Z. PSNO: Predicting cysteine S-nitrosylation sites by incorporating various sequence-derived features into the general form of Chou’s PseAAC. Int. J. Mol. Sci., 2014, 15(7), 11204-11219.
[http://dx.doi.org/10.3390/ijms150711204] [PMID: 24968264]
[59]
Ehsan, A.; Mahmood, K.; Khan, Y.D.; Khan, S.A.; Chou, K-C. A novel modeling in mathematical biology for classification of signal peptides. Sci. Rep., 2018, 8(1), 1039.
[http://dx.doi.org/10.1038/s41598-018-19491-y] [PMID: 29348418]
[60]
Hussain, W.; Khan, Y.D.; Rasool, N.; Khan, S.A.; Chou, K-C. SPalmitoylC-PseAAC: A sequence-based model developed via Chou’s 5-steps rule and general PseAAC for identifying S-palmitoylation sites in proteins. Anal. Biochem., 2018, 568, 14-23.
[PMID: 30593778]
[61]
Awais, M.; Hussain, W.; Khan, Y.D.; Rasool, N.; Khan, S.A.; Chou, K.-C. Bioinformatics, 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. 2019, 1.
[62]
Chen, Z.; Liu, X.; Li, F.; Li, C.; Marquez-Lago, T.; Leier, A.; Akutsu, T.; Webb, G.I.; Xu, D.; Smith, A.I.J.B.B. Large-scale comparative assessment of computational predictors for lysine post-translational modification sites. Brief. Bioinform., 2018.
[http://dx.doi.org/10.1093/bib/bby089]
[63]
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]
[64]
Li, F.; Zhang, Y.; Purcell, A.W.; Webb, G.I.; Chou, K-C.; Lithgow, T.; Li, C.; Song, J. Positive-unlabelled learning of glycosylation sites in the human proteome. BMC Bioinformatics, 2019, 20(1), 112.
[http://dx.doi.org/10.1186/s12859-019-2700-1]
[65]
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, 2017, 110(5), 239-246.
[PMID: 29107015]
[66]
Wang, L.; Zhang, R.; Mu, Y. J. J. o. t. b. Fu-SulfPred: Identification of protein S-sulfenylation sites by fusing forests via Chou’s general PseAAC. 2019, 461, 51-58.
[67]
Xie, H.-L.; Fu, L.; Nie, X.-D. J. P. E. Design; Selection, using ensemble SVM to identify human GPCRs N-linked glycosylation sites based on the general form of Chou's PseAAC 2013, 26(11), 735-742.
[68]
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, 5.
[http://dx.doi.org/10.1093/bib/bby079] [PMID: 30351377]
[69]
Li, B-Q.; Hu, L-L.; Chen, L.; Feng, K-Y.; Cai, Y-D.; Chou, K-C. Prediction of protein domain with mRMR feature selection and analysis. PLoS One, 2012, 7(6)e39308
[http://dx.doi.org/10.1371/journal.pone.0039308] [PMID: 22720092]
[70]
Sun, Y-M.; Liao, W-L.; Huang, H-D.; Liu, B-J.; Chang, C-W.; Horng, J-T.; Wu, L-C. In: A human DNA methylation site predictor based on SVM 2009 Ninth IEEE International Conference on Bioinformatics and BioEngineering,, 2009, 22-29.
[http://dx.doi.org/10.1109/BIBE.2009.22]
[71]
Shao, J.; Xu, D.; Tsai, S-N.; Wang, Y.; Ngai, S-M. Computational identification of protein methylation sites through bi-profile Bayes feature extraction. PLoS One, 2009, 4(3)e4920
[http://dx.doi.org/10.1371/journal.pone.0004920] [PMID: 19290060]
[72]
Hu, L.L.; Li, Z.; Wang, K.; Niu, S.; Shi, X.H.; Cai, Y.D.; Li, H.P. Prediction and analysis of protein methylarginine and methyllysine based on multisequence features. Biopolymers, 2011, 95(11), 763-771.
[PMID: 21544797]
[73]
Shi, S-P.; Qiu, J-D.; Sun, X-Y.; Suo, S-B.; Huang, S-Y.; Liang, R-P. PMeS: Prediction of methylation sites based on enhanced feature encoding scheme. PLoS One, 2012, 7(6)e38772
[http://dx.doi.org/10.1371/journal.pone.0038772] [PMID: 22719939]
[74]
Valavanis, I.; Sifakis, E.G.; Georgiadis, P.; Kyrtopoulos, S.; Chatziioannou, A.A. A composite framework for the statistical analysis of epidemiological DNA methylation data with the Infinium Human Methylation 450K BeadChip. IEEE J. Biomed. Health Inform., 2014, 18(3), 817-823.
[http://dx.doi.org/10.1109/JBHI.2014.2298351] [PMID: 24808224]
[75]
Li, Z.; Chen, L.; Lai, Y.; Dai, Z.; Zou, X. The prediction of methylation states in human DNA sequences based on hexanucleotide composition and feature selection. Anal. Methods, 2014, 6(6), 1897-1904.
[http://dx.doi.org/10.1039/c3ay41962b]
[76]
Karagod, V.V.; Sinha, K. A novel machine learning framework for phenotype prediction based on genome-wide DNA methylation data. 2017 International Joint Conference on Neural Networks (IJCNN),, 2017, 1657-1664.
[http://dx.doi.org/10.1109/IJCNN.2017.7966050]
[77]
Khan, Y.D.; Ahmad, F.; Anwar, M.W. A neuro-cognitive approach for iris recognition using back propagation. World Appl. Sci. J., 2012, 16(5), 678-685.
[78]
Khan, Y.D.; Khan, N.S.; Farooq, S.; Abid, A.; Khan, S.A.; Ahmad, F.; Mahmood, M.K. An efficient algorithm for recognition of human actions. ScientificWorldJournal, 2014, 2014875879
[http://dx.doi.org/10.1155/2014/875879]
[79]
Khan, Y.D.; Khan, S.A.; Ahmad, F.; Islam, S. Iris recognition using image moments and k-means algorithm. ScientificWorldJournal, 2014, 2014, 1-9.
[http://dx.doi.org/10.1155/2014/723595]
[80]
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]
[81]
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]
[82]
Cheng, X.; Lin, W-Z.; Xiao, X.; Chou, K-C.; Hancock, J. pLoc_bal-mAnimal: Predict subcellular localization of animal proteins by balancing training dataset and PseAAC. Bioinformatics, 2018, 1, 9.
[PMID: 30010789]
[83]
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]
[84]
Xiao, X.; Cheng, X.; Chen, G.; Mao, Q.; Chou, K-C. pLoc_bal-mGpos: Predict subcellular localization of Gram-positive bacterial proteins by quasi-balancing training dataset and PseAAC. Genomics, 2018, 114(4), 886-892.
[PMID: 29842950]
[85]
Chou, K-C.; Cheng, X.; Xiao, X. pLoc_bal-mHum: Predict subcellular localization of human proteins by PseAAC and quasibalancing training dataset Genomics,, 2018, S0888-7543(18), 30276-3.
[http://dx.doi.org/10.1016/j.ygeno.2018.08.007] [PMID: 30179658]
[86]
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]
[87]
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]
[88]
Javed, F.; Hayat, M. Predicting subcellular localization of multilabel proteins by incorporating the sequence features into Chou’s PseAAC. Genomics, 2018, S0888-7543(18), 30519-6.
[http://dx.doi.org/10.1016/j.ygeno.2018.09.004] [PMID: 30196077]
[89]
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]
[90]
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.
[91]
Chen, W.; Feng, P.-M.; Lin, H.; Chou, K.-C. iRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition. 2013, 41(6)e68
[92]
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]
[93]
Chou, K.; Cheng, X.; Xiao, X. pLoc_bal-mEuk: Predict subcellular localization of eukaryotic proteins by general PseAAC and quasi-balancing training dataset. Med. Chem., 2018, 15(5), 472-485.
[94]
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
[95]
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]
[96]
Jia, J.; Li, X.; Qiu, W.; Xiao, X.; Chou, K-C. iPPI-PseAAC(CGR): Identify protein-protein interactions by incorporating chaos game representation into PseAAC. J. Theor. Biol., 2019, 460, 195-203.
[http://dx.doi.org/10.1016/j.jtbi.2018.10.021] [PMID: 30312687]
[97]
Khan, Y.D.; Batool, A.; Rasool, N.; Khan, S.A.; Chou, K-C. Prediction of nitrosocysteine sites using position and composition variant features. 2019, 16(4), 283-293.
[98]
Li, J.-X.; Wang, S.-Q.; Du, Q.-S.; Wei, H.; Li, X.-M.; Meng, J.-Z.; Wang, Q.-Y.; Xie, N.-Z.; Huang, R.-B.; Chou, K.-C. Simulated protein thermal detection (SPTD) for enzyme thermostability study and an application example for pullulanase from Bacillus deramificans. 2018, 24(34), 4023-4033.
[99]
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]
[100]
Liu, B.; Fang, L.; Long, R.; Lan, X.; Chou, K.-C. J. B. iEnhancer- 2L: A two-layer predictor for identifying enhancers and their strength by pseudo k-tuple nucleotide composition. 2015, 32(3), 362-369.
[101]
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]
[102]
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.
[103]
Lu, Y.; Wang, S.; Wang, J.; Zhou, G.; Zhang, Q.; Zhou, X.; Niu, B.; Chen, Q.; Chou, K-C. An epidemic avian influenza prediction model based on google trends. 2019, 16(4), 303-310.
[104]
Xiao, X.; Min, J-L.; Lin, W-Z.; Liu, Z.; Cheng, X.; Chou, K-C. Dynamics, 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.
[105]
Chou, K.J.C. Advance in predicting subcellular localization of multi-label proteins and its implication for developing multi-target drugs. Curr. Med. Chem., 2019.
[http://dx.doi.org/10.2174/0929867326666190507082559]
[106]
Apweiler, R.; Bairoch, A.; Wu, C.H.; Barker, W.C.; Boeckmann, B.; Ferro, S.; Gasteiger, E.; Huang, H.; Lopez, R.; Magrane, M. UniProt: the universal protein knowledgebase. Nucleic Acids Res., 2004, 45(Database issue), D158-D169.
[http://dx.doi.org/10.1093/nar/gkh131]
[107]
Huang, K-Y.; Su, M-G.; Kao, H-J.; Hsieh, Y-C.; Jhong, J-H.; Cheng, K-H.; Huang, H-D.; Lee, T-Y. dbPTM 2016: 10-year anniversary of a resource for post-translational modification of proteins. Nucleic Acids Res., 2016, 44(D1), D435-D446.
[http://dx.doi.org/10.1093/nar/gkv1240] [PMID: 26578568]
[108]
Fu, L.; Niu, B.; Zhu, Z.; Wu, S.; Li, W. CD-HIT: Accelerated for clustering the next-generation sequencing data. Bioinformatics, 2012, 28(23), 3150-3152.
[http://dx.doi.org/10.1093/bioinformatics/bts565] [PMID: 23060610]
[109]
Crooks, G.E.; Hon, G.; Chandonia, J-M.; Brenner, S.E. WebLogo: A sequence logo generator. Genome Res., 2004, 14(6), 1188-1190.
[http://dx.doi.org/10.1101/gr.849004] [PMID: 15173120]
[110]
Chen, W.; Xing, P.; Zou, Q.; Detecting, N. Detecting N6-methyladenosine sites from RNA transcriptomes using ensemble Support Vector Machines. Sci. Rep., 2017, 7, 40242.
[http://dx.doi.org/10.1038/srep40242] [PMID: 28079126]
[111]
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]
[112]
Chou, K-C. Prediction of signal peptides using scaled window. Peptides, 2001, 22(12), 1973-1979.
[113]
Feng, P-M.; Ding, H.; Chen, W.; Lin, H. Naive Bayes classifier with feature selection to identify phage virion proteins. Comput. Math. Methods Med., 2013, 2013530696
[http://dx.doi.org/10.1155/2013/530696]
[114]
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]
[115]
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]
[116]
Qiu, W.R.; Sun, B.Q.; Xiao, X.; Xu, D.; Chou, K.C. iPhos-PseEvo: Identifying human phosphorylated proteins by incorporating evolutionary information into general PseAAC via grey system theory. Mol. Inform., 2017, 36(5-6)1600010
[http://dx.doi.org/10.1002/minf.201600010] [PMID: 28488814]
[117]
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]
[118]
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]
[119]
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]
[120]
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]
[121]
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]
[122]
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]
[123]
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]
[124]
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]
[125]
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]
[126]
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]
[127]
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]
[128]
Ehsan, A.; Mahmood, K.; Khan, Y.D.; Khan, S.A.; Chou, K.C. A novel modeling in mathematical biology for classification of signal peptides. Sci. Rep., 2018, 8(1), 1039.
[http://dx.doi.org/10.1038/s41598-018-19491-y] [PMID: 29348418]
[129]
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, 2018, 111(1), 96-102.
[http://dx.doi.org/10.1016/j.ygeno.2018.01.005] [PMID: 29360500]
[130]
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.
[131]
Jia, J.; Liu, Z.; Xiao, X.; Liu, B.; Chou, K-C.J.M. iPPBS-Opt: A sequence-based ensemble classifier for identifying protein-protein binding sites by optimizing imbalanced training datasets. Molecules, 2016, 21(1), 95.
[132]
Jia, J.; Liu, Z.; Xiao, X.; Liu, B.; Chou, K-C. Dynamics, 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.
[133]
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]
[134]
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.
[135]
Song, J.; Wang, Y.; Li, F.; Akutsu, T.; Rawlings, N.D.; Webb, G.I.; Chou, K-C. iProt-Sub: A comprehensive package for accurately mapping and predicting protease-specific substrates and cleavage sites. Brief. Bioinform., 2018, 20(2), 638-658.
[PMID: 29897410]
[136]
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.
[137]
Yang, H.; Qiu, W-R.; Liu, G.; Guo, F-B.; Chen, W.; Chou, K-C.; Lin, H.J. iRSpot-Pse6NC: Identifying recombination spots in Saccharomyces cerevisiae by incorporating hexamer composition into general PseKNC. Int. J. Biol. Sci., 2018, 14(8), 883.
[138]
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]
[139]
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]
[140]
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]
[141]
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]
[142]
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]
[143]
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]
[144]
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, 2017, 110(1), 50-58.
[PMID: 28818512]
[145]
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]
[146]
Cheng, X.; Xiao, X.; Chou, K-C. pLoc-mVirus: Predict subcellular localization of multi-location virus proteins via incorporating the optimal GO information into general PseAAC. Gene, 2017, 628, 315-321.
[http://dx.doi.org/10.1016/j.gene.2017.07.036] [PMID: 28728979]
[147]
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]
[148]
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(4), 231-239.
[http://dx.doi.org/10.1016/j.ygeno.2017.10.002] [PMID: 28989035]
[149]
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]
[150]
Xiao, X.; Cheng, X.; Su, S.; Mao, Q.; Chou, K-C. pLoc-mGpos: Incorporate key gene ontology information into general PseAAC for predicting subcellular localization of Gram-positive bacterial proteins. Nat. Sci., 2017, 9(9), 330.
[http://dx.doi.org/10.4236/ns.2017.99032]
[151]
Cheng, X.; Zhao, S-G.; Xiao, X.; Chou, K-C. iATC-mISF: A multi-label classifier for predicting the classes of anatomical therapeutic chemicals. Bioinformatics, 2017, 33(3), 341-346.
[http://dx.doi.org/10.1093/bioinformatics/btx387] [PMID: 28172617]
[152]
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.
[153]
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]
[154]
Goksuluk, D.; Korkmaz, S.; Zararsiz, G.; Karaagaoglu, A.E. easyROC: An interactive web-tool for ROC curve analysis using R language environment. R J., 2016, 8(2), 213-230.
[http://dx.doi.org/10.32614/RJ-2016-042]
[155]
Xiao, X.; Xu, Z-C.; Qiu, W-R.; Wang, P.; Ge, H-T.; Chou, K-C. iPSW(2L)-PseKNC: A two-layer predictor for identifying promoters and their strength by hybrid features via pseudo K-tuple nucleotide composition Genomics, 2018, S0888-7543(18), 30613-X.
[http://dx.doi.org/10.1016/j.ygeno.2018.12.001] [PMID: 30529532]
[156]
Wang, J.; Li, J.; Yang, B.; Xie, R.; Marquez-Lago, T.T.; Leier, A.; Hayashida, M.; Akutsu, T.; Zhang, Y.; Chou, K-C. Bastion3: A two-layer ensemble predictor of type III secreted effectors. Bioinformatics, 2018, 35(12), 2017-2028.
[PMID: 30388198]
[157]
Srivastava, A.; Kumar, R.; Kumar, M. BlaPred: Predicting and classifying β-lactamase using a 3-tier prediction system via Chou’s general PseAAC. J. Theor. Biol., 2018, 457, 29-36.
[http://dx.doi.org/10.1016/j.jtbi.2018.08.030] [PMID: 30138632]
[158]
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]
[159]
Rasool, N.; Iftikhar, S.; Amir, A.; Hussain, W. Structural and quantum mechanical computations to elucidate the altered binding mechanism of metal and drug with pyrazinamidase from Mycobacterium tuberculosis due to mutagenicity. J. Mol. Graph. Model., 2018, 80, 126-131.
[http://dx.doi.org/10.1016/j.jmgm.2017.12.011] [PMID: 29331879]
[160]
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]
[161]
Li, F.; Wang, Y.; Li, C.; Marquez-Lago, T.T.; Leier, A.; Rawlings, N.D.; Haffari, G.; Revote, J.; Akutsu, T.; Chou, K-C.; Purcell, A.W.; Pike, R.N.; Webb, G.I.; Ian Smith, A.; Lithgow, T.; Daly, R.J.; Whisstock, J.C.; Song, J. Twenty years of bioinformatics research for protease-specific substrate and cleavage site prediction: A comprehensive revisit and benchmarking of existing methods. Brief. Bioinform., 2018.
[http://dx.doi.org/10.1093/bib/bby077] [PMID: 30184176]
[162]
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]
[163]
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]
[164]
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]
[165]
Althaus, I.W.; Chou, J.; Gonzales, A.; Deibel, M.; Chou, K.; Kezdy, F.; Romero, D.; Aristoff, P.; Tarpley, W.; 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.
[166]
Althaus, I.W.; Chou, J.J.; Gonzales, A.J.; Deibel, M.R.; Kuo-Chen, C.; Kezdy, F.J.; Romero, D.L.; Thomas, R.C.; Aristoff, P.A.; Tarpley, W.G. Kinetic studies with the non-nucleoside human immunodeficiency virus type-1 reverse transcriptase inhibitor U-90152E. Biochem. Pharmacol., 1994, 47(11), 2017-2028.
[http://dx.doi.org/10.1016/0006-2952(94)90077-9]
[167]
Althaus, I.W.; Gonzales, A.; Chou, J.; Romero, D.; Deibel, M.; Chou, K-C.; Kezdy, F.; Resnick, L.; Busso, M.; So, A. The quinoline U-78036 is a potent inhibitor of HIV-1 reverse transcriptase. J. Biol. Chem., 1993, 268(20), 14875-14880.
[168]
Chou, K.; Forsen, S.; Zhou, G. Schematic rules for deriving apparent rate constants 1980, 16(4), 109-113.
[169]
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]
[170]
Chou, K-C.; Lin, W-Z.; Xiao, X. Wenxiang: A web-server for drawing wenxiang diagrams. Nat. Sci., 2011, 03(10)
[http://dx.doi.org/10.4236/ns.2011.310111]
[171]
Chou, K-C. Graphic rules in steady and non-steady state enzyme kinetics. J. Biol. Chem., 1989, 264(20), 12074-12079.
[172]
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.
[173]
Chou, K-C. Graphic rule for drug metabolism systems. Curr. Drug Metab., 2010, 11(4), 369-378.
[http://dx.doi.org/10.2174/138920010791514261]
[174]
Chou, K. Graph theory of enzyme kinetics. J. Phys. Chem., 1979, 60, 1375-1378.
[175]
Kuochen, C.; Carter, R.; Forsen, S. A new graphical-method for deriving rate-equations for complicated mechanisms. J. Pre-Proof, 1981, 18(2), 82-86.
[176]
Kuo-Chen, C.; Forsen, S. Graphical rules of steady-state reaction systems. Can. J. Chem., 1981, 59(4), 737-755.
[http://dx.doi.org/10.1139/v81-107]
[177]
Zhou, G.; Deng, M. 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]
[178]
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]
[179]
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]
[180]
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]
[181]
Shen, H-B.; Song, J-N.; Chou, K-C. Engineering, Prediction of protein folding rates from primary sequence by fusing multiple sequential features. J. Biomed. Sci. Eng., 2009, 2, 136-143.
[182]
Chou, K.; Chen, N.; Forsen, S. The biological functions of low-frequency phonons. 2. Cooperative effects. Biophys. Chem., 1981, 18(3), 126-132.
[183]
Chou, K-C.; Shen, H-B. Recent advances in developing web-servers for predicting protein attributes. Nat. Sci., 2009, 1(2), 63-92.
[http://dx.doi.org/10.4236/ns.2009.12011]
[184]
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]
[185]
Chen, W.; Yang, H.; Feng, P.; Ding, H.; Lin, H. iDNA4mC: Identifying DNA N4-methylcytosine sites based on nucleotide chemical properties. Bioinformatics, 2017, 33(22), 3518-3523.
[http://dx.doi.org/10.1093/bioinformatics/btx479] [PMID: 28961687]
[186]
Xiao, X.; Cheng, X.; Chen, G.; Mao, Q.; Chou, K. pLoc_bal-mVirus: Predict subcellular localization of multi-label virus proteins by PseAAC and IHTS treatment to balance training dataset. Med. Chem., 2018, 15(5), 496-509.
[187]
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]

Rights & Permissions Print Cite
Article Metrics
30
2
1
1
© 2024 Bentham Science Publishers | Privacy Policy