Identifying genes that contribute most to good classification in microarrays

BMC Bioinformatics

Volumn 7, Issue , 2006, Pages

  Baker, Stuart G ^a   Kramer, Barnett S ^b  

^a NATIONAL CANCER INSTITUTE   (United States)

^b NATIONAL INSTITUTES OF HEALTH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION PERFORMANCE; CLASSIFICATION RULES; GENE INTERACTIONS; HIGH FREQUENCY HF; NORMAL TISSUE; PROTEOGLYCANS; RECEIVER OPERATING CHARACTERISTIC CURVES; SECRETORY GRANULES;

CLASSIFICATION (OF INFORMATION); DISEASES; MICROARRAYS;

GENES;

DESMIN; ESTROGEN RECEPTOR; PROTEOGLYCAN; ZYXIN; CYTOSKELETON PROTEIN; GLYCOPROTEIN; SECRETORY GRANULE PROTEOGLYCAN; ZYX PROTEIN, HUMAN;

ACUTE GRANULOCYTIC LEUKEMIA; ACUTE LYMPHOBLASTIC LEUKEMIA; ARTICLE; BIOINFORMATICS; BREAST CANCER; CANCER CLASSIFICATION; CANCER GENETICS; COLON CANCER; CONTROLLED STUDY; DATA ANALYSIS; DIAGNOSTIC VALUE; DNA MICROARRAY; FALSE POSITIVE RESULT; GENE FREQUENCY; GENE IDENTIFICATION; HUMAN; MEDULLOBLASTOMA; PERFORMANCE; ROC CURVE; SECRETORY GRANULE; SENSITIVITY AND SPECIFICITY; VALIDATION PROCESS; COLON TUMOR; COMPUTER PROGRAM; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION; GENETICS; LEUKEMIA; PROCEDURES;

COLONIC NEOPLASMS; CYTOSKELETAL PROTEINS; DESMIN; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION, NEOPLASTIC; GLYCOPROTEINS; HUMANS; LEUKEMIA; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; PROTEOGLYCANS; SOFTWARE VALIDATION; ZYXIN;

EID: 33749030973     PISSN: 14712105     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-7-407     Document Type: Article

References (23)

1. Simon R. Diagnostic and prognostic prediction using gene expression profiles in high-dimensional microarray data. Br Med J 2003, 89:1599-1604.
2. Michiels S, Koscielny S, Hill C: Prediction of cancer outcome with microarrays: a multiple random validation strategy. Lancet 2005, 365:488-92.
3. Tang EK, Suganthan PN, Yao X: Gene selection algorithms for microarray data based on least squares support vector machine. BMC Bioinformatics 2006, 7:95.
4. Baker SG: Identifying combinations of cancer biomarkers for further study as triggers of early intervention. Biometrics 2000, 56:1082-1087.
5. Dudoit S, Fridlyand J, Speed TP: Comparison of discrimination methods for the classification of tumors using gene expression data. J Am Stat Assoc 2002, 97:77-87.
6. van't Veer LJ, Dai HY, van de Vijver MJ, He YDD, Hart AAM, Mao M, Peterse HL, van der Kooy K, Marton MJ, Witteveen AT, Schreiber GJ, Kerkhoven RM, Roberts C, Linsley PS, Bernards R, Friend SH: Gene expression profiling predicts clinical outcome of breast cancer. Nature 2002, 415:530-536.
7. Ma S, Song X, Huang J: Regularized binormall ROC method in disease classification using microarray data. BMC Bioinformatics 2006, 7:253.
8. Li X, Rao S, Wang Y, Gong B: Gene mining: a novel and powerful ensemble decision approach to hunting for disease genes using microarray expression profiling. Nucl Acids Res 2004, 32:2685-2694.
9. Dabney AR: Classification of microarrays to the nearest centroids. Bioinformatics 2005, 21:4148-4125.
10. Pepe MS: The Statistical Evaluation of Medical Tests for Classification and Prediction Oxford University Press; 2003.
11. Alon U, Barkai N, Notterman DA, Gish K, Ybarra S, Mack D, Levine AJ: Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc Natl Acad Sci 1999, 96:6745-6750.
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: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 1999, 286:531-537.
13. Pomeroy SL, Tamayo P, Gaasenbeek M, Sturia LM, Angelo M, McLaughlin ME, Kim JYH, Goumnerova LC, Black PM, Lau C, Allen JC, Zagzag D, Olson JM, Curran T, Wetmore C, Biegel JA, Poggio T, Mukherjee S, Rifkin R, Califano A, Stolovitzky G, Louis DN, Mesirov JP, Lander ES, Golub TR: Prediction of central nervous system embryonal tumour outcome based on gene expression. Nature 2002, 414:436-42.
14. West M. Blanchette C, Dressman H, Huang E, Ishida S, Spang R, Zuzan H, Olson J, Marks J, Nevins JR: Predicting the clinical status of human breast cancer by using gene expression profiles. Proc Natl Acad Sci 2001, 98:11562-11467.
15. Li Z, Mericskay M, Agbulut O, Butler-Browne G, Carlsson L, Thornell L, Babinet C, Paulin D: Desmin Is Essential for the Tensile Strength and Integrity of Myofibrils but Not for Myogenic Commitment, Differentiation, and Fusion of Skeletal Muscle. J Cell Biol 1997, 139:129-144.
16. Klieverik L, Fehres O, Griffini P, Van Noorden CJF, Frederiks WM: Promotion of colon cancer metastases in rat liver by fish oil diet is not due to reduced stroma formation. Clin Exp Metastosis 2001, 18:371-377.
17. Groisman GM, Polak-Charcon S, Appelman HD: Fibroblastic polyp of the colon: clinicopathological analysis of 10 cases with emphasis on its common association with serrated crypts. Histopathology 2006, 48:431-437.
18. Hirota T, Morisaki T, Nishiyama Y, Marumoto T, Tada K, Hara T, Masuko N, Inagaki M, Hatakeyama K, Saya H: Zyxin, a regulator of actin filament assembly, targets the mitotic apparatus by interacting with h-warts/LATSI tumor suppressor. J Cell Biol 2000, 149:1073-86.
19. Wang Y, Tetko IV, Hall MA, Frank E, Facius A, Mayer KFX, Mewes HW: Gene selection from microarray data for cancer classification - a machine learning approach. Comput Biol Chem 2005, 29:37-46.
20. Humphries DE, Nicodemus CF, Schiller V, Stevens RL: The human serglycin gene - nucleotide-sequence and methylation pattern in human promyelocytic leukemia HL-60 cells and T-lymphoblast Molt-4 cells. J Biol Chem 1992, 267:13558-13563.
21. Niemann CU, Cowland JB, Klausen P, Askaa J, Calafat J, Borregaard N: Localization of serglycin in human neutrophil granulocytes and their precursors. J Leukoc Biol 2004, 76:406-415.
22. Baker SG, Kramer BS, McIntosh M, Patterson BH, Shyr Y, Skates S: Evaluating markers for the early detection of cancer: Overview of study designs and methods of analysis. Clinical Trials 2006, 3:43-56.
23. Bamber D: The area above the ordinal dominance graph and the area below the receiver operating characteristic graph. Journal of Mathematical Psychology 1975, 12:387-415.