iACP: A sequence-based tool for identifying anticancer peptides

Oncotarget

Volumn 7, Issue 13, 2016, Pages 16895-16909

  Chen, Wei ^a,c   Ding, Hui ^b   Feng, Pengmian ^a   Lin, Hao ^b,c   Chou, Kuo Chen ^c,d  

^a NORTH CHINA UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^b UNIVERSITY OF ELECTRONIC SCIENCE AND TECHNOLOGY OF CHINA   (China)

^c GORDON LIFE SCIENCE INSTITUTE   (United States)

^d KING ABDULAZIZ UNIVERSITY   (Saudi Arabia)

Author keywords

Anticancer peptides; g gap dipeptide mode; iACP webserver; Incremental feature selection; pseAAC

Indexed keywords

ANTINEOPLASTIC AGENT; PEPTIDE;

AMINO ACID SEQUENCE; ARTICLE; CLASSIFICATION ALGORITHM; CLIENT SERVER APPLICATION; DRUG IDENTIFICATION; INTERMETHOD COMPARISON; INTERNET; MEASUREMENT ACCURACY; ONLINE SYSTEM; PREDICTION; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINE; ALGORITHM; ANIMAL; BIOLOGY; DRUG SCREENING; HUMAN; PROCEDURES; SEQUENCE ANALYSIS; SOFTWARE;

ALGORITHMS; ANIMALS; ANTINEOPLASTIC AGENTS; COMPUTATIONAL BIOLOGY; DRUG SCREENING ASSAYS, ANTITUMOR; HUMANS; PEPTIDES; SEQUENCE ANALYSIS, PROTEIN; SOFTWARE;

EID: 84962865637     PISSN: None     EISSN: 19492553     Source Type: Journal    
DOI: 10.18632/oncotarget.7815     Document Type: Article

References (148)

1. Ferlay J, Shin HR, Bray F, Forman D, Mathers C, Parkin DM. Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. International journal of cancer. 2010; 127:2893-2917.
2. Al-Benna S, Shai Y, Jacobsen F, Steinstraesser L. Oncolytic activities of host defense peptides. International journal of molecular sciences. 2011; 12:8027-8051.
3. Kalyanaraman B, Joseph J, Kalivendi S, Wang S, Konorev E, Kotamraju S. Doxorubicin-induced apoptosis: implications in cardiotoxicity. Molecular and cellular biochemistry. 2002; 234-235:119-124.
4. Harris F, Dennison SR, Singh J, Phoenix DA. On the selectivity and efficacy of defense peptides with respect to cancer cells. Medicinal research reviews. 2013; 33:190-234.
5. Gaspar D, Veiga AS, Castanho MA. From antimicrobial to anticancer peptides. A review. Frontiers in microbiology. 2013; 4:294.
6. Huang Y, Feng Q, Yan Q, Hao X, Chen Y. Alpha-helical cationic anticancer peptides: a promising candidate for novel anticancer drugs. Mini reviews in medicinal chemistry. 2015; 15:73-81.
7. Chou KC. Review: Structural bioinformatics and its impact to biomedical science. Current Medicinal Chemistry. 2004; 11:2105-2134.
8. Zhang R, Wei DQ, Du QS. Molecular modeling studies of peptide drug candidates against SARS. Medicinal Chemistry. 2006; 2:309-314.
9. Wei DQ, Du QS, Sirois S, Zhong WZ. Review: Progress in computational approach to drug development against SARS. Current Medicinal Chemistry. 2006; 13:3263-3270.
10. Mader J, Hoskin D. Cationic antimicrobial peptides as novel cytotoxic agents for cancer treatment. Expert Opin Investig Drugs. 2006; 15:933-946.
11. Hoskin DW, Ramamoorthy A. Studies on anticancer activities of antimicrobial peptides. Biochimica et biophysica acta. 2008; 1778:357-375.
12. Hariharan S, Gustafson D, Holden S, McConkey D, Davis D, Morrow M, Basche M, Gore L, Zang C, O'Bryant CL, Baron A, Gallemann D, Colevas D, et al. Assessment of the biological and pharmacological effects of the alpha nu beta3 and alpha nu beta5 integrin receptor antagonist, cilengitide (EMD 121974), in patients with advanced solid tumors. Annals of oncology. 2007; 18:1400-1407.
13. Gregorc V, De Braud FG, De Pas TM, Scalamogna R, Citterio G, Milani A, Boselli S, Catania C, Donadoni G, Rossoni G, Ghio D, Spitaleri G, Ammannati C, et al. Phase I study of NGR-hTNF, a selective vascular targeting agent, in combination with cisplatin in refractory solid tumors. Clinical cancer research. 2011; 17:1964-1972.
14. Thundimadathil J. Cancer treatment using peptides: current therapies and future prospects. Journal of amino acids. 2012; 2012:967347.
15. Tyagi A, Kapoor P, Kumar R, Chaudhary K, Gautam A, Raghava GP. In silico models for designing and discovering novel anticancer peptides. Scientific reports. 2013; 3:2984.
16. 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. Journal of theoretical biology. 2014; 341:34-40.
17. Chen W, Feng PM, Lin H, Chou KC. iRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition Nucleic Acids Res. 2013; 41:e68.
18. Qiu WR, Xiao X, Lin WZ. iMethyl-PseAAC: Identification of Protein Methylation Sites via a Pseudo Amino Acid Composition Approach. Biomed Res Int (BMRI). 2014; 2014:947416.
19. Xu Y, Wen X, Wen LS, Wu LY. iNitro-Tyr: Prediction of nitrotyrosine sites in proteins with general pseudo amino acid composition. PLoS ONE. 2014; 9:e105018.
20. Chen W, Feng PM, Deng EZ, Lin H. iTIS-PseTNC: a sequence-based predictor for identifying translation initiation site in human genes using pseudo trinucleotide composition. Anal Biochem. 2014; 462:76-83.
21. Guo SH, Deng EZ, Xu LQ, Ding H, Lin H, Chen W. iNuc-PseKNC: a sequence-based predictor for predicting nucleosome positioning in genomes with pseudo k-tuple nucleotide composition. Bioinformatics. 2014; 30:1522-1529.
22. Qiu WR, Xiao X. iRSpot-TNCPseAAC: Identify recombination spots with trinucleotide composition and pseudo amino acid components. Int J Mol Sci (IJMS). 2014; 15:1746-1766.
23. Liu Z, Xiao X, Qiu WR. iDNA-Methyl: Identifying DNA methylation sites via pseudo trinucleotide compositi. Analytical Biochemistry (also, Data in Brief, 2015, 4:87-89). 2015; 474:69-77.
24. Qiu WR, Xiao X, Lin WZ. iUbiq-Lys: Prediction of lysine ubiquitination sites in proteins by extracting sequence evolution information via a grey system model Journal of Biomolecular Structure and Dynamics (JBSD) 2015; 33:1731-1742.
25. Jia J, Liu Z, Xiao X. 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.
26. Liu B, Fang L, Liu F, Wang X. Identification of real microRNA precursors with a pseudo structure status composition approach. PLoS ONE. 2015; 10:e0121501.
27. Chen W, Feng P, Ding H. iRNA-Methyl: Identifying N6-methyladenosine sites using pseudo nucleotide composition Analytical Biochemistry (also, Data in Brief, 2015, 5:376-378). 2015; 490:26-33.
28. Liu B, Fang L, Wang S, Wang X. Identification of microRNA precursor with the degenerate K-tuple or Kmer strategy Journal of Theoretical Biology. 2015; 385:153-159.
29. Liu B, Fang L, Long R, Lan X. iEnhancer-2L: a two-layer predictor for identifying enhancers and their strength by pseudo k-tuple nucleotide composition Bioinformatics. 2016; 32:362-389.
30. Chou KC. Some remarks on protein attribute prediction and pseudo amino acid composition (50th Anniversary Year Review). J Theor Biol. 2011; 273:236-247.
31. Chou KC, Shen HB. Review: Recent progresses in protein subcellular location prediction. Anal Biochem. 2007; 370:1-16.
32. Jiang SP, Liu WM, Fee CH. Graph theory of enzyme kinetics: 1. Steady-state reaction system. Scientia Sinica. 1979; 22:341-358.
33. Cornish-Bowden A. (1979). Fundamentals of Enzyme Kinetics, Chapter 4. (London: Butterworths).
34. Forsen S. Graphical rules for enzyme-catalyzed rate laws. Biochem J. 1980; 187:829-835.
35. Chou KC. A new schematic method in enzyme kinetics. Eur J Biochem. 1980; 113:195-198.
36. Liu WM. Graphical rules for non-steady state enzyme kinetics. J Theor Biol. 1981; 91:637-654.
37. Zhou GP, Deng MH. An extension of Chou's graphic rules for deriving enzyme kinetic equations to systems involving parallel reaction pathways. Biochem J. 1984; 222:169-176.
38. Chou KC. Graphic rules in steady and non-steady enzyme kinetics. J Biol Chem. 1989; 264:12074-12079.
39. Chou KC. Review: Applications of graph theory to enzyme kinetics and protein folding kinetics. Steady and non-steady state systems. Biophysical Chemistry. 1990; 35:1-24.
40. Shen HB. FoldRate: A web-server for predicting protein folding rates from primary sequence. The Open Bioinformatics Journal. 2009; 3:31-50.
41. Shen HB, Song JN. Prediction of protein folding rates from primary sequence by fusing multiple sequential features Journal of Biomedical Science and Engineering (JBiSE). 2009; 2: 136-143.
42. Chou KC, Shen HB. Review: recent advances in developing web-servers for predicting protein attributes. Natural Science. 2009; 1:63-92.
43. Althaus IW, Gonzales AJ, Romero DL, Aristoff PA, Tarpley WG, Reusser F. The quinoline U-78036 is a potent inhibitor of HIV-1 reverse transcriptase. J Biol Chem. 1993; 268:14875-14880.
44. Althaus IW, Chou JJ, Gonzales AJ, Diebel MR, Romero DL, Aristoff PA, Tarpley WG, Reusser F. Kinetic studies with the nonnucleoside HIV-1 reverse transcriptase inhibitor U-88204E. Biochemistry. 1993; 32:6548-6554.
45. Althaus IW, Chou JJ, Gonzales AJ, Kezdy FJ, Romero DL, Thomas RC, Aristoff PA, Tarpley WG, Reusser F. Kinetic studies with the non-nucleoside HIV-1 reverse transcriptase inhibitor U-90152E. Biochem Pharmacol. 1994; 47:2017-2028.
46. Diebel MR, Kezdy FJ, Romero DL, Thomas RC, Aristoff PA, Tarpley WG, Reusser F. The benzylthio-pyrididine U-31,355 is a potent inhibitor of HIV-1 reverse transcriptase. Biochem Pharmacol. 1996; 51:743-750.
47. Kezdy FJ, Reusser F. Review: Steady-state inhibition kinetics of processive nucleic acid polymerases and nucleases. Anal Biochem. 1994; 221:217-230.
48. Forsen S. Graphical rules of steady-state reaction systems. Can J Chem. 1981; 59:737-755.
49. Chou KC. Graphic rule for drug metabolism systems. Current Drug Metabolism. 2010; 11:369-378.
50. Chou KC, Zhang CT. Diagrammatization of codon usage in 339 HIV proteins and its biological implication. AIDS Research and Human Retroviruses. 1992; 8:1967-1976.
51. Zhang CT. Graphic analysis of codon usage strategy in 1490 human proteins. J Protein Chem. 1993; 12:329-335.
52. Zhang CT, Chou KC. Analysis of codon usage in 1562 E. Coli protein coding sequences. J Mol Biol. 1994; 238:1-8.
53. Chou KC, Zhang CT, Elrod DW. Do antisense proteins exist? J Protein Chem. 1996; 15:59-61.
54. Wu ZC, Xiao X. 2D-MH: A web-server for generating graphic representation of protein sequences based on the physicochemical properties of their constituent amino acids. J Theor Biol. 2010; 267:29-34.
55. Chou KC, Lin WZ, Xiao X. Wenxiang: a web-server for drawing wenxiang diagrams Natural Science. 2011; 3:862-865.
56. Zhou GP. The disposition of the LZCC protein residues in wenxiang diagram provides new insights into the protein-protein interaction mechanism. J Theor Biol. 2011; 284:142-148.
57. Zhou GP, Huang RB. The pH-Triggered Conversion of the PrP(c) to PrP(sc.). Curr Top Med Chem. 2013; 13:1152-1163.
58. Wilkinson L, Friendly M. The history of the cluster heat map. The American Statistician. 2009; 63:179-184.
59. Dobrzynska I, Szachowicz-Petelska B, Sulkowski S, Figaszewski Z. Changes in electric charge and phospholipids composition in human colorectal cancer cells. Molecular and cellular biochemistry. 2005; 276:113-119.
60. Utsugi T, Schroit AJ, Connor J, Bucana CD, Fidler IJ. Elevated expression of phosphatidylserine in the outer membrane leaflet of human tumor cells and recognition by activated human blood monocytes. Cancer research. 1991; 51:3062-3066.
61. Huang YB, Wang XF, Wang HY, Liu Y, Chen Y. Studies on mechanism of action of anticancer peptides by modulation of hydrophobicity within a defined structural framework. Molecular cancer therapeutics. 2011; 10:416-426.
62. Wang G, Li X, Wang Z. APD2: the updated antimicrobial peptide database and its application in peptide design. Nucleic acids research. 2009; 37:D933-937.
63. Bals R. Epithelial antimicrobial peptides in host defense against infection. Respiratory research. 2000; 1:141-150.
64. Fu L, Niu B, Zhu Z, Wu S, Li W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics. 2012; 28:3150-3152.
65. Chou KC, Wu ZC, Xiao X. iLoc-Hum: Using accumulation-label scale to predict subcellular locations of human proteins with both single and multiple sites. Molecular Biosystems. 2012; 8:629-641.
66. Tyagi A, Tuknait A, Anand P, Gupta S, Sharma M, Mathur D, Joshi A, Singh S, Gautam A, Raghava GP. CancerPPD: a database of anticancer peptides and proteins. Nucleic Acids Res. 2015; 43:D837-843.
67. Wootton JC, Federhen S. Statistics of local complexity in amino acid sequences and sequence databases. Comput Chem. 1993; 17:149-163.
68. Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 1997; 25:3389-3402.
69. Zhang CT. An optimization approach to predicting protein structural class from amino acid composition. Protein Science. 1992; 1:401-408.
70. Chou KC, Zhang CT. A correlation coefficient method to predicting protein structural classes from amino acid compositions. Eur J Biochem. 1992; 207:429-433.
71. Chou JJ. A formulation for correlating properties of peptides and its application to predicting human immunodeficiency virus protease-cleavable sites in proteins. Biopolymers. 1993; 33:1405-1414.
72. Thompson TB, Zheng C. Neural network prediction of the HIV-1 protease cleavage sites. Journal of Theoretical Biology 177. 1995:369-379.
73. Zhou GP, Doctor K. Subcellular location prediction of apoptosis proteins. Proteins: Struct, Funct, Genet. 2003; 50:44-48.
74. Chou KC. Prediction of G-protein-coupled receptor classes. Journal of Proteome Research. 2005; 4:1413-1418.
75. Wang M, Yang J, Xu ZJ. SLLE for predicting membrane protein types. J Theor Biol. 2005; 232:7-15.
76. Shen HB. Using optimized evidence-theoretic K-nearest neighbor classifier and pseudo amino acid composition to predict membrane protein types. Biochemical & Biophysical Research Communications. 2005; 334:288-292.
77. Chou KC, Shen HB. Euk-mPLoc: a fusion classifier for large-scale eukaryotic protein subcellular location prediction by incorporating multiple sites. Journal of Proteome Research. 2007; 6:1728-1734.
78. Xiao X, Wang P. GPCR-2L: Predicting G protein-coupled receptors and their types by hybridizing two different modes of pseudo amino acid compositions. Molecular Biosystems. 2011; 7:911-919.
79. Wang P, Xiao X. NR-2L: A Two-Level Predictor for Identifying Nuclear Receptor Subfamilies Based on Sequence-Derived Features. PLoS ONE. 2011; 6:e23505.
80. Kandaswamy KK, Moller S, Suganthan PN, Sridharan S, Pugalenthi G. AFP-Pred: A random forest approach for predicting antifreeze proteins from sequence-derived properties. J Theor Biol. 2011; 270:56-62.
81. Xu Y, Ding J, Wu LY. 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:e55844.
82. Chou KC. Some Remarks on Predicting Multi-Label Attributes in Molecular Biosystems. Molecular Biosystems. 2013; 9:1092-1100.
83. Liu B, Zhang D, Xu R, Xu J, Wang X. Combining evolutionary information extracted from frequency profiles with sequence-based kernels for protein remote homology detection. Bioinformatics. 2014; 30:472-479.
84. Nakashima H, Nishikawa K, Ooi T. The folding type of a protein is relevant to the amino acid composition. J Biochem. 1986; 99:152-162.
85. Klein P, Delisi C. Prediction of protein structural class from amino acid sequence. Biopolymers. 1986; 25:1659-1672.
86. Cedano J, Aloy P, Perez-Pons JA, Querol E. Relation between amino acid composition and cellular location of proteins. J Mol Biol. 1997; 266:594-600.
87. Zhou GP. An intriguing controversy over protein structural class prediction. J Protein Chem. 1998; 17:729-738.
88. Chou KC. Prediction of protein cellular attributes using pseudo amino acid compositi. PROTEINS: Structure, Function, and Genetics (Erratum: ibid, 2001, Vol44, 60). 2001; 43:246-255.
89. Chou KC. Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes. Bioinformatics. 2005; 21:10-19.
90. Lin SX, Lapointe J. Theoretical and experimental biology in one -A symposium in honour of Professor Kuo-Chen Chou's 50th anniversary and Professor Richard Giegé's 40th anniversary of their scientific careers. J Biomedical Science and Engineering (JBiSE). 2013; 6:435-442.
91. Cao DS, Xu QS, Liang YZ. propy: a tool to generate various modes of Chou's PseAAC. Bioinformatics. 2013; 29:960-962.
92. 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. International Journal of Molecular Sciences. 2014; 15:3495-3506.
93. Zhou XB, Chen C, Li ZC, Zou XY. Using Chou's amphiphilic pseudo-amino acid composition and support vector machine for prediction of enzyme subfamily classes. J Theor Biol. 2007; 248:546-551.
94. 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; 263:203-209.
95. Sahu SS, Panda G. A novel feature representation method based on Chou's pseudo amino acid composition for protein structural class prediction. Computational Biology and Chemistry. 2010; 34:320-327.
96. Mohabatkar H, Mohammad Beigi M, Esmaeili A. Prediction of GABA(A) receptor proteins using the concept of Chou's pseudo-amino acid composition and support vector machine. J Theor Biol. 2011; 281:18-23.
97. 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. Journal of Structural and Functional Genomics. 2011; 12:191-197.
98. 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 Transaction on Computational Biolology and Bioinformatics. 2012; 9:467-475.
99. Gupta MK, 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; 24:597-609.
100. 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.
101. Huang C, Yuan JQ. 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.
102. Mohabatkar H, Beigi MM, 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. Medicinal Chemistry. 2013; 9:133-137.
103. Khan ZU, Hayat M, Khan MA. 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.
104. 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.
105. Kumar R, Srivastava A, Kumari B, Kumar M. Prediction of beta-lactamase and its class by Chou's pseudo-amino acid composition and support vector machine. J Theor Biol. 2015; 365:96-103.
106. Wang X, Zhang W, Zhang Q, Li GZ. MultiP-SChlo: multi-label protein subchloroplast localization prediction with Chou's pseudo amino acid composition and a novel multi-label classifier. Bioinformatics. 2015; 31:2639-2645.
107. 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. Medical & biological engineering & computing. 2015; 53:331-344.
108. Liu B, Liu F, Wang X, Chen J. Chou KC. Pse-in-One: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences Nucleic Acids Res. 2015; 43:W65-W71.
109. Chen W, Lin H, Chou KC. Pseudo nucleotide composition or PseKNC: an effective formulation for analyzing genomic sequences. Mol BioSyst. 2015; 11:2620-2634.
110. Zhong WZ, Zhou SF. Molecular science for drug development and biomedicine. Intenational Journal of Molecular Sciences. 2014; 15:20072-20078.
111. Chou KC. Impacts of bioinformatics to medicinal chemistry. Medicinal Chemistry. 2015; 11:218-234.
112. Xu Y. Recent progress in predicting posttranslational modification sites in proteins. Curr Top Med Chem. 2016; 16:591-603.
113. Chen W, Lin H. Identification of voltage-gated potassium channel subfamilies from sequence information using support vector machine. Computers in biology and medicine. 2012; 42:504-507.
114. Lin H, Chen W. Prediction of thermophilic proteins using feature selection technique. Journal of microbiological methods. 2011; 84:67-70.
115. Ding H, Deng EZ, Yuan LF, Liu L. iCTX-Type: A Sequence-Based Predictor for Identifying the Types of Conotoxins in Targeting Ion Channels. BioMed research international. 2014; 2014:286419.
116. Ding H, Feng PM, Chen W, Lin H. Identification of bacteriophage virion proteins by the ANOVA feature selection and analysis. Molecular bioSystems. 2014; 10:2229-2235.
117. Lin H, Chen W, Ding H. AcalPred: a sequence-based tool for discriminating between acidic and alkaline enzymes. PloS one. 2013; 8:e75726.
118. Liu W. Protein secondary structural content prediction. Protein Eng. 1999; 12:1041-1050.
119. Xu Y, Wen X, Shao XJ. iHyd-PseAAC: Predicting hydroxyproline and hydroxylysine in proteins by incorporating dipeptide position-specific propensity into pseudo amino acid composition. Int J Mol Sci. 2014; 15:7594-7610.
120. Xu Y, Shao XJ, Wu LY, Deng NY. iSNO-AAPair: incorporating amino acid pairwise coupling into PseAAC for predicting cysteine S-nitrosylation sites in proteins. PeerJ. 2013; 1:e171.
121. Han GS, Yu ZG, 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.
122. Lin H, Deng EZ, Ding H, Chen W, Chou KC. iPro54-PseKNC: a sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition. Nucleic Acids Res. 2014; 42:12961-12972.
123. Xiao X, Min JL, Lin WZ, Liu Z. iDrug-Target: predicting the interactions between drug compounds and target proteins in cellular networking via the benchmark dataset optimization approach. Journal of Biomolecular Structure & Dynamics (JBSD). 2015; 33:2221-2233.
124. Liu B, Fang L, Liu F, Wang X. iMiRNA-PseDPC: microRNA precursor identification with a pseudo distance-pair composition approach. Journal of Biomolecular Structure & Dynamics (JBSD). 2016; 34:223-235.
125. Chou KC, Cai YD. Using functional domain composition and support vector machines for prediction of protein subcellular location. J Biol Chem. 2002; 277:45765-45769.
126. Cai YD, Zhou GP. Support vector machines for predicting membrane protein types by using functional domain composition. Biophys J. 2003; 84:3257-3263.
127. Cristianini N, Shawe-Taylor J. An introduction of Support Vector Machines and other kernel-based learning methodds. (Cambridge, UK: Cambridge University Press). 2000.
128. Chang C, Lin J. LIBSVM: a library for support vector machines. http://www.csientuedutw/~cjlin/libsvm/. 2001.
129. Chou KC, Zhang CT. Review: Prediction of protein structural classes. Crit Rev Biochem Mol Biol. 1995; 30:275-349.
130. Chou KC, Cai YD. Prediction of membrane protein types by incorporating amphipathic effects. Journal of Chemical Information and Modeling. 2005; 45:407-413.
131. Shen HB. Virus-PLoc: A fusion classifier for predicting the subcellular localization of viral proteins within host and virus-infected cells. Biopolymers. 2007; 85:233-240.
132. Chen W, Lin H, Feng PM, Ding C. iNuc-PhysChem: A Sequence-Based Predictor for Identifying Nucleosomes via Physicochemical Properties. PLoS ONE. 2012; 7:e47843.
133. Sun XY, Shi SP, Qiu JD, Suo SB, Huang SY, Liang RP. Identifying protein quaternary structural attributes by incorporating physicochemical properties into the general form of Chou's PseAAC via discrete wavelet transform. Molecular BioSystems. 2012; 8:3178-3184.
134. Kabir M, Hayat M. iRSpot-GAEnsC: identifing recombination spots via ensemble classifier and extending the concept of Chou's PseAAC to formulate DNA samples. Molecular genetics and genomics. 2016; 291:285-296.
135. Chou KC. Using subsite coupling to predict signal peptides. Protein Eng. 2001; 14:75-79.
136. Jia J, Liu Z, Xiao X, Liu B. iPPBS-Opt: A Sequence-Based Ensemble Classifier for Identifying Protein-Protein Binding Sites by Optimizing Imbalanced Training Datasets. Molecules. 2016; 21:95.
137. Jia J, Liu Z, Xiao X, Liu B. 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.
138. Liu Z, Xiao X, Yu DJ, Jia J, Qiu WR. pRNAm-PC: Predicting N-methyladenosine sites in RNA sequences via physical-chemical properties Anal Biochem. 2016; 497:60-67.
139. Chen W, Feng P, Ding H, Lin H. Using deformation energy to analyze nucleosome positioning in genomes. Genomics. 2016; 107:69-75.
140. Chou KC. A vectorized sequence-coupling model for predicting HIV protease cleavage sites in proteins. J Biol Chem. 1993; 268:16938-16948.
141. Lin WZ, Fang JA, Xiao X. iLoc-Animal: A multi-label learning classifier for predicting subcellular localization of animal proteins Molecular BioSystems. 2013; 9:634-644.
142. Xiao X, Wu ZC. 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; 284:42-51.
143. Xiao X, Wang P, Lin WZ. iAMP-2L: A two-level multi-label classifier for identifying antimicrobial peptides and their functional types. Anal Biochem. 2013; 436:168-177.
144. Lin H, Ding H. Predicting ion channels and their types by the dipeptide mode of pseudo amino acid composition. J Theor Biol. 2011; 269:64-69.
145. Li BQ, Hu LL, Chen L, Feng KY. Prediction of Protein Domain with mRMR Feature Selection and Analysis. PLoS One. 2012; 7:e39308.
146. Wang P, Hu L, Liu G, Jiang N, Chen X, Xu J, Zheng W, Li L, Tan M, Chen Z. Prediction of antimicrobial peptides based on sequence alignment and feature selection methods. PLoS ONE. 2011; 6:e18476.
147. Li BQ, Huang T, Liu L. Identification of colorectal cancer related genes with mRMR and shortest path in protein-protein interaction network. PLoS ONE. 2012; 7:e33393.
148. Huang T, Wang J, Cai YD. Hepatitis C virus network based classification of hepatocellular cirrhosis and carcinoma. PLoS ONE. 2012; 7:e34460.