Semi-supervised protein subcellular localization

BMC Bioinformatics

Volumn 10, Issue SUPPL. 1, 2009, Pages

  Xu, Qian ^a   Hu, Derek Hao ^a   Xue, Hong ^a   Yu, Weichuan ^a   Yang, Qiang ^a  

^a HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Hong Kong)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURATE PREDICTION; GENERALIZATION ABILITY; LEARNING-BASED APPROACH; LOCATION INFORMATION; PREDICTION OF PROTEIN FUNCTION; PROTEIN SUBCELLULAR LOCALIZATION; SEMI-SUPERVISED LEARNING; SUBCELLULAR LOCALIZATIONS; SEMI- SUPERVISED LEARNING;

COMPUTATIONAL METHODS; PROTEINS; SUPERVISED LEARNING; SUPPORT VECTOR MACHINES; BIOINFORMATICS; LEARNING ALGORITHMS;

FORECASTING;

PROTEIN;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; BIOLOGICAL MODEL; BIOLOGY; CHEMISTRY; METHODOLOGY; PROTEIN DATABASE; PROTEIN TRANSPORT;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTATIONAL BIOLOGY; DATABASES, PROTEIN; MODELS, BIOLOGICAL; PROTEIN TRANSPORT; PROTEINS;

EID: 60849118651     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-10-S1-S47     Document Type: Conference Paper

References (56)

1. Shen HB Yang J Chou KC Euk-PLoc: An ensemble classifier for large-scale eukaryotic protein subcellular location prediction Amino Acids 2007, 33:57-67. 17235453
2. Su ECY Chiu HS Lo A Hwang JK yi Sung T Hsu WL Protein subcellular localization prediction based on compartment-specific feature and structure conservation BMC Bioinformatics 2007, 8:330. 2040162 17825110
3. Claros M Brunak S Heijne G Prediction of N-terminal protein sorting signals Current Opinion in Structural Biology 1997, 7:394-398. 9204282
4. Muskal S Kim S Predicting protein secondary structure-content-a tandem neural network approach J Mol Biol 1992, 225:713-727. 1602478
5. Bahar I Atilgan A Jernigan R Erman B Understanding the recognition of protein structural classes by amino acid composition Proteins 1997, 29:172-185. 9329082
6. Cai Y Zhou G Prediction of protein structural classes by neural network Biochimie 2000, 82:783-785. 11018296
7. Cai Y Liu X Xu X Zhou G Support vector machines for predicting protein structural class BMC Bioinformatics 2001, 2:3. 35360 11483157
8. Chou K A novel approach to predicting protein structural classes in a (20-1)-D amino-acid-composition space Proteins 1995, 21:319-344. 7567954
9. Chou K Prediction of protein structural classes and subcellular locations Curr Protein Pept Sci 2000, 1:171-208. 12369916
10. Chou K Zhang C A correlation-coefficient method to predicting protein-structural classes from amino-acid compositions Eur J Biochem 1992, 207:429-433. 1633801
11. Chou K Zhang C Predicting protein-folding types by distance functions that make allowances for amino-acid interactions J Biol Chem 1994, 269:22014-22020. 8071322
12. Luo R Feng Z Liu J Prediction of protein structural class by amino acid and polypeptide composition Eur J Biochem 2002, 269:4219-4225. 12199700
13. Zhang C Chou K Maggiora G Predicting protein structural classes from amino-acid-composition-application of fuzzy clustering Protein Eng 1995, 8:425-435. 8532663
14. Zhang C Chou K An optimization approach to predicting protein structural class from amino-acid-composition Protein Sci 1992, 1:401-408. 2142205 1304347
15. Zhou G Xu X Zhang C A weighting method for predicting protein structural class from amino-acid-composition Eur J Biochem 1992, 210:747-749. 1483458
16. Zhou G An intriguing controversy over protein structural class prediction J Protein Chem 1998, 17:729-738. 9988519
17. Zhou G Assa-Munt N Some insights into protein structural class prediction Proteins 2001, 44:57-59. 11354006
18. Chou K Prediction of protein cellular attributes using pseudo-amino acid composition Proteins 2001, 43:246-255. 11288174
19. Nakashima H Nishikawa K Ooi T The folding type of a protein is relevant to the amino-acid-composition J Biochem (Tokyo) 1986, 99:153-162.
20. Chou K Using pair-coupled amino acid composition to predict protein secondary structure content J Protein Chem 1999, 18:473-480. 10449044
21. Liu W Chou K Prediction of protein secondary structure content Protein Eng 1999, 12:1041-1050. 10611397
22. Reinhardt A Hubbard T Using neural networks for prediction of the subcellular location of proteins Nucleic Acid Res 1998, 26:2230-2236. 147531 9547285
23. Huang Y Li Y Prediction of protein subcellular locations using fuzzy k-NN method Bioinformatics 2004, 20:21-28. 14693804
24. Hua S Sun Z Support vector machine approach for protein subcellular localization prediction Bioinformatics 2001, 7:721-728.
25. Chine-Sheng Yu CJL Hwang JK Predicting subcellular localization of proteins for Gram-negative bacteria by support vector machine based on n-peptide compositions Protein Sci 2004, 13:1402-1406. 2286765 15096640
26. Guo T Hua S Ji X Sun Z DBSubLoc: Database of protein subcellular localization Nucleic Acids Res 2004, 32(Database issue):D122-D124. 308843 14681374
27. Bhasin M Raghava GPS ESLpred: SVM-based method for subcellular localization of eukaryotic proteins using dipeptide composition and PSI-BLAST Nucleic Acids Res 2004, 32:414-419.
28. Garg A Bhasin M Raghava GPS SVM-based method for subcellular localization of human proteins using amino acid compositions, their order and similarity search J Biol Chem
29. Xie D Li A Wang M Fan Z Feng H LOCSVMPSI: A web server for subcellular localization of eukaryotic proteins using SVM and profile of PSI-BLAST Nucleic Acids Research 2005, 33:105-110.
30. Nair R Rost B LOC3D: Annotate sub-cellular localization for protein structures Nucleic Acids Research 2003, 31:3337-3340. 168921 12824321
31. Gardy J Spencer C PSORT-B: Improving protein subcellular localization prediction for Gram-negative bacteria Nucleic Acids Res 2003, 31:3613-3617. 169008 12824378
32. Nakai K Kanehisa M Expert system for predicting protein localization sites in gram-negative bacteria Proteins 1991, 11:95-110. 1946347
33. Nair R Rost B Mimicking cellular sorting improves prediction of Subcellular Localization J Mol Biol 2005, 348:85-100. 15808855
34. PieAleoni A Martelli PL et al.: BaCelLo: a balanced subcellular localization predictor Bioinformatics 2006, 22:e408-e416. 16873501
35. Emanuelsson O Nielsen H et al.: Predicting subcellular localization of proteins based on their N-terminal amino acid sequence J Mol Biol 2000, 300:1005-1016. 10891285
36. JD B Jensen L Feature-based prediction of non-classical and leaderless protein secretion Protein Eng Des Sel 2004 17 349 356 15115854
37. NDir RPC et al.: NLSdb: database of nuclear localization signals Nucleic Acids Res 2003, 31:397-399. 165448 12520032
38. Horton P Park KJ Obayashi T Nakai K Protein Subcellular Localization Prediction with WoLF PSORT Proceedings of Asian Pacific Bioinformatics Conference: 13-16 Feb 2006; Taiwan 2006:39-48.
39. Lu Z et al.: Szafron D Predicting subcellular localization of proteins using machine-learned classifiers Bioinformatics 2004, 20:547-556. 14990451
40. Yu C Lin C Hwang J Predicting subcellular localization of proteins for Gram-negative bacteria by support vector machines based on n-peptide compositions Protein Sci 2004 13 1402 1406 2286765 15096640
41. Park K M K Prediction of Protein Subcellular Locations by Support Vectors Machines using Compositions of Amino Acids an Amino Acid Pairs Bioinformatics 2003, 19:1656-1663. 12967962
42. Wang J Sung WK Krishnan A Li KB Protein subcellular localization prediction for Gram-negative bacteria using amino acid subalphabets and a combination of multiple support vector machines Bioinformatics 2005, 6:174. 1190155 16011808
43. Weke package http://www.cs.waikato.ac.nz/ml/weka/
44. Zhu X Semi-Supervised Learning Literature Survey Tech Rep 1530 Computer Sciences, University of Wisconsin-Madison 2005
45. Nigam K McCallum A Thrun S Mitchell TM Text Classification from Labeled and Unlabeled Documents using EM Machine Learning 2000, 39:103-134.
46. Joachims T Transductive Inference for Text Classification using Support Vector Machines ICML 1999:200-209.
47. Joachims T Transductive Learning via Spectral Graph Partitioning ICML 2003:290-297.
48. Blum A Mitchell TM Combining Labeled and Unlabeled Sata with Co-Training COLT 1998:92-100.
49. Sarkar A Applying Co-Training Methods to Statistical Parsing NAACL 2001:1-8.
50. Levin A Viola PA Freund Y Unsupervised Improvement of Visual Detectors using Co-Training ICCV 2003:626-633.
51. Breiman L Bagging Predictors Machine Learning 1996, 24:123-140.
52. Breiman L Stacked Regressions Machine Learning 1996, 24:49-64.
53. Ho TK The Random Subspace Method for Constructing Decision Forests IEEE Trans Pattern Anal Mach Intell 1998, 20:832-844.
54. Breiman L Random Forests Machine Learning 2001, 45:5-32.
55. Freund Y Schapire RE A decision-theoretic generalization of on-line learning and an application to boosting EuroCOLT 1995:23-37.
56. Li M Zhou ZH Improve Computer-Aided Diagnosis With Machine Learning Techniques Using Undiagnosed Samples IEEE Transactions on Systems, Man, and Cybernetics, Part A 2007, 37:1088-1098.