A between-class overlapping filter-based method for transcriptome data analysis

Journal of Bioinformatics and Computational Biology

Volumn 10, Issue 5, 2012, Pages

  Sharma, Alok ^a,b   Imoto, Seiya ^a   Miyano, Satoru ^a  

^a UNIVERSITY OF TOKYO   (Japan)

^b UNIVERSITY OF THE SOUTH PACIFIC   (Fiji)

Author keywords

between class overlapping; Feature selection; filter based method; transcriptome data

Indexed keywords

TRANSCRIPTOME;

ALGORITHM; ARTICLE; AUTOMATED PATTERN RECOGNITION; GENE EXPRESSION PROFILING; GENETIC DATABASE; GENETICS; HUMAN; LEUKEMIA; METHODOLOGY;

ALGORITHMS; DATABASES, GENETIC; GENE EXPRESSION PROFILING; HUMANS; LEUKEMIA; PATTERN RECOGNITION, AUTOMATED; TRANSCRIPTOME;

EID: 84864451927     PISSN: 02197200     EISSN: 17576334     Source Type: Journal    
DOI: 10.1142/S0219720012500102     Document Type: Article

References (36)

1. Siedelecky W, Sklansky J, On automatic feature selection, Int J Pattern Recog 2:197-220, 1998.
2. Inza I, Larranaga P, Etxeberria R, Sierra B, Feature subset selection by Bayesian networks based optimization, Artif Intel 123:157-184, 2000.
3. Ramaswamy S, Tamayo P, Rifkin R, Mukherjee S, Yeang CH, Angelo M, Ladd C, Reich M, Latulippe E, Mesirov JP, Poggio T, Gerald W, Loda M, Lander ES, Golub TR, Multiclass cancer diagnosis using tumor gene expression signatures, Proc Natl Acad Sci 98(26):15149-15154, 2001.
4. Guyon I, Weston J, Barnhill S, Vapnik V, Gene selection for cancer classification using support vector machines, Mach Learn 46:389-422, 2002.
5. Yu L, Liu H, Effcient feature selection via analysis of relevance and redundancy, J Mach Learn Res 5:1205-1224, 2004.
6. Cong G, Tan K-L, Tung AKH, Xu X, Mining top-k covering rule groups for gene expression data, ACM SIGMOD Int Conf Management of Data, pp. 670-681, 2005.
7. Jafari P, Azuaje F, An assessment of recently published gene expression data analyses: Reporting experimental design and statistical factors, BMC Med Inform Decision Making 6:27, 2006.
8. Mamitsuka H, Selecting features in microarray classification using ROC curves, Pattern Recog 39:2393-2404, 2006.
9. Zhang C, Lu X, Zhang X, Significance of gene ranking for classification of microarray samples, IEEE/ACM Transact Computational Biology and Bioinformatics 3(3):312-320, 2006.
10. Saeys Y, Inza I, Larranaga P, A review of feature selection techniques in bioinformatics, Bioinformatics 23(19):2507-2517, 2007.
11. Wang X, Gotoh O, Cancer classification using single genes, Genome Inform 23:179-188, 2009.
12. Sharma A, Imoto S, Miyano S, Sharma V, Null space based feature selection method for gene expression data, Int J Mach Learn Cybernetics, 2011, doi: 10.1007/s13042-011-0061-9.
13. Sharma A, Koh CH, Imoto S, Miyano S, Strategy of finding optimal number of features on gene expression data, IEE Electron Lett 47(8):480-482, 2011.
14. Sharma A, Imoto S, Miyano S, A top-r feature selection algorithm for microarray gene expression data, IEEE/ACM Transact Computational Biology and Bioinformatics 9(3):754-764, 2012.
15. Zhang Y-Q, Rajapakse JC (eds.), Machine Learning in Bioinformatics, Wiley, 2009.
16. Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh M, Downing JR, Caligiuri MA, Bloomfield CD, Lander ES, Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring, Science 286:531-537, 1999.
17. Furey TS, Cristianini N, Duffy N, Bednarski DW, Schummer M, Haussler D, Support vector machine classification and validation of cancer tissue samples using microarray expression data, Bioinformatics 16:906-914, 2000.
18. Baldi P, Long A, A Bayesian framework for the analysis of microarray expression data: Regularized t-test and statistical inferences of gene changes, Bioinformatics 17(6):509-516, 2001.
19. Pavlidis P, Weston J, Cai J, Grundy WN, Gene functional classification from heterogeneous data, Int Conf Comput Biol, pp. 249-255, 2001.
20. Thomas J, Olson JM, Tapscott SJ, Zhao LP, An effcient and robust statistical modeling approach to discover differentially expressed genes using genomic expression profiles, Genome Res 11:1227-1236, 2001.
21. Pan W, A comparative review of statistical methods for discovering differentially expressed genes in replicated microarray experiments, Bioinformatics 18:546-554, 2002.
22. Dudoit S, Fridlyand J, Speed TP, Comparison of discriminant methods for the classifi-cation of tumors using gene expression data, J Am Stat Assoc 97:77-87, 2002.
23. Fox R, Dimmic M, A two-sample Bayesian t-test for microarray data, BMC Bioinformatics 7:126, 2006.
24. Mak MW, Kung SY, A solution to the curse of dimensionality problem in pairwise scoring techniques, Int Conf Neural Inform Proc, pp. 314-323, 2006.
25. Ben-Bassat M, Pattern recognition and reduction of dimensionality, In Krishnaiah P, Kanal L (eds.) Handbook of Statistics II, Vol. 1, Amsterdam, pp. 773-791, 1982.
26. Quinlan JR, Introduction of decision trees, Mach Learn 1(1):81-106, 1986.
27. Meesad P, Boonrawd P, Nuipian V, A Chi-square-test for word importance differentiation in text classification, Int Conf Inform Electron Engg 6:110-114, 2011.
28. Duda RO, Hart PE, Pattern Classification and Scene Analysis, Wiley, New York, 1973.
29. Sharma A, Paliwal KK, Rotational linear discriminant analysis technique for dimensionality reduction, IEEE Transact Knowledge and Data Engg 20(10):1336-1347, 2008.
30. Anton H, Calculus, John Wiley & Sons, 1995.
31. Yang K, Li J, Gao H, The impact of samples imbalance on identifying differentially expressed genes, BMC Bioinformatics 7(Suppl 4):S8, 2006.
32. Witten IH, Frank E, Data Mining: Practical Machine Learning Tools with Java Implementations, Morgan Kaufmann, San Francisco, CA, 2000. http:/www.cs.waikato. ac.nz/ml/weka/
33. Hoerl AE, Kennard RW, Ridge regression: Biased estimation for nonorthogonal problems, Technometrics 12(1):55-67, 1970.
34. Tibshirani R, Regression shrinkage and selection via the lasso, J Royal Stat Soc B 58(1):267-288, 1996.
35. Friedman J, Hastie T, Hoing H, Tibshirani R, Pathwise coordinate optimization, Annals Appl Stat 1(2):302-332, 2007.
36. Friedman J, Hastie T, Tibshirani R, Regularization paths for generalized linear models via coordinate descent, J Stat Software 33(1), 2010.