Extracting gene regulation information for cancer classification

Pattern Recognition

Volumn 40, Issue 12, 2007, Pages 3379-3392

  Wang, Hong Qiang ^a   Wong, Hau San ^a   Huang, De Shuang ^b   Shu, Jun ^c  

^a CITY UNIVERSITY OF HONG KONG   (Hong Kong)

^b INSTITUTE OF INTELLIGENT MACHINES   (China)

^c First Affiliated Hospital   (China)

Author keywords

Cancer classification; Gene expression levels; Gene regulation; Microarray; Prediction strength

Indexed keywords

ALGORITHMS; GENE EXPRESSION; INFORMATION RETRIEVAL; MICROARRAYS; ONCOLOGY; TISSUE;

CLASS DISTINCTION; CONSTRUCT CLASSIFIERS; GENE REGULATION INFORMATION; LEUKEMIA DATA; PROSTATE DATA;

GENETIC ENGINEERING;

EID: 34547691339     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2007.04.007     Document Type: Article

References (39)

1. Dougherty E.R., Datta A., and Chao S. Research issues in genomic signal processing. IEEE Trans. Signal Process. 22 (2005) 46-68
2. Golub T.R., Slonim D.K., Tamayo P., Huard C., Gaasenbeek M., Mesirov J.P., Coller H., Loh M.L., Downing J.R., Caligiuri M.A., Bloomfield C.D., and Lander E.S. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286 (1999) 531-537
3. Alon U., Barkai N., Notterman D.A., Gish K., Ybarra S., Mack D., and Levine A.J. Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc. Nat. Acad. Sci. USA 96 (1999) 6745-6750
4. Baldi P., and Long A.D. A Bayesian framework for the analysis of microarray expression data: regularized t-test and statistical inferences of gene changes. Bioinformatics 17 (2001) 509-519
5. Kyeong E.L., Naijun S., Edward R.D., Marina V., and Bani K.M. Gene selection: a Bayesian variable selection approach. Bioinformatics 19 (2003) 90-97
6. Dudoit S., Fridlyand J., and Speed T.P. Comparison of discrimination methods for the classification of tumors using gene expression data. J. Amer. Statist. Assoc. 97 (2000)
7. Huang X., and Pan W. Linear regression and two-class classification with gene expression data. Bioinformatics 19 (2003) 2072-2978
8. Khan J., Wei J.S., Ringner M., Saal L.H., Landanyi M., Westermann F., Berthold F., Schwab M., Antnescu C.R., Peterson C., and Meltzer P.S. Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nature Med. 7 (2001) 673-679
9. Furey T.S., Cristianini N., Duffy N., Bednarski D., Schummer M., and Haussler D. Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 16 (2000) 906-914
10. Guyon I., Weston J., Barnhill S., and Vapnik V. Gene selection for cancer classification using support vector machines. Mach. Learn. 46 (2002) 389-422
11. Mao K.Z. Feature subset selection for support vector machines through discriminative function pruning analysis. IEEE Trans. Syst. Man Cybern. Part B 34 (2004) 60-67
12. Yan X., Deng M., Fung W.K., and Qian M. Detecting differentially expressed genes by relative entropy. J. Theor. Biol. 234 (2005) 395-402
13. Dudoit S., Yang Y.H., Callow M.J., and Speed T.P. Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments. Statist. Sin. 12 (2002) 111-139
14. Tusher V.G., Tibshirani R., and Chu G. Significance analysis of microarrays applied to the ionizing radiation response. PNAS 98 (2001) 5116-5121
15. Newton M.A., Noueiry A., Sarkar D., and Ahlquist P. Detecting differential gene expression with a semiparametric hierarchical mixture method gene expression profiles. Biostatistics 5 (2004)
16. Pan W., Lin J., and Le C.T. A mixture model approach to detecting differentially expressed genes with microarray data. Funct Integr Genomics 3 (2003) 117-124
17. Yang Y.H., Xiao Y., and Segal M.R. Identifying differentially expressed genes from microarray experiments via statistic synthesis. Bioinformatics 21 (2005) 1084-1093
18. Ghosh D. Singular value decomposition regression models for classification of tumors from microarray experiments. Pac. Symp. Biocomput. 98 (2002) 18-29
19. Antoniadis A., Lambert-Lacroix S., and Leblanc F. Effective dimension reduction methods for tumor classification using gene expression data. Bioinformatics 19 (2003) 563-570
20. Bicciato S., Luchini A., and Bello C.D. PCA disjoint models for multiclass cancer analysis using gene expression data. Bioinformatics 19 (2003) 571-578
21. Yeung K.Y., and Ruzzo W.L. Principal component analysis for clustering gene expression data. Bioinformatics 17 (2001) 763-774
22. Nguyen D.V., and Rocke D.M. Tumor classification by partial least squares using microarray gene expression data. Bioinformatics 18 (2002) 39-50
23. Pochet N., Smet F.D., Suykens J.A., and Moor B.D. Systematic benchmarking of microarray data classification: assessing the role of non-linearity and dimensionality reduction. Bioinformatics 20 (2004) 3185-3195
24. Ron K., and George J.H. Wrappers for feature subset selection. Artif. Intell. J. 97 (1997) 273-324
25. Guo G., and Dyer C.R. Simultaneous feature selection and classifier training via linear programming: a case study for face expression recognition. Proceedings of the Third IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2003)
26. Li J., and Zha H. Simultaneous classification and feature clustering using discriminant vector quantization with applications to microarray data analysis. Proceedings of the IEEE Computer Society Bioinformatics Conference (CSB'02) (2002)
27. Fort G., and Lambert-Lacroix S. Classification using partial least squares with penalized logistic regression. Bioinformatics 21 (2005) 1104-1111
28. Li Y., Campbell C., and Michael T. Bayesian automatic relevance determination algorithm for classifying gene expression data. Bioinformatics 18 (2002) 1332-1339
29. Bae K., and Mallick B.K. Gene selection using a two-level hierarchical Bayesian model. Bioinformatics 20 (2004) 3423-3430
30. M. West, J.R. Nevins, J.R. Marks, R. Spang, H. Zuzan, Bayesian regression analysis in the Large p, small n paradigm with application in DNA micrarray studies, Technical Report, Duke University, 2000.
31. Parmigiani G., Garrett E.S., Anbazhagan R., and Gabrielson E. A statistical framework for expression-based molecular classification in cancer. J. Roy. Stat. Soc. Ser. B 64 (2002) 717
32. Baldi P., and Hatfield G.W. DNA Microarrays and Gene Expression (2002), Cambridge University Press, Cambridge, New York
33. Culhane A.C., Perriere G., Considine E.C., Cotter T.G., and Higgins D.G. Between-group analysis of microarray data. Bioinformatics 18 (2002) 1600-1608
34. Dror R.O. Noise models in gene array analysis (2001), MIT Department of Electrical Engineering and Computer Science
35. Newton M.A., Kendziorski C.M., Richmond C.S., Blattner F.R., and Tsui K.W. On differential variability of expression ratios: improving statistical inference about gene expression changes from microarray data. J. Comput. Biol. 8 (2001) 37-52
36. Guan Z., and Zhao H. A semiparametric approach for marker gene selection based on gene expression data. Bioinformatics 21 (2005) 529-536
37. Singh D., Febbo P.G., Ross K., Jackson D.G., Manola J., Ladd C., Tamayo P., Renshaw A.A., D' Amico A.V., and Richie J.P.e.a. Gene expression correlates of clinical prostate cancer behavior. Cancer Cell 1 (2002)
38. Welsh J.B., Sapinoso L.M., Su A.I., Kern S.G., Wang-Rodriguez J., Moskaluk C.A., Frierson Jr. H.F., and Hampton G.M. Analysis of gene expression identifies candidate markers and pharmacological targets in prostate cancer. Cancer Res. 61 (2001) 5974-5978
39. Antonov A.V., Tetko I.V., Mader M.T., Budczies J., and Mewes H.W. Optimization models for cancer classification: extracting gene interaction information from microarray expression data. Bioinformatics 20 (2004) 644-652