Transmembrane segments prediction and understanding using support vector machine and decision tree

Expert Systems with Applications

Volumn 30, Issue 1, 2006, Pages 64-72

  He, Jieyue ^a,b   Hu, Hae Jin ^b   Harrison, Robert ^b   Tai, Phang C ^b   Pan, Yi ^b  

^a SOUTHEAST UNIVERSITY   (China)

^b GEORGIA STATE UNIVERSITY   (United States)

Author keywords

Decision tree; Expert systems; Prediction; Rule extraction; Support vector machine; Transmembrane; Understanding

Indexed keywords

BIOLOGICAL MEMBRANES; EXPERT SYSTEMS; LEARNING SYSTEMS; MOLECULAR STRUCTURE; PROTEINS; TREES (MATHEMATICS);

DECISION TREES; PREDICTION; RULE EXTRACTION; SUPPORT VECTOR MACHINES; TRANSMEMBRANES; UNDERSTANDING;

DECISION THEORY;

EID: 27844596815     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2005.09.045     Document Type: Article

References (37)

1. N. Acir, and C. Güzeli Automatic recognition of sleep spindles in EEG by using artificial neural networks Expert Systems with Applications 27 3 2004 451 458
2. R. Andrews, J. Diederich, and A. Tickle A survey and critique of techniques for extracting rules from trained artificial neural networks Knowledge-Based Systems 8 6 1995 373 389
3. N. Barakat, and J. Diederich Learning-based rule-extraction from support vector machine. The third conference on neuro-computing and evolving intelligence (NCEI'04) 2004
4. L. Breiman Classification and regression trees 1993 Chapman & Hall New York
5. Burges, C. J. C. (1998). SA tutorial on support vector machines for pattern recognition. Data mining and knowledge, 2(2), 121-167.
6. C.P. Chen, A. Kernytsky, and B. Rost Transmembrane helix predictions revisited Protein Science 11 2002 2774 2791
7. Y.H. Cho, J.K. Kim, and S.H. Kim A personalized recommender system based on web usage mining and decision tree induction Expert Systems with Applications 23 3 2002 329 342
8. Cortes, C., & Vapnik, V. (1995). Support-vector networks Machine learning (pp. 237-297), Vol. 20. Boston, MA: Kluwer Academic Publisher.
9. N. Cristianini, and J. Shawe-Taylor An introduction to support vector machines and other kernel-based learning methods 2000 Cambridge University Press Cambridge
10. D. Gorgevik, D. Cakmakov, and V. Radevski Handwritten digit recognition using statistical and rule-based decision fusion. IEEE MELECON, May 7-9 2002
11. S. Henikoff, and J.G. Henikoff Amino acid substitution matrices from protein blocks PNAS 89 1992 10915 10919
12. H. Hu, R. Harrison, P.C. Tai, and Y. Pan Transmembrane segments prediction with support vector machine based on high performance encoding schemes. Proceedings of the 2004 IEEE symposium on computational intelligence in bioinformatics and computational biology, October 7-8, La Jolla, CA, USA 2004
13. S. Hua, and Z. Sun A novel method of protein secondary structure prediction with high segment overlap measure: Support vector machine approach Journal of Molecular Biology 308 2001 397 407
14. Joachims, T. (2002). SVM light, [Online]. Available: http://www.cs. cornell.edu/people/tj/svm_light/.
15. D.T. Jones Protein secondary structure prediction based on position-specific scoring matrix Journal of Molecular Biology 292 1999 195 202
16. Kim, H. & Park, H. (2002). Protein secondary structure prediction based on an improved support vector machines approach.
17. E. Kretschmann, W. Fleischmann, and R. Apweiler Automatic rule generation for protein annotation with the C4.5 data mining algorithm applied on SWISS-PROT Bioinformatics 17 10 2001
18. V.G. Krishnan, and D.R. Westhead A comparative study of machine-learning methods to predict the effects of single nucleotide polymorphisms on protein function Bioinformatics 19 17 2003
19. Lim, T. S., Loh, W. Y., & Shih, Y. S. (2000). SA comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithm. T Machine Learning, 40(3), 203-228.
20. S. Lin, S. Patel, and A. Duncan Using decision trees and support vector machines to classify genes by names. Proceeding of the Europen workshop on data mining and text mining for bioinformatics 2003
21. J.H. Min, and Y.C. Lee Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters Expert Systems with Applications 28 4 2005 603 614
22. M.T. Mitchell Machine learning 1997 McGraw-Hill New York
23. R. Mitsdorffer, J. Diederich, and C. Tan Rule-extraction from technology IPOs in the US stock market. ICONIP02, Singapore 2002
24. S. Möller, E.V. Kriventseva, and R. Apweiler A collection of well characterized integral membrane proteins Bioinformatics 16 2000 1159 1160
25. W.S. Noble B. Schoelkopf K. Tsuda J.-P. Vert Kernel methods in computational biology 2004 MIT Press Cambridge, MA 71 92
26. H. Núñez, C. Angulo, and A. Catala Rule-extraction from support vector machines. The European symposium on artifical neural networks, Burges 2002 2-930307-02-1 (pp.107-112)
27. C.W. Omlin, and C.L. Giles Extraction of rules from discrete-time recurrent neural networks Neural Networks 9 1 1996 41 52
28. J.R. Quinlan C4.5: Programs for machine learning 1993 Morgan Kaufmann San Mateo, CA
29. J.R. Quinlan Improved use of continuous attributes in C4.5 Journal of Artificial Intelligence Research 4 1996 77 90
30. S.K. Riis, and A. Krogh Improving prediction of protein secondary structure using structured neural networks and multiple sequence alignments Journal of Computational Biology 3 1996 163 183
31. B. Rost, and C. Sander Combining evolutionary information and neural networks to predict protein secondary structure Proteins 19 1994 55 72
32. K.S. Shin, T.S. Lee, and H.J. Kim An application of support vector machines in bankruptcy prediction model Expert Systems with Applications 28 1 2005 127 135
33. A.R. Sikder, and A.Y. Zomaya An overview of protein-folding techniques: Issues and perspectives International Journal of Bioinformatics Research and Applications 1 1 2005 121 143
34. S.Y. Sohn, and T.H. Moon Decision tree based on data envelopment analysis for effective technology commercialization Expert Systems with Applications 26 2 2004 279 284
35. A. Tickle, R. Andrews, G. Mostefa, and J. Diederich The truth will come to light: Directions and challenges in extracting the knowledge embedded within trained artificial neural networks IEEE Transactions on Neural Networks 9 6 1998 1057 1068
36. V. Vapnik Statistical learning theory 1998 Wiley New York
37. Z.-H. Zhou, and Y. Jiang NeC4.5: Neural ensemble based C4.5 IEEE Transactions on Knowledge and Data Engineering 16 6 2004 770 773