Top scoring pair decision tree for gene expression data analysis

Advances in Experimental Medicine and Biology

Volumn 696, Issue , 2011, Pages 27-35

  Czajkowski, Marcin ^a   Krȩtowski, Marek ^a  

^a NONE

Author keywords

[No Author keywords available]

Indexed keywords

ACCURACY; CLASSIFICATION; COMPUTER PROGRAM; CONFERENCE PAPER; CONTROLLED STUDY; DATA ANALYSIS; DECISION TREE; GENE EXPRESSION PROFILING; GENETIC ANALYSIS; INTERMETHOD COMPARISON; MICROARRAY ANALYSIS; PRIORITY JOURNAL; SUPPORT VECTOR MACHINE; ALGORITHM; ARTICLE; BREAST TUMOR; COMPARATIVE STUDY; DNA MICROARRAY; EVALUATION; FEMALE; GENETICS; HUMAN; NUCLEIC ACID DATABASE; STATISTICAL ANALYSIS; STATISTICS;

DNA;

ALGORITHMS; BREAST NEOPLASMS; DATA INTERPRETATION, STATISTICAL; DATABASES, NUCLEIC ACID; DECISION TREES; DNA, NEOPLASM; FEMALE; GENE EXPRESSION PROFILING; HUMANS; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS;

EID: 79958036171     PISSN: 00652598     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-1-4419-7046-6_3     Document Type: Conference Paper

References (39)

1. Alon, U., Barkai, N.: Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proceedings of the National Academy of Sciences of the USA, 96(12):6745-6750 (1999) (Pubitemid 29274954)
2. Bittner, M., Meltzer, P.: Molecular classification of cutaneous malignant melanoma by gene expression profiling. Nature, 406:536-540 (2000) (Pubitemid 30625740)
3. Breiman, L., Friedman, J.: Classification and Regression Trees, Wadsworth International Group, Belmont, CA, USA (1984)
4. Breiman, L.: Bagging predictors. Machine Learning, 24(2):123-140 (1996) (Pubitemid 126724382)
5. Breiman, L.: Random forests. Machine Learning, 45(1):5-32 (2001) (Pubitemid 32933532)
6. Brown, P.O., Botstein, D.: Exploring the new world of the genome with DNA microarrays. Nature Genetics, 21:33-37 (1999) (Pubitemid 29031489)
7. Cho, H.S., Kim, T.S.: cDNA microarray data based classification of cancers using neural networks and genetic algorithms. Nanotechnology, 1:28-31 (2003)
8. Czajkowski, M., Krȩtowski, M.: Novel extension of k-TSP algorithm for micro-array classification. Lecture Notes in Artificial Intelligence, 5027:456-465 (2008)
9. Cohen, W.W.: Fast Effective Rule Induction, Twelfth International Conference on Machine Learning, Morgan Kaufmann, San Francisco, CA, USA, 115-123 (1995)
10. Dhanasekaran, S.M.: Delineation of prognostic biomarkers in prostate cancer. Nature, 412:822-826 (2001) (Pubitemid 32801464)
11. Dudoit, S.J., Fridlyand, J.: Comparison of discrimination methods for the classification of tumors using gene expression data. Journal of the American Statistical Association, 97:77-87 (2002)
12. Duggan, D.J., Bittner, M.: Expression profiling using cDNA microarrays. Nature Genetics, 21(suppl 1):10-14 (1999) (Pubitemid 29031485)
13. Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm, Thirteenth International Conference on Machine Learning, San Francisco, CA, USA, 148-156 (1996)
14. Geman, D., dAvignon, C.: Classifying gene expression profiles from pairwise mRNA comparisons. Statistical Applications in Genetics and Molecular Biology, 3(1):19 (2007)
15. Grzés, M., Krȩtowski, M.: Decision tree approach to microarray data analysis. Biocybernetics and Biomedical Engineering, 27(3):29-42 (2007)
16. Hastie, T., Tibshirani, R.: The Elements of Statistical Learning. Springer, New York (2001)
17. Hu, H., Li, J.: AMaximally Diversified Multiple Decision Tree Algorithm for Microarray Data Classification, Workshop on Intelligent Systems for Bioinformatics, Hobart, Australia (2006)
18. Jinyan. L., Huiqing, L.: Ensembles of cascading trees, Proceedings of the Third IEEE International Conference on Data Mining, 585-588 (2003)
19. Kent Ridge Bio-medical Dataset Repository: http://datam.i2r.a-star.edu. sg/datasets/index.html
20. Kononenko, I.: Estimating Attributes: Analysis and Extensions of RELIEF. In: European Conference on Machine Learning, Catania, Italy, 171-182 (1994)
21. Lockhart, D.J., Winzeler, E.A.: Genomics, gene expression and DNA arrays. Nature, 405:827-836 (2000) (Pubitemid 30407861)
22. Lu, Y., Han, J.: Cancer classification using gene expression data. Information Systems, 28(4):243-268 (2003)
23. Mao, Y., Zhou, X.: Multiclass cancer classification by using fuzzy support vector machine and binary decision tree with gene selection. Journal of Biomedicine and Biotechnology, 2:160-171 (2005) (Pubitemid 41627873)
24. Murthy, S.: Automatic construction of decision trees from data: A multi-disciplinary survey. Data Mining and Knowledge Discovery, 2:345-389 (1998) (Pubitemid 128695483)
25. Nelson, P.S.: Predicting prostate cancer behavior using transcript profiles. Journal of Urology, 172:28-32 (2004)
26. Rokach, L., Maimon, O.: Top-down induction of decision trees classifiers-A survey. IEEE Transactions on Systems, Man, and Cybernetics-Part C, 35(4):476-487 (2005) (Pubitemid 41638167)
27. Sebastiani, P., Gussoni, E.: Statistical challenges in functional genomics. Statistical Science, 18(1):33-70 (2003) (Pubitemid 41461822)
28. Simon, R., Radmacher, M.D.: Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification. Journal of the National Cancer Institute, 95:14-18 (2003) (Pubitemid 36124291)
29. Tan, A.C., Gilbert, D.: Ensemble machine learning on gene expression data for cancer classification. Applied Bioinformatics, 2:75-83 (2003)
30. Tan, A.C., Naiman, D.Q.: Simple decision rules for classifying human cancers from gene expression profiles. Bioinformatics, 21:3896-3904 (2005) (Pubitemid 41535505)
31. Quinlan, R.: Inductive knowledge acquisition: A case study. Addison-Wesley, Boston, MA, USA, chapt. 9, 157-173 (1987)
32. Quinlan, R.: C4.5: Programs for Machine Learning, Morgan Kaufmann, San Mateo, CA, USA (1993)
33. Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco, CA, USA (2005)
34. Valentini, G., Muselli, M.: Bagged Ensembles of SVMs for Gene Expression Data Analysis, International Joint Conference on Neural Networks 2003, Portland, OR, USA (2003)
35. Veer, L. J., Dai, H.: Gene expression profiling predicts clinical outcome of breast cancer. Nature, 415:530-536 (2002) (Pubitemid 34130608)
36. Xu, L., Tan, A.C.: Robust prostate cancer marker genes emerge from direct integration of inter-study microarray data. Bioinformatics, 21(20):3905-3911 (2005) (Pubitemid 41535506)
37. Yoon, S., Kim, S.: k-Top scoring pair algorithm for feature selection in SVM with applications to microarray data classification. Soft Computing-A Fusion of Foundations, Methodologies and Applications, 14(2):151-159 (2009)
38. Zhang. H., Yu, C.Y.: Recursive partitioning for tumor classification with gene expression microarray data. Proceedings of the National Academy of Sciences of the USA, 98(12):6730-6735 (2001) (Pubitemid 32538285)
39. Zhang, C., Li, P.: Parallelization of multicategory support vector machines (PMC-SVM) from classifying microarray data. BMC Bioinformatics, 7(Suppl 4):S15 (2006)