Prediction of Golgi-resident protein types using general form of Chou's pseudo-amino acid compositions: Approaches with minimal redundancy maximal relevance feature selection

Journal of Theoretical Biology

Volumn 402, Issue , 2016, Pages 38-44

  Jiao, Ya Sen ^a   Du, Pu Feng ^a  

^a TIANJIN UNIVERSITY   (China)

Author keywords

Feature selection; Golgi resident proteins; MRMR; PseAAC; PSPCP; SVM

Indexed keywords

CELL PROTEIN; GOLGI RESIDENT PROTEIN; UNCLASSIFIED DRUG; AMINO ACID;

AMINO ACID; CELL ORGANELLE; PHYSICOCHEMICAL PROPERTY; PREDICTION; PROTEIN; SUPPORT VECTOR MACHINE;

ALGORITHM; AMINO ACID COMPOSITION; AMINO ACID SEQUENCE; ARTICLE; CELLULAR DISTRIBUTION; COMPUTER MODEL; GOLGI COMPLEX; JACKKNIFE TEST; MATHEMATICAL COMPUTING; MINIMUM REDUNDANCY MAXIMAL RELEVANCE FEATURE SELECTION; PREDICTION; PRIORITY JOURNAL; PSEUDO AMINO ACID COMPOSITION; SUPPORT VECTOR MACHINE; BIOLOGY; CHEMISTRY; METABOLISM; PROCEDURES; PROTEIN DATABASE;

ALGORITHMS; AMINO ACIDS; COMPUTATIONAL BIOLOGY; DATABASES, PROTEIN; GOLGI APPARATUS;

EID: 84965002305     PISSN: 00225193     EISSN: 10958541     Source Type: Journal    
DOI: 10.1016/j.jtbi.2016.04.032     Document Type: Article

References (77)

1. Ali F., Hayat M. Classification of membrane protein types using voting feature interval in combination with Chou's pseudo amino acid composition. J. Theor. Biol. 2015, 384:78-83. 10.1016/j.jtbi.2015.07.034.
2. Altschul S.F., Madden T.L., Schäffer A.A., Zhang J., Zhang Z., Miller W., et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 1997, 25:3389-3402.
3. Cao D.-S., Xu Q.-S., Liang Y.-Z. Propy: a tool to generate various modes of Chou's PseAAC. Bioinformatics 2013, 29:960-962. 10.1093/bioinformatics/btt072.
4. Chang C.-C., Lin C.-J. LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2011, 2(27):1-27. 10.1145/1961189.1961199.
5. Chen L., Zeng W.-M., Cai Y.-D., Feng K.-Y., Chou K.-C. Predicting anatomical therapeutic chemical (ATC) classification of drugs by integrating chemical-chemical interactions and similarities. PLoS One 2012, 7:e35254. 10.1371/journal.pone.0035254.
6. Chen W., Lin H., Chou K.-C. Pseudo nucleotide composition or PseKNC: an effective formulation for analyzing genomic sequences. Mol Biosyst. 2015, 11:2620-2634. 10.1039/c5mb00155b.
7. Chen W., Feng P.-M., Lin H., Chou K.-C. iRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition. Nucleic Acids Res. 2013, 41:e68. 10.1093/nar/gks1450.
8. Chen W., Lei T.-Y., Jin D.-C., Lin H., Chou K.-C. PseKNC: a flexible web server for generating pseudo K-tuple nucleotide composition. Anal. Biochem. 2014, 456:53-60. 10.1016/j.ab.2014.04.001.
9. Chen W., Ding H., Feng P., Lin H., Chou K.-C. iACP: a sequence-based tool for identifying anticancer peptides. Oncotarget 2016, 10.18632/oncotarget.7815.
10. Chen W., Feng P., Ding H., Lin H., Chou K.-C. Using deformation energy to analyze nucleosome positioning in genomes. Genomics 2016, 107:69-75. 10.1016/j.ygeno.2015.12.005.
11. Chen W., Zhang X., Brooker J., Lin H., Zhang L., Chou K.-C. PseKNC-General: a cross-platform package for generating various modes of pseudo nucleotide compositions. Bioinformatics 2015, 31:119-120. 10.1093/bioinformatics/btu602.
12. Chou K.C. Prediction of protein cellular attributes using pseudo-amino acid composition. Proteins 2001, 43:246-255.
13. Chou K.C., Zhang C.T. Prediction of protein structural classes. Crit. Rev. Biochem. Mol. Biol. 1995, 30:275-349. 10.3109/10409239509083488.
14. Chou K.-C. Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes. Bioinformatics 2005, 21:10-19. 10.1093/bioinformatics/bth466.
15. Chou K.-C. Pseudo amino acid composition and its applications in bioinformatics, proteomics and system biology. Curr. Proteom. 2009, 6:262-274. 10.2174/157016409789973707.
16. Chou K.-C. Some remarks on protein attribute prediction and pseudo amino acid composition. J. Theor. Biol. 2011, 273:236-247. 10.1016/j.jtbi.2010.12.024.
17. Chou K.-C. Some remarks on predicting multi-label attributes in molecular biosystems. Mol. Biosyst. 2013, 9:1092-1100. 10.1039/c3mb25555g.
18. Chou K.-C. Impacts of bioinformatics to medicinal chemistry. Med. Chem. 2015, 11:218-234.
19. Chou K.-C., Cai Y.-D. Predicting protein quaternary structure by pseudo amino acid composition. Proteins 2003, 53:282-289. 10.1002/prot.10500.
20. Chou K.-C., Shen H.-B. Cell-PLoc: a package of Web servers for predicting subcellular localization of proteins in various organisms. Nat. Protoc. 2008, 3:153-162. 10.1038/nprot.2007.494.
21. Chou K.-C., Shen H.-B. Plant-mPLoc: a top-down strategy to augment the power for predicting plant protein subcellular localization. PLoS One 2010, 5:e11335. 10.1371/journal.pone.0011335.
22. 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. 10.1371/journal.pone.0018258.
23. 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, 8:629-641. 10.1039/C1MB05420A.
24. 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. 10.1016/j.jtbi.2014.09.029.
25. Ding C., Peng H. Minimum redundancy feature selection from microarray gene expression data. J Bioinform. Comput. Biol. 2005, 3:185-205.
26. Ding H., Liu L., Guo F.-B., Huang J., Lin H. Identify Golgi protein types with modified mahalanobis discriminant algorithm and pseudo amino acid composition. Protein Pept. Lett. 2011, 18:58-63.
27. Ding H., Guo S.-H., Deng E.-Z., Yuan L.-F., Guo F.-B., Huang J., et al. Prediction of Golgi-resident protein types by using feature selection technique. Chemom. Intell. Lab. Syst. 2013, 124:9-13. 10.1016/j.chemolab.2013.03.005.
28. Du P., Li Y. Prediction of protein submitochondria locations by hybridizing pseudo-amino acid composition with various physicochemical features of segmented sequence. BMC Bioinformatics 2006, 7:518. 10.1186/1471-2105-7-518.
29. Du P., Yu Y. SubMito-PSPCP: predicting protein submitochondrial locations by hybridizing positional specific physicochemical properties with pseudoamino acid compositions. BioMed. Res. Int. 2013, 2013:1-7. 10.1155/2013/263829.
30. Du P., Cao S., Li Y. SubChlo: predicting protein subchloroplast locations with pseudo-amino acid composition and the evidence-theoretic K-nearest neighbor (ET-KNN) algorithm. J. Theor. Biol. 2009, 261:330-335. 10.1016/j.jtbi.2009.08.004.
31. Du P., Li T., Wang X. Recent progress in predicting protein sub-subcellular locations. Expert Rev. Proteom. 2011, 8:391-404. 10.1586/epr.11.20.
32. Du P., Gu S., Jiao Y. PseAAC-General: fast building various modes of general form of Chou's pseudo-amino acid composition for large-scale protein datasets. Int. J. Mol. Sci. 2014, 15:3495-3506. 10.3390/ijms15033495.
33. Du P., Wang X., Xu C., Gao Y. PseAAC-builder: a cross-platform stand-alone program for generating various special Chou's pseudo-amino acid compositions. Anal. Biochem. 2012, 425:117-119. 10.1016/j.ab.2012.03.015.
34. Fujita Y., Ohama E., Takatama M., Al-Sarraj S., Okamoto K. Fragmentation of Golgi apparatus of nigral neurons with alpha-synuclein-positive inclusions in patients with Parkinson's disease. Acta Neuropathol. 2006, 112:261-265. 10.1007/s00401-006-0114-4.
35. Gonatas N.K., Gonatas J.O., Stieber A. The involvement of the Golgi apparatus in the pathogenesis of amyotrophic lateral sclerosis, Alzheimer's disease, and ricin intoxication. Histochem. Cell Biol. 1998, 109:591-600.
36. Guo S.-H., Deng E.-Z., Xu L.-Q., Ding H., Lin H., Chen W., et al. iNuc-PseKNC: a sequence-based predictor for predicting nucleosome positioning in genomes with pseudo k-tuple nucleotide composition. Bioinformatics 2014, 30:1522-1529. 10.1093/bioinformatics/btu083.
37. Hu L., Huang T., Shi X., Lu W.-C., Cai Y.-D., Chou K.-C. Predicting functions of proteins in mouse based on weighted protein-protein interaction network and protein hybrid properties. PLoS One 2011, 6:e14556. 10.1371/journal.pone.0014556.
38. Huang T., Chen L., Cai Y.-D., Chou K.-C. Classification and analysis of regulatory pathways using graph property, biochemical and physicochemical property, and functional property. PLoS One 2011, 6:e25297. 10.1371/journal.pone.0025297.
39. 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:e34460. 10.1371/journal.pone.0034460.
40. Huang T., Niu S., Xu Z., Huang Y., Kong X., Cai Y.-D., et al. Predicting transcriptional activity of multiple site p53 mutants based on hybrid properties. PLoS One 2011, 6:e22940. 10.1371/journal.pone.0022940.
41. 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. 10.1016/j.jtbi.2015.04.011.
42. 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. 10.1016/j.ab.2015.12.009.
43. 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. 10.1016/j.jtbi.2016.01.020.
44. Jia J., Liu Z., Xiao X., Liu B., Chou K.-C. iPPBS-Opt: a sequence-based ensemble classifier for identifying protein-protein binding sites by optimizing imbalanced training datasets. Molecules 2016, 21. 10.3390/molecules21010095.
45. Jiao Y.-S., Du P.-F. Predicting Golgi-resident protein types using pseudo amino acid compositions: Approaches with positional specific physicochemical properties. J. Theor. Biol. 2016, 391:35-42. 10.1016/j.jtbi.2015.11.009.
46. Jiao, Y., Du, P. , Su, X., Predicting Golgi-resident proteins in plants by incorporating N-terminal transmembrane domain information in the general form of Chou's pseudoamino acid compositions. In: 8th International Conference on Systems Biology (ISB), 2014: pp. 226-229. http://doi:10.1109/ISB.2014.6990759.
47. Kawashima S., Pokarowski P., Pokarowska M., Kolinski A., Katayama T., Kanehisa M. AAindex: amino acid index database, progress report 2008. Nucleic Acids Res. 2008, 36. 10.1093/nar/gkm998.
48. Kumar R., Srivastava A., Kumari B., Kumar M. Prediction of β-lactamase and its class by Chou's pseudo-amino acid composition and support vector machine. J. Theor. Biol. 2015, 365:96-103. 10.1016/j.jtbi.2014.10.008.
49. 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:e39308. 10.1371/journal.pone.0039308.
50. 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:e33393. 10.1371/journal.pone.0033393.
51. 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, 9:634. 10.1039/c3mb25466f.
52. Liu B., Long R., Chou K.-C. iDHS-EL: identifying DNase I hypersensitive-sites by fusing three different modes of pseu-do nucleotide composition into an ensemble learning framework. Bioinformatics 2016, 10.1093/bioinformatics/btw186.
53. Liu B., Liu F., Fang L., Wang X., Chou K.-C. repDNA: a python package to generate various modes of feature vectors for DNA sequences by incorporating user-defined physicochemical properties and sequence-order effects. Bioinformatics 2015, 31:1307-1309. 10.1093/bioinformatics/btu820.
54. Liu B., Liu F., Fang L., Wang X., Chou K.-C. repRNA: a web server for generating various feature vectors of RNA sequences. Mol. Genet. Genom. 2015, 10.1007/s00438-015-1078-7.
55. 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. 10.1016/j.jtbi.2015.08.025.
56. Liu B., Fang L., Liu F., Wang X., Chou K.-C. iMiRNA-PseDPC: microRNA precursor identification with a pseudo distance-pair composition approach. J. Biomol. Struct. Dyn. 2015, 1-13. 10.1080/07391102.2015.1014422.
57. Liu B., Liu F., Wang X., Chen J., Fang L., Chou K.-C. 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. 10.1093/nar/gkv458.
58. Liu B., Xu J., Fan S., Xu R., Zhou J., Wang X. PseDNA-Pro: DNA-binding protein identification by combining Chou's PseAAC and physicochemical distance transformation. Mol. Inf. 2015, 34:8-17. 10.1002/minf.201400025.
59. Liu B., Fang L., Long R., Lan X., Chou K.-C. iEnhancer-2L: a two-layer predictor for identifying enhancers and their strength by pseudo k-tuple nucleotide composition. Bioinformatics 2016, 32:362-369. 10.1093/bioinformatics/btv604.
60. 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. 10.1016/j.ab.2014.12.009.
61. 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. 10.1016/j.ab.2015.12.017.
62. Peng Hanchuan, Long Fuhui, Ding C. Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell. 2005, 27:1226-1238. 10.1109/TPAMI.2005.159.
63. Shen H.-B., Chou K.-C. Virus-PLoc: a fusion classifier for predicting the subcellular localization of viral proteins within host and virus-infected cells. Biopolymers 2007, 85:233-240. 10.1002/bip.20640.
64. Shen H.-B., Chou K.-C. Nuc-PLoc: a new web-server for predicting protein subnuclear localization by fusing PseAA composition and PsePSSM. Protein Eng. Des. Sel. 2007, 20:561-567. 10.1093/protein/gzm057.
65. Shen H.-B., Chou K.-C. PseAAC: a flexible web server for generating various kinds of protein pseudo amino acid composition. Anal. Biochem. 2008, 373:386-388. 10.1016/j.ab.2007.10.012.
66. Shen H.-B., Yang J., Chou K.-C. Euk-PLoc: an ensemble classifier for large-scale eukaryotic protein subcellular location prediction. Amino Acids 2007, 33:57-67. 10.1007/s00726-006-0478-8.
67. Shen H.-B., Yi D.-L., Yao L.-X., Yang J., Chou K.-C. Knowledge-based computational intelligence development for predicting protein secondary structures from sequences. Expert Rev. Proteom. 2008, 5:653-662. 10.1586/14789450.5.5.653.
68. van Dijk A.D.J., Bosch D., ter Braak C.J.F., van der Krol A.R., van Ham R.C.H.J. Predicting sub-Golgi localization of type II membrane proteins. Bioinformatics 2008, 24:1779-1786. 10.1093/bioinformatics/btn309.
69. Vapnik V.N. The Nature of Statistical Learning Theory 2000, Springer, New York, New York, NY, (accessed 15.12.15). http://link.springer.com/10(.1007/978-1-4757-326)4-1.
70. Wang P., Hu L., Liu G., Jiang N., Chen X., Xu J., et al. Prediction of antimicrobial peptides based on sequence alignment and feature selection methods. PLoS One 2011, 6:e18476. 10.1371/journal.pone.0018476.
71. Wu Z.-C., Xiao X., Chou K.-C. iLoc-Plant: a multi-label classifier for predicting the subcellular localization of plant proteins with both single and multiple sites. Mol. Biosyst. 2011, 7:3287. 10.1039/c1mb05232b.
72. Wu Z.-C., Xiao X., Chou K.-C. iLoc-Gpos: a multi-layer classifier for predicting the subcellular localization of singleplex and multiplex gram-positive bacterial proteins. Protein Peptide Lett. 2012, 19:4-14. 10.2174/092986612798472839.
73. 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, 284:42-51. 10.1016/j.jtbi.2011.06.005.
74. 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, 436:168-177. 10.1016/j.ab.2013.01.019.
75. Xiao X., Min J.-L., Lin W.-Z., Liu Z., Cheng X., Chou K.-C. iDrug-Target: predicting the interactions between drug compounds and target proteins in cellular networking via benchmark dataset optimization approach. J. Biomol. Struct. Dyn. 2015, 33:2221-2233. 10.1080/07391102.2014.998710.
76. Xu Y., Chou K.-C. Recent progress in predicting posttranslational modification sites in proteins. Curr. Top. Med. Chem. 2016, 16:591-603.
77. Zheng L.-L., Li Y.-X., Ding J., Guo X.-K., Feng K.-Y., Wang Y.-J., et al. A comparison of computational methods for identifying virulence factors. PLoS One 2012, 7:e42517. 10.1371/journal.pone.0042517.