Imbalanced multi-modal multi-label learning for subcellular localization prediction of human proteins with both single and multiple sites

PLoS ONE

Volumn 7, Issue 6, 2012, Pages

  He, Jianjun ^a   Gu, Hong ^a   Liu, Wenqi ^a  

^a DALIAN UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURACY; ALGORITHM; ARTICLE; CELLULAR DISTRIBUTION; CLASSIFIER; COMPUTER PREDICTION; GAUSSIAN PROCESS; INFORMATION SERVICE; INTERMETHOD COMPARISON; INTERNET; MATHEMATICAL COMPUTING; NUCLEOTIDE SEQUENCE; PROTEIN LOCALIZATION; ARTIFICIAL INTELLIGENCE; BIOLOGICAL MODEL; HUMAN; METABOLISM; PHYSIOLOGY; PROTEIN TRANSPORT;

PROTEIN;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; HUMANS; MODELS, BIOLOGICAL; PROTEIN TRANSPORT; PROTEINS;

EID: 84862027781     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0037155     Document Type: Article

References (75)

1. Ma J, Gu H, (2010) A novel method for predicting protein subcellular localization based on pseudo amino acid composition. BMB Reports 43: 670 676.
2. Chou KC, Shen HB, (2007) Recent progress in protein subcellular location prediction. Analytical Biochemistry 370: 1 16.
3. Imai K, Nakai K, (2010) Prediction of subcellular locations of proteins: where to proceed? Proteomics 10: 3970 3983.
4. He Z, Zhang J, Shi XH, Hu LL, Kong X, (2010) Predicting drugtarget interaction networks based on functional groups and biological features. PLoS ONE 5: e9603.
5. Huang T, Niu S, Xu Z, Huang Y, Kong X, (2011) Predicting transcriptional activity of multiple site p53 mutants based on hybrid properties. PLoS ONE 6: e22940.
6. Hu LL, Huang T, Cai YD, Chou KC, (2011) Prediction of body fluids where proteins are secreted into based on protein interaction network. PLoS ONE 6: e22989.
7. Huang T, Shi XH, Wang P, He Z, Feng KY, (2010) Analysis and prediction of the metabolic stability of proteins based on their sequential features, subcellular locations and interaction networks. PLoS ONE 5: e10972.
8. Wang P, Hu L, Liu G, Jiang N, Chen X, (2011) Prediction of antimicrobial peptides based on sequence alignment and feature selection methods. PLoS ONE 6: e18476.
9. Lin WZ, Fang JA, Xiao X, Chou KC, (2011) iDNA-Prot: Identification of DNA binding proteins using random forest with grey model. PLoS ONE 6: e24756.
10. Huang T, Chen L, Cai YD, Chou KC, (2011) Classification and analysis of regulatory pathways using graph property, biochemical and physicochemical property, and functional property. PLoS ONE 6: e25297.
11. Chou KC, Shen HB, (2007) Signal-CF: a subsite-coupled and window-fusing approach for predicting signal peptides. Biochemical and Biophysical Research Communications 357: 633 640.
12. Chou KC, (1993) A vectorized sequence-coupling model for predicting HIV protease cleavage sites in proteins. Journal of Biological Chemistry 268: 16938 16948.
13. Chou KC, (1996) Review: Prediction of HIV protease cleavage sites in proteins. Analytical Biochemistry 233: 1 14.
14. Chen L, Feng KY, Cai YD, Chou KC, Li HP, (2010) Predicting the network of substrate-enzymeproduct triads by combining compound similarity and functional domain composition. BMC Bioinformatics 11: 293.
15. Chen L, Huang T, Shi XH, Cai YD, Chou KC, (2010) Analysis of protein pathway networks using hybrid properties. Molecules 15: 8177 8192.
16. Chou KC, Shen HB, (2008) ProtIdent: A web server for identifying proteases and their types by fusing functional domain and sequential evolution information. Biochemical and Biophysical Research Communications 376: 321 325.
17. Chou KC, Shen HB, (2007) MemType-2L: AWeb server for predicting membrane proteins and their types by incorporating evolution information through Pse-PSSM. Biochemical and Biophysical Research Communications 360: 339 345.
18. Chou KC, (2004) Review: Structural bioinformatics and its impact to biomedical science. Current Medicinal Chemistry 11: 2105 2134.
19. Nakashima H, Nishikawa K, (1994) Discrimination of intracellular and extracellular proteins using amino acid composition and residue-pair frequencies. Journal of Molecular Biology 238: 54 61.
20. Chou KC, Elrod DW, (1999) Protein subcellular location prediction. Protein Engineering 12: 107 118.
21. Emanuelsson O, Nielsen H, Brunak S, Heijne GV, (2000) Predicting subcellular localization of proteins based on their N-terminal amino acid sequence. Journal of Molecular Biology 300: 1005 1016.
22. Hua S, Sun Z, (2001) Support vector machine approach for protein subcellular localization prediction. Bioinformatics 17: 721 728.
23. Chou KC, Cai YD, (2002) Using functional domain composition and support vector machines for prediction of protein subcellular location. Journal of Biological Chemistry 277: 45765 45769.
24. Zhou GP, Doctor K, (2003) Subcellular location prediction of apoptosis proteins. Proteins: Structure, Function, and Genetics 50: 44 48.
25. Chen YL, Li QZ, (2007) Prediction of the subcellular location of apoptosis proteins. Journal of Theoretical Biology 245: 775 783.
26. Chou KC, (2001) Prediction of protein cellular attributes using pseudo amino acid composition. Proteins: Structure, Function, and Genetics 43: 246 255.
27. Li FM, Li QZ, (2008) Predicting protein subcellular location using Chou's pseudo amino acid composition and improved hybrid approach. Protein & Peptide Letters 15: 612 616.
28. Jiang X, Wei R, Zhang TL, Gu Q, (2008) Using the concept of Chou's pseudo amino acid composition to predict apoptosis proteins subcellular location: an approach by approximate entropy. Protein & Peptide Letters 15: 392 396.
29. Chen YL, Li QZ, (2007) Prediction of apoptosis protein subcellular location using improved hybrid approach and pseudo amino acid composition. Journal of Theoretical Biology 248: 377 381.
30. Liao B, Jiang JB, Zeng QG, Zhu W, (2011) Predicting apoptosis protein subcellular location with PseAAC by incorporating tripeptide composition. Protein & Peptide Letters 18: 1086 1092.
31. Liu T, Zheng X, Wang C, Wang J, (2010) Prediction of subcellular location of apoptosis proteins using pseudo amino acid composition: An approach from auto covariance transformation. Protein & Peptide Letters 17: 1263 1269.
32. Shi JY, Zhang SW, Pan Q, Zhou GP, (2008) Using pseudo amino acid composition to predict protein subcellular location: Approached with amino acid composition distribution. Amino Acids 35: 321 327.
33. Zeng YH, Guo YZ, Xiao RQ, Yang L, Yu LZ, (2009) Using the augmented Chou's pseudo amino acid composition for predicting protein submitochondria locations based on auto covariance approach. Journal of Theoretical Biology 259: 366 372.
34. Mei S, (2011) Multi-kernel transfer learning based on Chou's PseAAC formulation for protein submitochondria localization. Journal of Theoretical Biology 293: 121 130.
35. Lu Z, Szafron D, Greiner R, Lu P, Wishart DS, (2004) Predicting subcellular localization of proteins using machine-learned classifiers. Bioinformatics 20: 547 556.
36. Hoglund A, Donnes P, Blum T, Adolph HW, Kohlbacher O, (2006) MultiLoc: prediction of protein subcellular localization using N-terminal targeting sequences, sequence motifs and amino acid composition. Bioinformatics 22: 1158 1165.
37. Chou KC, Shen HB, (2008) Cell-PLoc: a package of Web servers for predicting subcellular localization of proteins in various organisms. Nature Protocols 3: 153 162.
38. Ma J, Liu W, Gu H, (2010) Using elman networks ensemble for protein subnuclear location prediction. International Journal of Innovative Computing, Information & Control 6: 5093 5103.
39. Tian J, Gu H, Liu W, Gao C, (2011) Robust prediction of protein subcellular localization combining PCA and WSVMs. Computers in Biology and Medicine 41: 648 652.
40. Foster LJ, de Hoog CL, Zhang Y, Zhang Y, Xie X, (2006) A mammalian organelle map by protein correlation profiling. Cell 125: 187 199.
41. Zhang S, Xia X, Shen J, Zhou Y, Sun Z, (2008) DBMLoc: a Database of proteins with multiple subcellular localizations. BMC Bioinformatics 9: 127.
42. Scott MS, Thomas DY, Hallett MT, (2004) Predicting subcellular localization via protein motif co-occurrence. Genome Research 14: 1957 1966.
43. Chou KC, Cai YD, (2005) Predicting protein localization in budding Yeast. Bioinformatics 21: 944 950.
44. Chou KC, Shen HB, (2007) Euk-mPLoc: a fusion classifier for large-scale eukaryotic protein subcellular location prediction by incorporating multiple sites. Journal of Proteome Research 6: 1728 1734.
45. Shen HB, Chou KC, (2007) Hum-mPLoc: an ensemble classifier for large-scale human protein subcellular location prediction by incorporating samples with multiple sites. Biochemical and Biophysical Research Communications 355: 1006 1011.
46. Chou KC, Shen HB, (2010) A new method for predicting the subcellular localization of eukaryotic proteins with both single and multiple sites: Euk-mPLoc 2.0. PloS ONE 5: e9931.
47. Shen HB, Chou KC, (2009) A top-down approach to enhance the power of predicting human protein subcellular localization: Hum-mPLoc 2.0. Analytical Biochemistry 394: 269 274.
48. Chou KC, Shen HB, (2010) Plant-mPLoc: a top-down strategy to augment the power for predicting plant protein subcellular localization. PloS ONE 5: e11335.
49. Shen HB, Chou KC, (2010) Virus-mPLoc: a fusion classifier for viral protein subcellular location prediction by incorporating multiple sites. Journal of Biomolecular Structure & Dynamics 28: 175 186.
50. Lee KY, Kim DW, Na DK, Lee KH, Lee D, (2006) PLPD: reliable protein localization prediction from imbalanced and overlapped datasets. Nucleic Acids Research 34: 4655 4666.
51. Briesemeister S, Rahnenfuhrer J, Kohlbacher O, (2010) Going from where to why-interpretable prediction of protein subcellular localization. Bioinformatics 26: 1232 1238.
52. Lin HN, Chen CT, Sung TY, Ho SY, Hsu WL, (2009) Protein subcellular localization prediction of eukaryotes using a knowledge-based approach. BMC Bioinformatics 10: S8.
53. Chou KC, Wu ZC, Xiao X, (2011) iLoc-Euk: A Multi-Label Classifier for Predicting the Subcellular Localization of Singleplex and Multiplex Eukaryotic Proteins. PloS ONE 6: e18258.
54. Xiao X, Wu ZC, Chou KC, (2011) A Multi-Label Classifier for Predicting the Subcellular Localization of Gram-Negative Bacterial Proteins with Both Single and Multiple Sites. PloS ONE 6: e20592.
55. Wu ZC, Xiao X, Chou KC, (2011) iLoc-Plant: A multi-label classifier for predicting the subcellular localization of plant proteins with both single and multiple sites. Molecular BioSystems 7: 3287 3297.
56. Xiao X, Wu ZC, Chou KC, (2011) iLoc-Virus: A multi-label learning classifier for identifying the subcellular localization of virus proteins with both single and multiple sites. Journal of Theoretical Biology 284: 42 51.
57. Wu ZC, Xiao X, Chou KC, (2011) iLoc-Gpos: A multi-layer classifier for predicting the subcellular localization of singleplex and multiplex gram-positive bacterial proteins. Protein & Peptide Letters, DOI: BSP/PPL/E pub/0380 [pii].
58. Chou KC, Wu ZC, Xiao X, (2012) iLoc-Hum: Using accumulation-label scale to predict subcellular locations of human proteins with both single and multiple sites. Molecular BioSystems 8: 629 641.
59. Chou KC, (2011) Some remarks on protein attribute prediction and pseudo amino acid composition. Journal of Theoretical Biology 273: 236 247.
60. Rasmussen CE, Williams KI, (2006) Gaussian process for machine learning. The MIT press.
61. Mei S, Fei W, Zhou S, (2011) Gene ontology based transfer learning for protein subcellular localization. BMC Bioinformatics 12: 44.
62. Shen HB, Chou KC, (2008) PseAAC: A flexible web server for generating various kinds of protein pseudo amino acid composition. Analytical Biochemistry 373: 386 388.
63. Yin J, Li T, Shen HB, (2011) Gaussian kernel optimization: Complex problem and a simple solution. Neurocomputing 74: 3816 3822.
64. Chou KC, Zhang CT, (1995) Review: Prediction of protein structural classes. Critical Reviews in Biochemistry and Molecular Biology 30: 275 349.
65. Chou KC, Shen HB, (2010) Cell-PLoc 2.0: An improved package of web-servers for predicting subcellular localization of proteins in various organisms. Natural Science 2: 1090 1103.
66. Esmaeili M, Mohabatkar H, Mohsenzadeh S, (2010) Using the concept of Chou's pseudo amino acid composition for risk type prediction of human papillomaviruses. Journal of Theoretical Biology 263: 203 209.
67. Georgiou DN, Karakasidis TE, Nieto JJ, Torres A, (2009) 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 257: 17 26.
68. Mohabatkar H, (2010) Prediction of cyclin proteins using Chou's pseudo amino acid composition. Protein & Peptide Letters 17: 1207 1214.
69. Mohabatkar H, Mohammad Beigi M, Esmaeili A, (2011) Prediction of GABA(A) receptor proteins using the concept of Chou's pseudo-amino acid composition and support vector machine. Journal of Theoretical Biology 281: 18 23.
70. Hu L, Huang T, Shi X, Lu WC, Cai YD, (2011) Predicting functions of proteins in mouse based on weighted protein-protein interaction network and protein hybrid properties. PLoS ONE 6: e14556.
71. Wang P, Xiao X, Chou KC, (2011) NR-2L: A two-level predictor for identifying nuclear receptor subfamilies based on sequence-derived features. PLoS ONE 6: e23505.
72. Xiao X, Wang P, Chou KC, (2011) GPCR-2L: Predicting G protein-coupled receptors and their types by hybridizing two different modes of pseudo amino acid compositions. Molecular BioSystems 7: 911 919.
73. Schapire RE, Singer Y, (2000) BoosTexter: A boosting-based system for text categorization. Machine Learning 39: 135 168.
74. Tsoumakas G, Katakis I, (2007) Multi-label classification: An overview. International Journal of Data Warehousing and Mining 3: 1 13.
75. Chou KC, Shen HB, (2009) Review: recent advances in developing web-servers for predicting protein attributes. Natural Science 2: 63 92.