Protein function classification via support vector machine approach

Mathematical Biosciences

Volumn 185, Issue 2, 2003, Pages 111-122

  Cai, C Z ^a,b   Wang, W L ^a   Sun, L Z ^b   Chen, Y Z ^b  

^a CHONGQING UNIVERSITY   (China)

^b NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

Author keywords

Classification; Drug absorption protein; Drug distribution protein; Drug excretion protein; Drug metabolizing enzyme; Protein homodimer; RNA binding protein; Support vector machine

Indexed keywords

ABSORPTION; DIMERS; RNA;

SUPPORT VECTOR MACHINES (SVM);

PROTEINS;

DIMER; DRUG METABOLIZING ENZYME; PROTEIN; RNA BINDING PROTEIN;

PROTEIN;

ACCURACY; CLASSIFICATION; DRUG ABSORPTION; DRUG DISTRIBUTION; DRUG EXCRETION; DRUG METABOLISM; LINEAR SYSTEM; NONLINEAR SYSTEM; PREDICTION; PROTEIN FUNCTION; REVIEW; SUPPORT VECTOR MACHINE;

EID: 0041736652     PISSN: 00255564     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0025-5564(03)00096-8     Document Type: Article

References (46)

1. J. Downward, The ins and outs of signalling, Nature 411 (2001) 759.
2. J.W. Lengeler, Metabolic networks: a signal-oriented approach to cellular models, Biol. Chem. 381 (2000) 911.
3. H. Siomi, G. Dreyfuss, RNA-binding proteins as regulators of gene expression, Curr. Opin. Genet. Dev. 7 (1997) 345.
4. D.E. Draper, Themes in RNA-protein recognition, J. Mol. Biol. 293 (1999) 255.
5. E.V. Koonin, R.L. Tatusov, M.Y. Galperin, Beyond complete genomes: from sequence to structure and function, Curr. Opin. Struc. Biol. 8 (1998) 355.
6. J.S. Fetrow, J. Skolnick, Method for prediction of protein function from sequence using the sequence-to-structure-to-function paradigm with application to glutaredoxins/thioredoxins and T-1 ribonucleases, J. Mol. Biol. 281 (1998) 949.
7. V.N. Vapnik, The Nature of Statistical Learning Theory, second ed., Springer, New York, 1999.
8. C.J.C. Burges, A tutorial on support vector machine for pattern recognition, Data Min. Knowl. Disc. 2 (1998) 121.
9. J.R. Bock, D.A. Gough, Predicting protein-protein interactions from primary structure, Bioinformatics 17 (2001) 455.
10. C.H.Q. Ding, I. Dubchak, Multi-class protein fold recognition using support vector machines and neural networks, Bioinformatics 17 (2001) 349.
11. Y.D. Cai, X.J. Liu, X.B. Xu, K.C. Chou, Support vector machines for the classification and prediction of β-turn types, J. Peptide Sci. 8 (2002) 297.
12. Z. Yuan, K. Burrage, J.S. Mattick, Prediction of protein solvent accessibility using support vector machines, Proteins 48 (2002) 566.
13. S.J. Hua, Z.R. Sun, A novel method of protein secondary structure prediction with high segment overlap measure: support vector machine approach, J. Mol. Biol. 308 (2001) 397.
14. Y.D. Cai, X.J. Liu, X.B. Xu, K.C. Chou, Prediction of protein structural classes by support vector machines, Comput. Chem. 26 (2002) 293.
15. O. de Vel, A. Anderson, M. Corney, G. Mohay, Mining e-mail content for author identification forensics, Sigmod Record 30 (2001) 55.
16. K.I. Kim, K. Jung, S.H. Park, H.J. Kim, Support vector machine-based text detection in digital video, Pattern Recogn. 34 (2001) 527.
17. H. Drucker, D.H. Wu, V.N. Vapnik, Support vector machine for spam categorization, IEEE T. Neur. Network 10 (1999) 1048.
18. S. Tong, D. Koller, Support vector machine active learning with applications to text classification, J. Mach. Learn. Res. 2 (2001) 45.
19. Z.Y. Li, S.W. Tang, S.C. Yan, Multi-class SVM classifier based on pairwise coupling, Lect. Notes Comput. Sci. 2388 (2002) 321.
20. N. Thubthong, B. Kijsirikul, Support vector machines for Thai phoneme recognition, Int. J. Uncertain. Fuzz. 9 (2001) 803.
21. M. Gordan, C. Kotropoulos, I. Pitas, A temporal network of support vector machine classifiers for the recognition of visual speech, Lect. Notes Anal. Intell. 2308 (2002) 355.
22. S. Ben-Yacoub, Y. Abdeljaoued, E. Mayoraz, Fusion of face and speech data for person identity verification, IEEE Trans. Neural Network 10 (1999) 1065.
23. C.Y. Wu, C. Liu, J. Zhou, Eyeglasses verification by support vector machine, Lect. Notes Comput. Sci. 2195 (2001) 1126.
24. Y.J. Wang, C.S. Chua, Y.K. Ho, Facial feature detection and face recognition from 2D and 3D images, Pattern Recogn. Lett. 23 (2002) 1191.
25. J.W. Hsieh, L.W. Huang, Y.S. Huang, Multiple-person tracking system for content analysis, Lect. Notes Comput. Sci. 2195 (2001) 897.
26. C. Papageorgiou, T. Poggio, A trainable system for object detection, Int. J. Comput. Vis. 38 (2000) 15.
27. R.E. Karlsen, D.J. Gorsich, G.R. Gerhart, Target classification via support vector machines, Opt. Eng. 39 (2000) 704.
28. Q. Zhao, J.C. Principe, V.L. Brennan, D.X. Xu, Z. Wang, Synthetic aperture radar automatic target recognition with three strategies of learning and representation, Opt. Eng. 39 (2000) 1230.
29. V.V. Gavrishchaka, S.B. Ganguli, Support vector machine as an efficient tool for high-dimensional data processing: application to substorm forecasting, J. Geophys. Res. 106 (2001) 29911.
30. S.Y. Liong, C. Sivapragasam, Flood stage forecasting with support vector machines, J. Am. Water Resour. As. 38 (2002) 173.
31. I. Guyon, J. Weston, S. Barnhill, V. Vapnik, Gene selection for cancer classification using support vector machines, Mach. Learn. 46 (2002) 389.
32. H.A. Fritsche, Tumor markers and pattern recognition analysis: a new diagnostic tool for cancer, J. Clin. Ligand Assay 25 (2002) 11.
33. L. Bao, Z.R. Sun, Identifying genes related to drug anticancer mechanisms using support vector machine, FEBS Lett. 521 (2002) 109.
34. S. Ramaswamy, P. Tamayo, R. Rifkin, et al., Multiclass cancer diagnosis using tumor gene expression signatures, Proc. Natl. Acad. Sci. USA 98 (2001) 15149.
35. K. Chan, T.W. Lee, P.A. Sample, M.H. Goldbaum, R.N. Weinreb, T.J. Sejnowski, Comparison of machine learning and traditional classifiers in glaucoma diagnosis, IEEE Trans. Biomed. Eng. 49 (2002) 963.
36. T.S. Furey, N. Cristianini, N. Duffy, D.W. Bednarski, M. Schummer, D. Haussler, Support vector machine classification and validation of cancer tissue samples using microarray expression data, Bioinformatics 16 (2000) 906.
37. P. Pavlidis, J. Weston, J.S. Cai, W.S. Noble, Learning gene functional classifications from multiple data types, J. Comput. Biol. 9 (2002) 401.
38. M.P.S. Brown, W.N. Grundy, D. Lin, N. Cristianini, C.W. Sugnet, T.S. Furey, M. Ares Jr., D. Haussler, Knowledge-based analysis of microarray gene expression data by using support vector machines, Proc. Nat. Acad. Sci. USA 97 (2000) 262.
39. R. Burbidge, M. Trotter, B. Buxton, S. Holden, Drug design by machine learning: support vector machines for pharmaceutical data analysis, Comput. Chem. 26 (2001) 5.
40. Y.D. Cai, X.J. Liu, X.B. Xu, K.C. Chou, Support vector machines for predicting HIV protease cleavage sites in protein, J. Comput. Chem. 23 (2002) 267.
41. N. Cristianini, J. Shawe-Taylor, An Introduction to Support Vector Machines, Cambridge University, Cambridge, 2000.
42. P. Baldi, S. Brunak, Y. Chauvin, C.A.F. Anderson, H. Nielsen, Assessing the accuracy of prediction algorithms for classification: an overview, Bioinformatics 16 (2000) 412.
43. J.E. Roulston, Screening with tumor markers, Mol. Biotechnol. 20 (2002) 153.
44. C.Z. Cai, W.L. Wang, Y.Z. Chen, Support vector machine classification of physical and biological datasets, Int. J. Mod. Phys. C 14 (5) (2003) in press.
45. S. Jones, J.M. Thornton, Principles of protein-protein interactions, Proc. Nat. Acad. Sci. USA 93 (1996) 13.
46. L.Z. Sun, Z.L. Ji, X. Chen, J.F. Wang, Y.Z. Chen, Absorption, distribution metabolism, and excretion-associated protein database, Clin. Pharmacol. Ther. 71 (2002) 405, Available: 〈 http://xin.cz3.nus.edu.sg/group/admeap/admeap.asp〉.