A new test system for stability measurement of marker gene selection in DNA microarray data analysis

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

Volumn 3746 LNCS, Issue , 2005, Pages 437-447

  Xiong, Fei ^a   Huang, Heng ^a   Ford, James ^a   Makedon, Fillia S ^a   Pearlman, Justin D ^b  

^a Dartmouth College   (United States)

^b DARTMOUTH HITCHCOCK MEDICAL CENTER   (United States)

Author keywords

Gene Selection; Microarray Data; Sample Classification; Stability Measurement

Indexed keywords

BIOMARKERS; DATA REDUCTION; DNA; GENETIC ENGINEERING; PATTERN RECOGNITION; PRINCIPAL COMPONENT ANALYSIS; PROBLEM SOLVING;

GENE SELECTION; MICROARRAY DATA; SAMPLE CLASSIFICATION; STABILITY MEASUREMENT;

GENES;

EID: 33646523827     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11573036_41     Document Type: Conference Paper

References (24)

1. Medvedovic, M., Yeung, K. and Bumgarner, R. E.: Bayesian mixture model based clustering of replicated microarray data. Bioinformatics 20(8) (2004) 1222-1232.
2. Dougherty, E. R., Barrera, J., Brun, M., Kim, S., Cesar, R. M., Chen, Y., Bittner, M. and Trent, J. M.: Inference from clustering with application to gene-expression microarrays. J. Comput. Biol. 9 (2002) 105-126.
3. Yeung, K. Y., Praley, C., Murua, A., Raftery, A. E. and Ruzzo, W. L.: Model-based clustering and data transformations for gene expression data. Bioinformatics 17 (2001) 977-987.
4. Kerr, M. k. and Churchill, G. A.: Bootstrapping cluster analysis: assessing the reliability of conclusions from microarray experiments. Proc. Natl. Acad. Sci. USA 98 (2001) 8961-8965.
5. Pollard, K. S. and Van der Laan, M. J.: Multiple testing for gene expression data: an investigation of null distributions with consequences for the permutation test. Proceedings of the 2003 International MultiConference in Computer Science and Engineering, METMBS'03 Conference, (2003) 3-9.
6. Ge, Y., Dudoit, S. and Speed, T. P.: Resampling-based multiple testing for microarray data analysis. Technical Report 633, Department of Statistics, UC Berkeley, (2003).
7. Troyanskaya, O., Cantor, M., Sherlock, G., Brown, P., Hastie, T., Tibshirani, R., Botstein, D. and Altman, R. B.: Missing value estimation methods for DNA microarrays. Bioinformatics 17 (2001) 520-525.
8. Zhou, X., Wang, X. and Dougherty, E. R.: Missing-value estimation using linear and non-linear regression with Bayesian gene selection. Bioinformatics 19(17) (2003) 2302-2307.
9. Kim, H., Golub, G. and Park, H.: Missing value estimation for DNA microarray gene expression data: local least squares imputation. Bioinformatics 21(2) (2005) 187-198.
10. McShane, L., Radmacher, M., Freidlin, B., Yu, R., Li, M. and Simon, R.: Methods for assessing reproducibility of clustering patterns observerd in analyses of microarray data. Bioinformatics 18(11) (2002) 1462-1469
11. Fu, L. and Youn E.: Improving Reliability of Gene Selection From Microarray Functional Genomics Data. IEEE Transactions on Information Technology in Biomedicine 7(3) (2003) 191-196
12. Kohavi, R. and John, G.: Wrapper for feature subset selection. Artifical Intelligence 97(1-2) (1997) 273-324
13. Kononenko, I.: Estimating attributes: analysis and extensions of relief. In Proceedings of the European conference on machine learning on Machine Learning. Springer-Verlag, New York, Inc. (1994) 171-182
14. Mitchell, T.: Machine Learning. McGraw-Hill, New York. (1997)
15. Liu, H. and Setiono, R.: Chi2: feature selection and discretization of numeric attributes. In Proceedings of the Seventh International Conference on Tools with Artifical Intelligence. (1995) 388-391
16. Wang, Y., Makedon, F., Ford, J. and Pearlman, J.: HykGene: a hybrid approach for selecting marker genes for phenotype classification using microarray gene expression data. Bioinformatics 21(8) (2004) 1530-1537
17. Massart, D., Vandeginste, B., Deming, S., Michotte, Y. and Kaufman, L.: The k-nearest neighbor method. In Chemometrics: A Textbook (Data Handling in Science and Technology, vol. 2. Elsevier Science, New York. (1988) 395-397
18. Vapnik, V.: Statistical Learning Theory. Wiley-Interscience, New York. (1998)
19. Quinlan, J. R.: C4.5: Programs for Machine Learning. Morgan Kauffman, San Francisco, USA. (1993)
20. Stone, M.: Cross-Validatory choice and assessment of statistical predictions. J. R. Stat. Soc. B 36(1) (1974) 111-147
21. Jolliffe, I.: Principal Component Analysis (Second Edition): Springer-Verlag, New York. (2002)
22. Gordon, G., Jensen, R., Hsiao, L., Gullans, S., Blumenstock, J., Ramaswamy, S., Richards, W., Sugarbaker, D., and Bueno, R.: Translation of Microarray Data into Clinically Relevant Cancer Diagnostic Tests Using Gege Expression Ratios in Lung Cancer And Mesothelioma. Cancer Research 62 (2002) 4963-4967
23. Spira, A., Beane, J., Shah, V., Liu, G., Schembri, F., Yang, X., Palma, J., and Brody, J.: Effects of cigarette smoke on the human airway epithelial cell transcriptome. Proc. Natl. Acad. Sci. USA 101(27) (2004) 10143-10148
24. Witten, I. and Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Francisco, Calif. (1999)