Very Important Pool (VIP) genes - An application for microarray-based molecular signatures

BMC Bioinformatics

Volumn 9, Issue SUPPL. 9, 2008, Pages

  Su, Zhenqiang ^a   Hong, Huixiao ^a   Fang, Hong ^b   Shi, Leming ^a   Perkins, Roger ^b   Tong, Weida ^a  

^a NATIONAL CENTER FOR TOXICOLOGICAL RESEARCH   (United States)

^b ICF INTERNATIONAL   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLINICAL ENVIRONMENTS; DISCRIMINATORY ANALYSIS; DNA MICROARRAY TECHNOLOGY; GENE EXPRESSION DATA; HYPOTHESIS GENERATION; MICROARRAY DATA SETS; MOLECULAR SIGNATURES; PERSONALIZED MEDICINES;

BIOINFORMATICS; GENE EXPRESSION; LAKES; PRINCIPAL COMPONENT ANALYSIS;

STATISTICAL TESTS;

ACUTE LYMPHOCYTIC LEUKEMIA; B CELL LYMPHOMA; BIOINFORMATICS; BREAST CANCER; CLASSIFIER; COMPUTER PROGRAM; CONFERENCE PAPER; CONTROLLED STUDY; DISCRIMINANT ANALYSIS; DNA MICROARRAY; GENE EXPRESSION; GENE IDENTIFICATION; INTERMETHOD COMPARISON; LARGE CELL LYMPHOMA; LIVER CELL CARCINOMA; LUNG ADENOCARCINOMA; MEDULLOBLASTOMA; NONHODGKIN LYMPHOMA; NUCLEOTIDE SEQUENCE; PREDICTION; PRINCIPAL COMPONENT ANALYSIS; PROSTATE CANCER; STATISTICAL SIGNIFICANCE; ALGORITHM; ARTICLE; GENE; GENE EXPRESSION PROFILING; GENE POOL; GENETIC MARKER; GENETICS; METHODOLOGY;

PROTEOME;

ALGORITHMS; GENE EXPRESSION PROFILING; GENE POOL; GENES; GENETIC MARKERS; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; PROTEOME;

EID: 49649124942     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-9-S9-S9     Document Type: Conference Paper

References (58)

1. Lockhart DJ Dong H Byrne MC Follettie MT Gallo MV Chee MS Mittmann M Wang C Kobayashi M Horton H et al.: Expression monitoring by hybridization to high-density oligonucleotide arrays Nat Biotechnol 1996, 14(13):1675-1680. 10.1038/nbt1296-1675 9634850
2. Schena M Shalon D Davis RW Brown PO Quantitative monitoring of gene expression patterns with a complementary DNA microarray Science 1995, 270(5235):467-470. 10.1126/science.270.5235.467 7569999
3. Quackenbush J Computational approaches to analysis of DNA microarray data Methods Inf Med 2006, 45(Suppl 1):91-103.
4. Quackenbush J Computational analysis of microarray data Nat Rev Genet 2001, 2(6):418-427. 10.1038/35076576 11389458
5. Dopazoa J Zandersb E Dragonib I Amphlettb G Falci F Methods and approaches in the analysis of gene expression data Journal of Immunological Methods 2001, 250(1-2):93-112. 10.1016/ S0022-1759(01)00307-6 11251224
6. Butte A The use and analysis of microarray data Nat Rev Drug Discov 2002, 1(12):951-960. 10.1038/nrd961 12461517
7. Hackl H Sanchez Cabo F Sturn A Wolkenhauer O Trajanoski Z Analysis of DNA microarray data Curr Top Med Chem 2004, 4(13):1357-1370. 10.2174/ 1568026043387773 15379650
8. Lee KE Sha N Dougherty ER Vannucci M Mallick BK Gene selection: A Bayesian variable selection approach Bioinformatics 2003, 19(1):90-97. 10.1093/bioinformatics/19.1.90 12499298
9. Ding C Peng H Minimum redundancy feature selection from microarray gene expression data J Bioinform Comput Biol 2005, 3(2):185-205. 10.1142/ S0219720005001004 15852500
10. Gould J Getz G Monti S Reich M Mesirov JP Comparative gene marker selection suite Bioinformatics 2006, 22(15):1924-1925. 10.1093/ bioinformatics/btl196 16709585
11. Chen JJ Tsai CA Tzeng S Chen CH Gene selection with multiple ordering criteria BMC Bioinformatics 2007, 8:74. 1829166 17338815 10.1186/ 1471-2105-8-74
12. Mukherjee S Roberts SJ A theoretical analysis of the selection of differentially expressed genes J Bioinform Comput Biol 2005, 3(3):627-643. 10.1142/S0219720005001211 16108087
13. Su Z Hong H Perkins R Shao X Cai W Tong W Consensus analysis of multiple classifiers using non-repetitive variables: Diagnostic application to microarray gene expression data Comput Biol Chem 2007, 31(1):48-56. 10.1016/j.compbiolchem.2007.01.001 17303535
14. Shi L Reid LH Jones WD Shippy R Warrington JA Baker SC Collins PJ de Longueville F Kawasaki ES Lee KY et al.: The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements Nat Biotechnol 2006, 24(9):1151-1161. 10.1038/ nbt1239 16964229
15. Shi L Tong W Fang H Scherf U Han J Puri RK Frueh FW Goodsaid FM Guo L Su Z et al.: Cross-platform comparability of microarray technology: intra-platform consistency and appropriate data analysis procedures are essential BMC Bioinformatics 2005, 6(Suppl 2):S12. 1637032 16026597 10.1186/1471-2105-6-S2-S12
16. Shi L Perkins RG Fang H Tong W Reproducible and reliable microarray results through quality control: Good laboratory proficiency and appropriate data analysis practices are essential Curr Opin Biotechnol 2008, 19(1):10-18. 10.1016/j.copbio.2007.11.003 18155896
17. Jain AK Duin RPW Mao J Statistical Pattern Recognition: A Review IEEE Transactions on Pattern Analysis and Machine Intelligence 2000, 22(1):4-37. 10.1109/34.824819
18. Raudys SJ Jain AK Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners IEEE Transactions on Pattern Analysis and Machine Intelligence 1991, 13(3):252-264. 10.1109/ 34.75512
19. Zhang HH Ahn J Lin X Park C Gene selection using support vector machines with non-convex penalty Bioinformatics 2006, 22(1):88-95. 10.1093/ bioinformatics/bti736 16249260
20. Bluma AL Langley P Selection of relevant features and examples in machine learning Artificial Intelligence 1997, 97(1-2):245-271. 10.1016/ S0004-3702(97)00063-5
21. Ambroise C McLachlan GJ Selection bias in gene extraction on the basis of microarray gene-expression data Proc Natl Acad Sci USA 2002, 99(10):6562-6566. 124442 11983868 10.1073/pnas.102102699
22. Diaz-Uriarte R Alvarez de Andres S Gene selection and classification of microarray data using random forest BMC Bioinformatics 2006, 7:3. 1363357 16398926 10.1186/1471-2105-7-3
23. Ein-Dor L Kela I Getz G Givol D Domany E Outcome signature genes in breast cancer: Is there a unique set? Bioinformatics 2005, 21(2):171-178. 10.1093/bioinformatics/bth469 15308542
24. Lai C Reinders MJ van't Veer LJ Wessels LF A comparison of univariate and multivariate gene selection techniques for classification of cancer datasets BMC Bioinformatics 2006, 7:235. 1569875 16670007 10.1186/ 1471-2105-7-235
25. Li L Weinberg CR Darden TA Pedersen LG Gene selection for sample classification based on gene expression data: Study of sensitivity to choice of parameters of the GA/KNN method Bioinformatics 2001, 17(12):1131-1142. 10.1093/bioinformatics/17.12.1131 11751221
26. Liu B Cui Q Jiang T Ma S A combinational feature selection and ensemble neural network method for classification of gene expression data BMC Bioinformatics 2004, 5:136. 522806 15450124 10.1186/1471-2105-5-136
27. Zhang JG Deng HW Gene selection for classification of microarray data based on the Bayes error BMC Bioinformatics 2007, 8(1):370. 2089123 17915022 10.1186/1471-2105-8-370
28. Wang Y Tetko IV Hall MA Frank E Facius A Mayer KF Mewes HW Gene selection from microarray data for cancer classification - a machine learning approach Comput Biol Chem 2005, 29(1):37-46. 10.1016/ j.compbiolchem.2004.11.001 15680584
29. Tang EK Suganthan PN Yao X Gene selection algorithms for microarray data based on least squares support vector machine BMC Bioinformatics 2006, 7:95. 1409801 16504159 10.1186/1471-2105-7-95
30. Wang L Zhu J Zou H Hybrid huberized support vector machines for microarray classification and gene selection Bioinformatics 2008, 24(3):412-419. 10.1093/bioinformatics/btm579 18175770
31. Breiman L Bagging predictors Machine Learning 1996, 24(2):123-140.
32. InfoMetrix Multivariate Data Analysis Version 4.0 Pirouette User Guide 2007.
33. Michiels S Koscielny S Hill C Prediction of cancer outcome with microarrays: A multiple random validation strategy Lancet 2005, 365(9458):488-492. 10.1016/S0140-6736(05)17866-0 15705458
34. Simon R Radmacher MD Dobbin K McShane LM Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification Journal of the National Cancer Institute 2003, 95(1):14-18. 12509396
35. Wessels LF Reinders MJ Hart AA Veenman CJ Dai H He YD van't Veer LJ A protocol for building and evaluating predictors of disease state based on microarray data Bioinformatics 2005, 21(19):3755-3762. 10.1093/ bioinformatics/bti429 15817694
36. Ben-Dor A Bruhn L Friedman N Nachman I Schummer M Yakhini Z Tissue classification with gene expression profiles J Comput Biol 2000, 7(3-4):559-583. 10.1089/106652700750050943 11108479
37. Vapnik VN The Nature of Statistical Learning Theory New York: Springer-Verlag New York, Inc 1 1995. 8555380
38. Lutz U Lutz RW Lutz WK Metabolic profiling of glucuronides in human urine by LC-MS/MS and partial least-squares discriminant analysis for classification and prediction of gender Anal Chem 2006, 78(13):4564-4571. 10.1021/ac0522299 16808466
39. Jarvis SE Barr W Feng ZP Hamid J Zamponi GW Molecular determinants of syntaxin 1 modulation of N-type calcium channels Journal of Biological Chemistry 2002, 277(46):44399-44407. 10.1074/jbc.M206902200 12221094
40. Gana Dresen IM Boes T Huesing J Neuhaeuser M Joeckel KH New resampling method for evaluating stability of clusters BMC Bioinformatics 2008, 9:42. 2265265 18218074 10.1186/1471-2105-9-42
41. Brehelin L Gascuel O Martin O Using repeated measurements to validate hierarchical gene clusters Bioinformatics 2008, 24(5):682-688. 10.1093/ bioinformatics/btn017 18204054
42. Dudoit S Fridlyand J Bagging to improve the accuracy of a clustering procedure Bioinformatics 2003, 19(9):1090-1099. 10.1093/bioinformatics/ btg038 12801869
43. Dettling M BagBoosting for tumor classification with gene expression data Bioinformatics 2004, 20(18):3583-3593. 10.1093/bioinformatics/ bth447 15466910
44. Peng Y A novel ensemble machine learning for robust microarray data classification Comput Biol Med 2006, 36(6):553-573. 10.1016/ j.compbiomed.2005.04.001 15978569
45. Fu WJ Carroll RJ Wang S Estimating misclassification error with small samples via bootstrap cross-validation Bioinformatics 2005, 21(9):1979-1986. 10.1093/bioinformatics/bti294 15691862
46. Jiang W Simon R A comparison of bootstrap methods and an adjusted bootstrap approach for estimating the prediction error in microarray classification Stat Med 2007, 26(29):5320-5334. 10.1002/sim.2968 17624926
47. 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 USA 1999, 96(12):6745-6750. 21986 10359783 10.1073/pnas.96.12.6745
48. Beer DG Kardia SL Huang CC Giordano TJ Levin AM Misek DE Lin L Chen G Gharib TG Thomas DG et al.Gene-expression profiles predict survival of patients with lung adenocarcinoma Nat Med 2002, 8(8):816-824. 12118244
49. Bhattacharjee A Richards WG Staunton J Li C Monti S Vasa P Ladd C Beheshti J Bueno R Gillette M et al. Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses Proc Natl Acad Sci USA 2001, 98 24:13790-13795. 61120 11707567 10.1073/pnas.191502998
50. Chen X Cheung ST So S Fan ST Barry C Higgins J Lai KM Ji J Dudoit S Ng IO et al. Gene expression patterns in human liver cancers Molecular Biology of the Cell 2002, 13(6):1929-1939. 117615 12058060 10.1091/ mbc.02-02-0023.
51. Gordon GJ Jensen RV Hsiao L-L Gullans SR Blumenstock JE Ramaswamy S Richards WG Sugarbaker DJ Bueno R Translation of Microarray Data into Clinically Relevant Cancer Diagnostic Tests Using Gege Expression Ratios in Lung Cancer And Mesothelioma Cancer Research 2002, 62:4963-4967. 12208747
52. Pomeroy SL Tamayo P Gaasenbeek M Sturla LM Angelo M McLaughlin ME Kim JY Goumnerova LC Black PM Lau C et al. Prediction of central nervous system embryonal tumour outcome based on gene expression Nature 2002, 415(6870):436-442. 10.1038/415436a 11807556
53. Rosenwald A Wright G Chan WC Connors JM Campo E Fisher RI Gascoyne RD Muller-Hermelink HK Smeland EB Giltnane JM et al. The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma N Engl J Med 2002, 346(25):1937-1947. 10.1056/ NEJMoa012914 12075054
54. Shipp MA Ross KN Tamayo P Weng AP Kutok JL Aguiar RC Gaasenbeek M Angelo M Reich M Pinkus GS et al. Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning Nat Med 2002, 8(1):68-74. 10.1038/nm0102-68 11786909
55. Singh D Febbo PG Ross K Jackson DG Manola J Ladd C Tamayo P Renshaw AA D'Amico AV Richie JP et al. Gene expression correlates of clinical prostate cancer behavior Cancer Cell 2002, 1(2):203-209. 10.1016/ S1535-6108(02)00030-2 12086878
56. Yeoh EJ Ross ME Shurtleff SA Williams WK Patel D Mahfouz R Behm FG Raimondi SC Relling MV Patel A et al. Classification, subtype discovery, and prediction of outcome in pediatric acute lymphoblastic leukemia by gene expression profiling Cancer Cell 2002, 1(2):133-143. 10.1016/ S1535-6108(02)00032-6 12086872
57. van't Veer LJ Dai H Vijver van de MJ He YD Hart AA Mao M Peterse HL Kooy van der K Marton MJ Witteveen AT et al. Gene expression profiling predicts clinical outcome of breast cancer Nature 2002, 415(6871):530-536. 10.1038/415530a 11823860
58. Wold S Pattern Recognition by Means of Disjoint Principle Components Models Pattern Recognition 1976, 8:127-139. 10.1016/0031-3203(76)90014-5