Improving microarray sample size using bootstrap data combination

3rd International Multi-Symposiums on Computer and Computational Sciences, IMSCCS'08

Volumn , Issue , 2008, Pages 37-44

  Phan, John H ^a   Moffitt, Richard A ^a   Barrett, Andrea B ^a   Wang, May D ^a  

^a GEORGIA INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATA REDUCTION; GENES;

ANALYTIC APPROACHES; BIOLOGICAL SAMPLES; CLASSIFICATION ERRORS; CLINICAL APPLICATIONS; DATA COLLECTIONS; DATA COMBINATIONS; DATA-SETS; FEATURE SELECTIONS; GENE SELECTIONS; GENETIC CHANGES; HIGH-THROUGHPUT TECHNOLOGIES; MICROARRAY DATA SETS; MICROARRAY DATUM; MICROARRAY TECHNOLOGIES; PREDICTIVE MODELS; SAMPLE SIZES; SIMPLE METHODS; SMALL SAMPLES; UNBIASED ESTIMATORS;

MICROARRAYS;

EID: 78650422613     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IMSCCS.2008.36     Document Type: Conference Paper

References (30)

1. R. A. Irizarry, et al., "Multiple-laboratory comparison of microarray platforms," Nature Methods, vol. 2, no. 5, 2005, pp. 345-350.
2. T. Patterson, et al., "Performance comparison of one-color and two-color platforms within the MicroArray Quality Control (MAQC) project," Nature Biotechnology, vol. 24, no. 9, 2006, pp. 1140-1150.
3. T. M. Vo, et al., "Reproducibility of Differential Gene Detection Across Multiple Microarray Studies," Proc. 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2007.
4. M. Xiong, et al., "Biomarker Identification by Feature Wrappers," Genome Research, vol. 11, 2001, pp. 1878-1887.
5. O. Troyanskaya, et al., "Nonparametric methods for identifying differentially expressed genes in microarray data," Bioinformatics, vol. 18, no. 11, 2002, pp. 1454-1461.
6. Y. Xiao, et al., "Multivariate search for differentially expressed gene combinations," BMC Bioinformatics, vol. 5, no. 164, 2004.
7. U. Braga-Neto and E. Dougherty, "Is cross-validation valid for small-sample microarray classification?," Bioinformatics, vol. 20, 2004, pp. 374-380.
8. C. Huttenhower, et al., "A scalable method for integration and functional analysis of multiple microarray datasets," Bioinformatics, vol. 22, no. 23, 2006, pp. 2890-2897.
9. T. Park, et al., "Combining multiple microarrays in the presence of controlling variables," Bioinformatics, vol. 22, no. 14, 2006, pp. 1682-1689.
10. S. Yoon, et al., "Large scale data mining approach for gene-specific standardization of microarray gene expression data," Bioinformatics, vol. 22, no. 23, 2006, pp. 2898-2904.
11. J. Wang, et al., "Differences in gene expression between B-cell chronic lymphocytic leukemia and normal B cells: a meta-analysis of three microarray studies," Bioinformatics, vol. 20, no. 17, 2004, pp. 3166-3178.
12. J. Choi, et al., "Combining multiple microarray studies and modeling interstudy variation," Bioinformatics, vol. 19, 2003, pp. i84-i90.
13. A. N. Schuetz, et al., "Molecular Classification of Renal Tumors by Gene Expression Profiling," J Mol Diagn, vol. 7, no. 2, 2005, pp. 206-218.
14. J. Jones, et al., "Gene Signatures of Progression and Metastasis in Renal Cell Cancer," Clin Cancer Res, vol. 11, no. 16, 2005, pp. 5730-5739.
15. U. Chandran, et al., "Differences in gene expression in prostate cancer, normal appearing prostate tissue adjacent to cancer and prostate tissue from cancer free organ donors," BMC Cancer, vol. 5, no. 45, 2005.
16. D. Singh, et al., "Gene expression correlates of clinical prostate cancer behavior," Cancer Cell, vol. 1, no. 2, 2002, pp. 203-209.
17. B. Efron, "Estimating the error rate of a prediction rule: some improvements on cross-validation," J of the Am Stat Assoc, vol. 78, 1983, pp. 316-331.
18. R. Chuaqui, et al., "Post-analysis follow-up and validation of microarray experiments," Nature Genetics, vol. 32, 2002, pp. 509-514.
19. S. Mukherjee and S. Roberts, "A theoretical analysis of the selection of differentially expressed genes," J Bioinformatics Comput Biol, vol. 3, 2005, pp. 627-643.
20. A. Bradley, "The use of the area under the ROC curve in the evaluation of machine learning algorithms," Pattern Recognition, vol. 30, no. 7, 1997, pp. 1145-1159.
21. J. Morey, et al., "Microarray validation: factors influencing correlation between oligonucleotide microarrays and realtime PCR," Biol. Proced. Online, vol. 8, no. 1, 2006, pp. 175-193.
22. Y. Chen, et al., "Messenger RNA Expression Ratios among Four Genes Predict Subtypes of Renal Cell Carcinoma and Distinguish Oncocytoma from Carcinoma," Clin Cancer Res, vol. 11, 2005, pp. 6558-6566.
23. T. Ernst, et al., "Decrease and Gain of Gene Expression Are Equally Discriminatory Markers for Prostate Carcinoma: A Gene Expression Analysis on Total and Microdissected Prostate Tissue," American Journal of Pathology, vol. 160, no. 6, 2002, pp. 2169-2180.
24. N. Wang, et al., "Evaluation of tumor heterogeneity of prostate carcinoma by flow- and image DNA cytometry and histopathological grading," Analytical Cellular Pathology, vol. 20, no. 1, 2000, pp. 49-62.
25. E. Bronzetti, et al., "A possible role of BDNF in prostate cancer detection," Oncol Rep, vol. 19, no. 4, 2008, pp. 969-974.
26. M. Hofer, et al., "The Role of Metastasis-Associated Protein 1 in Prostate Cancer Progression," Cancer Research, vol. 64, 2004, pp. 825-829.
27. Y. Lu, et al., "CCR2 expression correlates with prostate cancer progression," J Cell Biochem, vol. 101, no. 3, 2007, pp. 676-685.
28. A. Hamm, et al., "Frequent expression loss of Inter-alphatrypsin inhibitor heavy chain (ITIH) genes in multiple human solid tumors: A systematic expression analysis," BMC Cancer, vol. 8, no. 25, 2008.
29. A. Baqchi, et al., "CHD5 is a tumor suppressor at human 1p36," Cell, vol. 128, no. 3, 2007, pp. 459-475.
30. A. Schrader, et al., "CXCR4/CXCL12 expression and signaling in kidney cancer," Br J Cancer, vol. 86, no. 8, 2002, pp. 1250-1256.