Merging microarray data, robust feature selection, and predicting prognosis in prostate cancer

Cancer Informatics

Volumn 2, Issue , 2006, Pages 87-97

  Wang, Jing ^a   Kim, Anh Do ^a   Wen, Sijin ^a   Tsavachidis, Spyros ^a   McDonnell, Timothy J ^a   Logothetis, Christopher J ^a   Coombes, Kevin R ^a  

^a UNIVERSITY OF TEXAS MD ANDERSON CANCER CENTER   (United States)

Author keywords

Combining data; Cross validation; Feature selection; Microarray expression profiling; Predictive model; Prostrate cancer

Indexed keywords

ARTICLE; DATA ANALYSIS; LOGISTIC REGRESSION ANALYSIS; MICROARRAY ANALYSIS; PROGNOSIS; PROSTATE CANCER; SIMULATION; CANCER PROGNOSIS; CONTROLLED STUDY; DISCRIMINANT ANALYSIS; FEATURE SELECTION; GENE EXPRESSION; GENETIC ALGORITHM; GENETIC SELECTION; HUMAN; HUMAN TISSUE; KOLMOGOROV SMIRNOV TEST; MAJOR CLINICAL STUDY; MALE; PREDICTION; PRINCIPAL COMPONENT ANALYSIS;

EID: 33746673810     PISSN: 11769351     EISSN: 11769351     Source Type: Journal    
DOI: 10.1177/117693510600200009     Document Type: Article

References (32)

1. Ambroise C and McLachian GJ. (2002) Selection bias in gene extraction on the basis of microarray gene-expression data. Pro Nat Acad Sci USA, Vol. 99, No. 10, p6562-6556.
2. Ashish Choudhary, Marcel Brun, Jianping Hua, James Lowey, Ed Suh and Edward R. Dougherty (2006). Genetic test bed for feature selection. Bioinformatics 22(7):837-842.
3. Baggerly KA, Morris JS, Wang J, Gold D, Xiao LC, Coombes KR (2003). A comprehensive approach to the analysis of matrix-assisted laser desorption/ionization-time of flight proteomics spectra from serum samples. Proteomics. 3(9):1667-72.
4. Benjamini, Y., and Hochberg, Y. (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. JRSS (B), 57, 289-300.
5. Choi, J.K., Choi, J.Y., Kim, D.G., Choi, D.W., Kim, B.Y., Lee, K.H., Yeom, Y.I., Yoo, H.S., Yoo, O.J., and Kim, S. (2004) Integrative analysis of multiple gene expression profiles applied to liver cancer study. FEBS Lett, 565(1-3), 93-100.
6. Chow M.L, Moler E.J, and Mian I.S. (2001) Identifying marker genes in transcription profiling data using a mixture of feature relevance experts. Physiol. Genomics, Mar; 5:99-111.
7. Deutsch JM. (2003) Evolutionary algorithms for finding optimal gene sets in microarray prediction. Bioinformatics. Jan; 19(1):45-52.
8. Dudoit, S., and Fridlyand, J. (2003) Classification in microarray experiments. In Speed, T.P. (ed), Statistical Analysis of Gene Expression Microarray Data. Chapman & Hall/CRC, London, pp. 93-158.
9. Gleason, D.F., and Mellinger, G.T. (1974) Prediction of prognosis for prostatic adenocarcinoma by combined histological grading and clinical staging. J Urology, 111(1), 58-64.
10. Gleason, D.F. (1977) Histologic grading and clinical staging of prostatic carcinoma. In: Tannenbaum M (ed.), Urologic Pathology: The Prostate. Lea and Febiger, Philadelphia, pp. 171-197.
11. Ghosh, D., Barette, T.R., Rhodes, D., and Chinnaiyan, A.M. (2003) Statistical issues and methods for meta-analysis of microarray data: a case study in prostate cancer. Funct Integr Genomics, 3(4), 180-188.
12. Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA, Bloomfield CD, Lander ES. (1999) Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science. Oct 15; 286(5439):531-7.
13. Guyon, I. and Elisseeff, A. (2003) An introduction to variable and feature selection. Journal of Machine Learning Research, 3:1157-82.
14. Hauskrecht, Milos; Pelikan, Richard; Malehorn, David E; Bigbee, William L; Lotze, Michael T; Zeh, Herbert J III; Whitcomb, David C; Lyons-Weiler, James (2005). Feature Selection for Classification of SELDITOF-MS Proteomic Profiles Appl Bioinformatics. 4(4):227-46.
15. Inza I, Larranaga P, Blanco R, Cerrolaza AJ (2004). Filter versus wrapper gene selection approaches in DNA microarray domains. Artif Intell Med. Jun; 31(2):91-103.
16. Lapointe, J., Li, C., Higgins, J.P., van de Rijn, M., Bair, E., Montgomery, K., Ferrari, M., Egevad, L., Rayford, W., Bergerheim, U., Ekman, P., DeMarzo, A.M., Tibshirani, R., Botstein, D., Brown, P.O., Brooks, J.D., and Pollack, J.R. (2004) Gene expression profiling identifies clinically relevant subtypes of prostate cancer. Proc Natl Acad Sci U S A, 101(3),811-816.
17. Li, C., and Wong, W.H. (2001). Model-based analysis of oligonucleotide arrays: model validation, design issues and standard error application, Genome Biology, 2(8):1-11.
18. Khan, J., Wei, J.S., Ringer, M., Saal, J.H., Ladanyi, M., Westermann, F., Berthold, F., Schwarb, M., Antonescu, C.R., Peterson, C., and Meltzer, P.S. (2001) Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nat Med, 7,673-679.
19. Magee, J.A., Araki, T., Patil, S., Ehrig, T., True, L., Humphrey, P.A., Catalona, W.J., Watson, M.A., and Milbrandt, J. (2001) Expression profiling reveals hepsin overexpression in prostate cancer. Cancer Res, 61(15):5692-5696.
20. Michiels, S., Koscielny, S. and Hill, C. (2005) Prediction of cancer outcome with microarrays: a multiple random validation strategy. Lancet, 165:488-492.
21. Ooi CH, Tan P (2003). Genetic algorithms applied to multi-class prediction for the analysis of gene expression data. Bioinformatics. Jan; 19(1):37-44.
22. Pounds, S., and Morris, S.W. (2003) Estimating the occurrence of false positive and false negatives in microarray studies by approximating and partitioning the empirical distribution of p-values. Bioinformatics, 19,1236-1242.
23. Rhodes, D.R., Barrette, T.R., Ghosh, R.D., and Chinnayan, A.M. (2002) Meta-analysis of microarrays: Interstudy validation of gene expression profiles reveals pathway dysregulation in prostate cancer. Cancer Res, 62:4427-4433.
24. Rhodes, D.R., Yu, J., Shanker, K., Deshpande, N., Varambally, R., Ghosh, D., Barrette, T., Pandey, A., and Chinnaiyan, A.M. (2004) Large-scale meta-analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression. Proc Natl Acad Sci U S A, 101(25):9309-9314.
25. Simon, R. (2003A) Diagnostic and prognostic prediction using gene expression profiles in high-dimensional microarray data. Br J Cancer, 89:1599-1604.
26. Simon, R., Radmacher, M.D., Dobbin, K. and McShane, L.M. (2003B) Pitfalls in the analysis of DNA microarray data for diagnostic and prognostic classification. J Nat Cancer Inst, 95:14-18.
27. Singh, D., Febbo, P.G., Ross, K., Jackson, D.G., Manola, J., Ladd, C., Tamayo, P., Renshaw, A.A., D'Amico, A.V., Richie, J.P., Lander, E.S., Loda, M., Kantoff, P.W. , Golub, T.R., and Sellers, W.R. (2002) Gene expression correlates of clinical prostate cancer behavior. Cancer Cell, 1(2):203-209.
28. Wang, J., Coombes, K.R., Highsmith, W., Keating, M.J., and Abruzzo, L.V. (2004) Differences in gene expression between B-cell chronic lymphocytic leukemia and B cells: a meta-analysis of three microarray studies. Bioinformatics, 20(17):3166-3178.
29. 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. (2001) Analysis of gene expression identifies candidate markers and pharmacological targets in prostate cancer. Cancer Res, 61(16):5974-5978.
30. West, M., Blanchette, C., Dressman, H., Huang, E., Ishida, S., Spang, R., Zuzan, H., Olson, J.A., Marks, J.R., and Neving, J.R. (2001) Predicting the clinical status of human breast cancer by using gene expression profiles. Pro Nat Acad Sci USA, 98:11462-11467.
31. Yang, Y.H., Dudoit, S., Luu, P., Lin, D.M., Peng, V., Ngai, J., and Speed, T.P. (2002) Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. Nucleic Acids Res, 30(4):e15.
32. Yongxi Tan, Leming Shi, Weida Tong and Charles Wang (2005). Multi-class cancer classification by total principal component regression (TPCR) using microarray gene expression data. Nucleic Acids Research, 33(1):56-65.