RSCTC'2010 Discovery Challenge: Mining DNA microarray data for medical diagnosis and treatment

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6086 LNAI, Issue , 2010, Pages 4-19

  Wojnarski, Marcin ^a,b   Janusz, Andrzej ^b   Nguyen, Hung Son ^b   Bazan, Jan ^c   Luo, Chuan Jiang ^d   Chen, Ze ^d   Hu, Feng ^d   Wang, Guoyin ^d   Guan, Lihe ^d   Luo, Huan ^d   Gao, Juan ^d   Shen, Yuanxia ^d   Nikulin, Vladimir ^e   Huang, Tian Hsiang ^e,f   McLachlan, Geoffrey J ^e   Bošnjak, Matko ^g   Gamberger, Dragan ^g  

^a TunedIT Solutions   (Poland)

^b UNIVERSITY OF WARSAW   (Poland)

^c UNIVERSITY OF RZESZÓW   (Poland)

^d CHONGQING UNIVERSITY OF POSTS AND TELECOMMUNICATIONS   (China)

^e UNIVERSITY OF QUEENSLAND   (Australia)

^f NATIONAL CHENG KUNG UNIVERSITY   (Taiwan)

^g RUDER BO KOVI INSTITUTE   (Croatia)

Author keywords

[No Author keywords available]

Indexed keywords

CURRENT TRENDS; DNA MICROARRAY DATA; FEATURE SELECTION; MEDICAL DIAGNOSIS; ROUGH SET;

COMPETITION; DIAGNOSIS; FEATURE EXTRACTION; NUCLEIC ACIDS; ROUGH SET THEORY; USER INTERFACES;

MEDICAL COMPUTING;

EID: 79956285071     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-13529-3_3     Document Type: Conference Paper

References (14)

1. Parkinson, H.E., et al.: Arrayexpress update - from an archive of functional genomics experiments to the atlas of gene expression. Nucleic Acids Research 37(Database issue), 868-872 (2009)
2. R Development Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2008)
3. Kira, K., Rendell, L.A.: A practical approach to feature selection. In: ML92: Proceedings of the ninth international workshop on Machine learning, pp. 249-256. Morgan Kaufmann Publishers Inc., San Francisco (1992)
4. Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157-1182 (2003)
5. Tibshirani, R., Hastie, T., Nara simhan, B., Chu, G.: Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proceedings of the National Academy of Sciences USA 99(10), 6567-6572 (2002) (Pubitemid 34526173)
6. Nikulin, V., McLachlan, G.J.: Classification of imbalanced marketing data with balanced random sets. In: JMLR: Workshop and Conference Proceedings, vol. 7, pp. 89-100 (2009)
7. Breiman, L.: Random forests. Machine Learning, 5-32 (2001) (Pubitemid 32933532)
8. Breiman, L.: Bagging predictors. Machine Learning, 123-140 (1996) (Pubitemid 126724382)
9. Ho, T.K.: The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell. 20(8), 832-844 (1998) (Pubitemid 128741345)
10. Gamberger, D., Lavrac, N.: Conditions for occam's razor applicability and noise elimination. In: van Someren, M., Widmer, G. (eds.) ECML 1997. LNCS, vol. 1224, pp. 108-123. Springer, Heidelberg (1997) (Pubitemid 127061919)
11. Wikipedia: Dna microarray - wikipedia, the free encyclopedia (2010), http://en.wikipedia.org/w/index.php?title=DNA-microarray
12. Nguyen, H.: Approximate boolean reasoning: Foundations and applications in data mining. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets V. LNCS, vol. 4100, pp. 334-506. Springer, Heidelberg (2006) (Pubitemid 351150611)
13. Chang, C., Lin, C.: Libsvm - a library for support vector machines. [EB/OL], http://www.csie.ntu.edu.tw/~cjlin/libsvm (2008-11-17/2010-01-3)
14. Dudoit, S., Fridlyand, J., Speed, T.: Comparison of discrimination methods for the classification of tumors using gene expression data. Journal of the American Statistical Association 97, 77-87 (2002)