ICTX-type: A sequence-based predictor for identifying the types of conotoxins in targeting ion channels

BioMed Research International

Volumn 2014, Issue , 2014, Pages

  Ding, Hui ^a   Deng, En Ze ^a   Yuan, Lu Feng ^a   Liu, Li ^b   Lin, Hao ^a,c   Chen, Wei ^c,d   Chou, Kuo Chen ^c,e  

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

^b INNER MONGOLIA UNIVERSITY   (China)

^c GORDON LIFE SCIENCE INSTITUTE   (United States)

^d HEBEI UNITED UNIVERSITY   (China)

^e KING ABDULAZIZ UNIVERSITY   (Saudi Arabia)

Author keywords

[No Author keywords available]

Indexed keywords

AMINO ACID; CALCIUM CHANNEL; CONOTOXIN; DIPEPTIDE; POTASSIUM CHANNEL; SODIUM CHANNEL; NEUROPEPTIDE; PEPTIDE;

ALGORITHM; AMINO ACID COMPOSITION; AMINO ACID SEQUENCE; ARTICLE; CORRELATIONAL STUDY; JACKKNIFE TEST; KERNEL METHOD; PREDICTION; CHEMISTRY; CLASSIFICATION; DRUG EFFECTS; HUMAN; METABOLISM;

ALGORITHMS; AMINO ACID SEQUENCE; AMINO ACIDS; CALCIUM CHANNELS; CONOTOXINS; HUMANS; NEUROPEPTIDES; PEPTIDES; POTASSIUM CHANNELS; SODIUM CHANNELS;

EID: 84903592187     PISSN: 23146133     EISSN: 23146141     Source Type: Journal    
DOI: 10.1155/2014/286419     Document Type: Article

References (99)

1. Gabashvili I. S., Sokolowski B. H., Morton C. C., Giersch A. B., Ion channel gene expression in the inner ear. Journal of the Association for Research in Otolaryngology 2007 8 3 305 328 2-s2.0-34547690932 10.1007/s10162-007-0082-y (Pubitemid 47222429)
2. Schnell J. R., Chou J. J., Structure and mechanism of the M2 proton channel of influenza A virus. Nature 2008 451 7178 591 595 2-s2.0-38749106195 10.1038/nature06531 (Pubitemid 351186272)
3. Pielak R. M., Schnell J. R., Chou J. J., Mechanism of drug inhibition and drug resistance of influenza A M2 channel. Proceedings of the National Academy of Sciences of the United States of America 2009 106 18 7379 7384 2-s2.0-66149112971 10.1073/pnas.0902548106
4. OuYang B., Xie S., Berardi M. J., Zhao X. M., Dev J., Yu W., Sun J., Chou J., Unusual architecture of the p7 channel from hepatitis C virus. Nature 2013 498 7455 521 525 10.1038/nature12283
5. Xiao X., Min J. L., Wang P., Predict drug-protein interaction in cellular networking. Current Topics in Medicinal Chemistry 2013 13 14 1707 1712 10.2174/15680266113139990121
6. Chou K.-C., Insights from modeling three-dimensional structures of the human potassium and sodium channels. Journal of Proteome Research 2004 3 4 856 861 2-s2.0-4444261145 10.1021/pr049931q (Pubitemid 39166248)
7. Yuan L. F., Ding C., Guo S. H., Ding H., Chen W., Lin H., Prediction of the types of ion channel-targeted conotoxins based on radial basis function network. Toxicology in Vitro 2013 27 2 852 856 10.1016/j.tiv.2012.12.024
8. Daly N. L., Craik D. J., Structural studies of conotoxins. IUBMB Life 2009 61 2 144 150 2-s2.0-63449129387 10.1002/iub.158
9. Mondal S., Bhavna R., Mohan Babu R., Ramakumar S., Pseudo amino acid composition and multi-class support vector machines approach for conotoxin superfamily classification. Journal of Theoretical Biology 2006 243 2 252 260 2-s2.0-33749596030 10.1016/j.jtbi.2006.06.014 (Pubitemid 44548567)
10. Chou K.-C., Prediction of protein cellular attributes using pseudo-amino acid composition. Proteins 2001 43 3 246 255. Erratum in: Proteins, vol. 44, no. 1, article 60, 2001 2-s2.0-0035400447 10.1002/prot.1072 (Pubitemid 32510585)
11. Chou K. C., Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes. Bioinformatics 2005 21 1 10 19 2-s2.0-12744279642 10.1093/bioinformatics/bth466
12. Lin H., Li Q. Z., Predicting conotoxin superfamily and family by using pseudo amino acid composition and modified Mahalanobis discriminant. Biochemical and Biophysical Research Communications 2007 354 2 548 551 2-s2.0-33846524311 10.1016/j.bbrc.2007.01.011 (Pubitemid 46161358)
13. Yin J. B., Fan Y. X., Shen H. B., Conotoxin superfamily prediction using diffusion maps dimensionality reduction and subspace classifier. Current Protein and Peptide Science 2011 12 6 580 588 2-s2.0-80052200968 10.2174/ 138920311796957702
14. Laht S., Koua D., Kaplinski L., Lisacek F., Stöcklin R., Remm M., Identification and classification of conopeptides using profile hidden Markov Models. Biochimica et Biophysica Acta 2012 1824 3 488 492 2-s2.0-84856118129 10.1016/j.bbapap.2011.12.004
15. Koua D., Laht S., Kaplinski L., Stocklin R., Remm M., Favreau P., Lisacek F., Position-specific scoring matrix and hidden Markov model complement each other for the prediction of conopeptide superfamilies. Biochimica et Biophysica Acta 2013 1834 4 717 724
16. Gowd K. H., Dewan K. K., Iengar P., Krishnan K. S., Balaram P., Probing peptide libraries from Conus achatinus using mass spectrometry and cDNA sequencing: identification of δ and ω -conotoxins. Journal of Mass Spectrometry 2008 43 6 791 805 2-s2.0-46849122906 10.1002/jms.1377 (Pubitemid 351954813)
17. Hillyard D. R., Mcintosh M. J., Jones R. M., Cartier E. G., Watkins M., Olivera B. M., Layer R. T., O-superfamily conotoxin peptides. Patent number JP2003533178, 2008
18. Chou K.-C., Some remarks on protein attribute prediction and pseudo amino acid composition. Journal of Theoretical Biology 2011 273 1 236 247 2-s2.0-79951518208 10.1016/j.jtbi.2010.12.024
19. Guo S. H., Deng E. Z., Xu L. Q., 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 10.1093/ bioinformatics/btu083
20. Xu Y., Ding J., Wu L. Y., 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 10.1093/bioinformatics/ btu083
21. Qiu W. R., Xiao X., iRSpot-TNCPseAAC: identify recombination spots with trinucleotide composition and pseudo amino acid components. International Journal of Molecular Sciences 2014 15 2 1746 1766 10.3390/ijms15021746
22. Liu B., Zhang D., Xu R., Xu J., Wang X., Chen Q., Dong Q., Combining evolutionary information extracted from frequency profiles with sequence-based kernels for protein remote homology detection. Bioinformatics 2014 30 4 472 479
23. Chen W., Feng P. M., Lin H., Chou K. C., iRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition. Nucleic Acids Research 2013 41 6, article e68 10.1093/nar/gks1450
24. Xiao J. L., Min X., Chou K.-C., iEzy-Drug: a web server for identifying the interaction between enzymes and drugs in cellular networking. BioMed Research International 2013 2013 13 701317 10.1155/2013/701317
25. Xiao X., Min J. L., Wang P., iCDI-PseFpt: identify the channel-drug interaction in cellular networking with PseAAC and molecular fingerprints. Journal of Theoretical Biology C 2013 337 71 79
26. 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 1, article e171 10.7717/peerj.171
27. Chen W., Lin H., Feng P. M., Ding C., Zuo Y. C., iNuc-PhysChem: a sequence-based predictor for identifying nucleosomes via physicochemical properties. PLoS ONE 2012 7 10 e47843 10.1371/journal.pone.0047843
28. Xu Y., Wen X., Shao X. J., Deng N. Y., iHyd-PseAAC: predicting hydroxyproline and hydroxylysine in proteins by incorporating dipeptide position-specific propensity into pseudo amino acid composition. International Journal of Molecular Sciences 2014 15 5 7594 7610 10.3390/ijms15057594
29. Consortium T. U., Reorganizing the protein space at the Universal Protein Resource (UniProt). Nucleic Acids Research 2012 40 D71 D75 10.1093/nar/gkr981
30. Chou K. C., Shen H. B., Predicting eukaryotic protein subcellular location by fusing optimized evidence-theoretic K-nearest neighbor classifiers. Journal of Proteome Research 2006 5 8 1888 1897 2-s2.0-33747197197 10.1021/pr060167c (Pubitemid 44232701)
31. Chou K. C., Shen H. B., Recent progress in protein subcellular location prediction. Analytical Biochemistry 2007 370 1 1 16 2-s2.0-34548606295 10.1016/j.ab.2007.07.006 (Pubitemid 47404494)
32. 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 10.1093/bioinformatics/bts565
33. Wootton J. C., Federhen S., Statistics of local complexity in amino acid sequences and sequence databases. Computers and Chemistry 1993 17 2 149 163 2-s2.0-0001514262
34. Chou J. J., A formulation for correlating properties of peptides and its application to predicting human immunodeficiency virus protease-cleavable sites in proteins. Biopolymers 1993 33 9 1405 1414 2-s2.0-0027170793 (Pubitemid 23249157)
35. Chou K. C., Prediction of G-protein-coupled receptor classes. Journal of Proteome Research 2005 4 4 1413 1418 2-s2.0-23944438427 10.1021/pr050087t (Pubitemid 41208699)
36. Wang M., Yang J., Xu Z. J., Chou K.C., SLLE for predicting membrane protein types. Journal of Theoretical Biology 2005 232 1 7 15 2-s2.0-6344231856 10.1016/j.jtbi.2004.07.023 (Pubitemid 39388370)
37. Xiao X., Wang P., Chou K.-C., Predicting protein structural classes with pseudo amino acid composition: an approach using geometric moments of cellular automaton image. Journal of Theoretical Biology 2008 254 3 691 696 2-s2.0-51349105695 10.1016/j.jtbi.2008.06.016
38. Feng K. Y., Cai Y. D., Chou K. C., Boosting classifier for predicting protein domain structural class. Biochemical and Biophysical Research Communications 2005 334 1 213 217 2-s2.0-22144470177 10.1016/j.bbrc.2005.06.075 (Pubitemid 40983934)
39. Cai Y. D., Chou K. C., Artificial neural network model for predicting α -turn types. Analytical Biochemistry 1999 268 2 407 409 2-s2.0-0033559165 10.1006/abio.1998.2992 (Pubitemid 29147159)
40. Thompson T. B., Zheng C., Chou K.-C., Neural network prediction of the HIV-1 protease cleavage sites. Journal of Theoretical Biology 1995 177 4 369 379 2-s2.0-0029595415 10.1006/jtbi.1995.0254 (Pubitemid 26052745)
41. Zhang C. T., Chou K. C., An optimization approach to predicting protein structural class from amino acid composition. Protein Science 1992 1 3 401 408 2-s2.0-0027080161
42. Feng P. M., Chen W., Lin H., iHSP-PseRAAAC: identifying the heat shock protein families using pseudo reduced amino acid alphabet composition. Analytical Biochemistry 2013 442 1 118 125 10.1016/j.ab.2013.05.024
43. 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 2-s2.0-84857411675 10.1371/journal.pone. 0030869 e30869
44. 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 2-s2.0-80052855771 10.1371/journal.pone.0024756 e24756
45. 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. Journal of Theoretical Biology 2011 270 1 56 62 2-s2.0-78649326452 10.1016/j.jtbi.2010.10. 037
46. Cai Y. D., Chou K. C., Predicting subcellular localization of proteins in a hybridization space. Bioinformatics 2004 20 7 1151 1156 2-s2.0-2442671554 10.1093/bioinformatics/bth054 (Pubitemid 38660352)
47. Chou K. C., Cai Y. D., Prediction of protease types in a hybridization space. Biochemical and Biophysical Research Communications 2006 339 3 1015 1020 2-s2.0-28844434867 10.1016/j.bbrc.2005.10.196 (Pubitemid 41772913)
48. Shen H., Chou K. C., Using optimized evidence-theoretic K-nearest neighbor classifier and pseudo-amino acid composition to predict membrane protein types. Biochemical and Biophysical Research Communications 2005 334 1 288 292 2-s2.0-22144487766 10.1016/j.bbrc.2005.06.087 (Pubitemid 40978537)
49. Chou K. C., Shen H. B., Euk-mPLoc: a fusion classifier for large-scale eukaryotic protein subcellular location prediction by incorporating multiple sites. Journal of Proteome Research 2007 6 5 1728 1734 2-s2.0-34249318001 10.1021/pr060635i (Pubitemid 46814497)
50. Shen H. B., Chou K. C., A top-down approach to enhance the power of predicting human protein subcellular localization: Hum-mPLoc 2.0. Analytical Biochemistry 2009 394 2 269 274 2-s2.0-69949101944 10.1016/j.ab.2009.07.046
51. Zhang T. L., Ding Y. S., Chou K. C., Prediction protein structural classes with pseudo-amino acid composition: approximate entropy and hydrophobicity pattern. Journal of Theoretical Biology 2008 250 1 186 193 2-s2.0-36348994911 10.1016/j.jtbi.2007.09.014 (Pubitemid 350160710)
52. 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. Molecular BioSystems 2011 7 3 911 919 2-s2.0-79951606205 10.1039/c0mb00170h
53. Shen H. B., Yang J., Chou K. C., Fuzzy KNN for predicting membrane protein types from pseudo-amino acid composition. Journal of Theoretical Biology 2006 240 1 9 13 2-s2.0-33748150984 10.1016/j.jtbi.2005.08.016 (Pubitemid 44313787)
54. Xiao X., Min J. L., Wang P., iGPCR-Drug: a web server for predicting interaction between GPCRs and drugs in cellular networking. PLoS ONE 2013 8 8 e72234 10.1371/journal.pone.0072234
55. 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. Analytical Biochemistry 2013 436 2 168 177 10.1016/j.ab.2013.01.019
56. Chou K. C., Some remarks on predicting multi-label attributes in molecular biosystems. Molecular Biosystems 2013 9 6 1092 1100 10.1039/c3mb25555g
57. Nakashima H., Nishikawa K., Ooi T., The folding type of a protein is relevant to the amino acid composition. Journal of Biochemistry 1986 99 1 153 162 2-s2.0-0022631926 (Pubitemid 16188308)
58. Cedano J., Aloy P., Pérez-Pons J. A., Querol E., Relation between amino acid composition and cellular location of proteins. Journal of Molecular Biology 1997 266 3 594 600 10.1006/jmbi.1996.0804 (Pubitemid 27117290)
59. Zhou G.-P., An intriguing controversy over protein structural class prediction. Protein Journal 1998 17 8 729 738 2-s2.0-54749084166 (Pubitemid 29062040)
60. Lin S.-X., Lapointe J., Theoretical and experimental biology in one. Journal of Biomedical Science and Engineering (JBiSE) 2013 6 435 442 10.4236/jbise.2013.64054
61. 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. Journal of Theoretical Biology 2007 248 3 546 551 2-s2.0-34548439881 10.1016/j.jtbi.2007.06.001 (Pubitemid 47368372)
62. 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 2-s2.0-43149097851 10.1007/s00726-007-0018-1
63. 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. Journal of Theoretical Biology 2009 257 1 17 26 2-s2.0-59649123088 10.1016/j.jtbi.2008.11.003
64. Esmaeili M., Mohabatkar H., Mohsenzadeh S., Using the concept of Chou's pseudo amino acid composition for risk type prediction of human papillomaviruses. Journal of Theoretical Biology 2010 263 2 203 209 2-s2.0-77649337793 10.1016/j.jtbi.2009.11.016
65. Mohabatkar H., Prediction of cyclin proteins using chou's pseudo amino acid composition. Protein and Peptide Letters 2010 17 10 1207 1214 2-s2.0-77958497871 10.2174/092986610792231564
66. Sahu S. S., 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 5-6 320 327 2-s2.0-78649324596 10.1016/j.compbiolchem.2010.09.002
67. 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. Journal of Theoretical Biology 2011 281 1 18 23 2-s2.0-79955564229 10.1016/j.jtbi.2011.04.017
68. 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 4 191 197 2-s2.0-84855690179 10.1007/s10969-011- 9120-4
69. Mei S., Multi-kernel transfer learning based on Chou's PseAAC formulation for protein submitochondria localization. Journal of Theoretical Biology 2012 293 121 130 2-s2.0-80155143429 10.1016/j.jtbi.2011.10.015
70. 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 10.1007/s00726-011-1114-9
71. 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 Transactions on Computational Biology and Bioinformatics 2012 9 2 467 475 2-s2.0-84856475734 10.1109/TCBB.2011.117
72. 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 and QSAR in Environmental Research 2013 24 7 597 609 10.1080/1062936X.2013.773378
73. 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 10.1016/j.jtbi.2013.08.037
74. 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 10.1016/j.biosystems.2013.04.005
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. Journal of Theoretical Biology 2013 335 205 212 10.1016/j.jtbi.2013.06.034
76. Mohabatkar H., Mohammad Beigi 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. Medicinal Chemistry 2013 9 1 133 137 10.2174/157340613804488341
77. 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 and Peptide Letters 2013 20 7 781 795 10.2174/0929866511320070008
78. 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. Journal of Theoretical Biology 2013 323 40 48 10.1016/j.jtbi.2013.01.012
79. Chen W., Lei T. Y., Jin D. C., Lin H., PseKNC: a flexible web-server for generating pseudo K-tuple nucleotide composition. Analytical Biochemistry 2014 456 53 60
80. Li B.-Q., Huang T., Liu L., Cai Y.-D., Chou K.-C., Identification of colorectal cancer related genes with mrmr and shortest path in protein-protein interaction network. PLoS ONE 2012 7 4 2-s2.0-84859298511 10.1371/journal.pone. 0033393 e33393
81. Huang T., Wang J., Cai Y.-D., Yu H., Chou K.-C., Hepatitis C virus network based classification of hepatocellular cirrhosis and carcinoma. PLoS ONE 2012 7 4 2-s2.0-84859499974 10.1371/journal.pone.0034460 e34460
82. Jiang Y., Huang T., Chen L., Gao Y. F., Cai Y., Chou K.-C., Signal propagation in protein interaction network during colorectal cancer progression. BioMed Research International 2013 2013 9 287019 10.1155/2013/287019
83. Shen H.-B., Chou K.-C., PseAAC: a flexible web server for generating various kinds of protein pseudo amino acid composition. Analytical Biochemistry 2008 373 2 386 388 2-s2.0-37549004451 10.1016/j.ab.2007.10.012
84. 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. Analytical Biochemistry 2012 425 2 117 119 2-s2.0-84859932176 10.1016/j.ab.2012.03.015
85. 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 10.1093/bioinformatics/ btt072
86. 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 3 3495 3506 10.3390/ijms15033495
87. Chen YW L. C., Guyon I., Nikravesh N., Gunn S., Zadeh L., Combining SVMs with various feature selection strategies. Feature Extraction 2006 Berlin, Germany Springer 315 324
88. Lin H., Ding H., Guo F.-B., Huang J., Prediction of subcellular location of mycobacterial protein using feature selection techniques. Molecular Diversity 2010 14 4 667 671 2-s2.0-78650178724 10.1007/s11030-009-9205-1
89. Chou K.-C., Cai Y.-D., Using functional domain composition and support vector machines for prediction of protein subcellular location. The Journal of Biological Chemistry 2002 277 48 45765 45769 2-s2.0-0037195776 10.1074/jbc.M204161200 (Pubitemid 35417553)
90. Cai Y.-D., Zhou G.-P., Chou K.-C., Support vector machines for predicting membrane protein types by using functional domain composition. Biophysical Journal 2003 84 5 3257 3263 2-s2.0-0038636605 (Pubitemid 36531773)
91. Cristianini N., Shawe-Taylor J., An Introduction of Support Vector Machines and Other Kernel-Based Learning Methodds 2000 Cambridge, UK Cambridge University Press
92. Chou K. C., Zhang C. T., Review: prediction of protein structural classes. Critical Reviews in Biochemistry and Molecular Biology 1995 30 4 275 349
93. Zhou G. P., Assa-Munt N., Some insights into protein structural class prediction. Proteins: Structure, Function and Genetics 2001 44 1 57 59 2-s2.0-0035400226 10.1002/prot.1071 (Pubitemid 32510584)
94. 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. Molecular BioSystems 2012 8 2 629 641 2-s2.0-84855641685 10.1039/c1mb05420a
95. 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 2-s2.0-79953316878 10.1371/journal.pone.0018258 e18258
96. Chou K.-C., Using subsite coupling to predict signal peptides. Protein Engineering 2001 14 2 75 79 2-s2.0-0035030201 (Pubitemid 32397553)
97. Chou K. C., Prediction of signal peptides using scaled window. Peptides 2001 22 12 1973 1979 2-s2.0-0035748859
98. Chou K. C., Shen H. B., Review: recent advances in developing web-servers for predicting protein attributes. Natural Science 2009 1 2 63 92 10.4236/ns.2009.12011
99. Chou K. C., Review: prediction of human immunodeficiency virus protease cleavage sites in proteins. Analytical Biochemistry 1996 233 1 1 14 10.1006/abio.1996.0001 (Pubitemid 26036360)