{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T14:49:41Z","timestamp":1771598981189,"version":"3.50.1"},"reference-count":58,"publisher":"Springer Science and Business Media LLC","issue":"S9","license":[{"start":{"date-parts":[[2008,8,1]],"date-time":"2008-08-01T00:00:00Z","timestamp":1217548800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/2.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"published-print":{"date-parts":[[2008,8]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:sec>\n            <jats:title>Background<\/jats:title>\n            <jats:p>Advances in DNA microarray technology portend that molecular signatures from which microarray will eventually be used in clinical environments and personalized medicine. Derivation of biomarkers is a large step beyond hypothesis generation and imposes considerably more stringency for accuracy in identifying informative gene subsets to differentiate phenotypes. The inherent nature of microarray data, with fewer samples and replicates compared to the large number of genes, requires identifying informative genes prior to classifier construction. However, improving the ability to identify differentiating genes remains a challenge in bioinformatics.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>A new hybrid gene selection approach was investigated and tested with nine publicly available microarray datasets. The new method identifies a Very Important Pool (VIP) of genes from the broad patterns of gene expression data. The method uses a bagging sampling principle, where the re-sampled arrays are used to identify the most informative genes. Frequency of selection is used in a repetitive process to identify the VIP genes. The putative informative genes are selected using two methods, t-statistic and discriminatory analysis. In the t-statistic, the informative genes are identified based on p-values. In the discriminatory analysis, disjoint Principal Component Analyses (PCAs) are conducted for each class of samples, and genes with high discrimination power (DP) are identified. The VIP gene selection approach was compared with the p-value ranking approach. The genes identified by the VIP method but not by the p-value ranking approach are also related to the disease investigated. More importantly, these genes are part of the pathways derived from the common genes shared by both the VIP and p-ranking methods. Moreover, the binary classifiers built from these genes are statistically equivalent to those built from the top 50 p-value ranked genes in distinguishing different types of samples.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusion<\/jats:title>\n            <jats:p>The VIP gene selection approach could identify additional subsets of informative genes that would not always be selected by the p-value ranking method. These genes are likely to be additional true positives since they are a part of pathways identified by the p-value ranking method and expected to be related to the relevant biology. Therefore, these additional genes derived from the VIP method potentially provide valuable biological insights.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/1471-2105-9-s9-s9","type":"journal-article","created":{"date-parts":[[2008,8,12]],"date-time":"2008-08-12T18:13:09Z","timestamp":1218564789000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Very Important Pool (VIP) genes \u2013 an application for microarray-based molecular signatures"],"prefix":"10.1186","volume":"9","author":[{"given":"Zhenqiang","family":"Su","sequence":"first","affiliation":[]},{"given":"Huixiao","family":"Hong","sequence":"additional","affiliation":[]},{"given":"Hong","family":"Fang","sequence":"additional","affiliation":[]},{"given":"Leming","family":"Shi","sequence":"additional","affiliation":[]},{"given":"Roger","family":"Perkins","sequence":"additional","affiliation":[]},{"given":"Weida","family":"Tong","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2008,8,12]]},"reference":[{"issue":"13","key":"2668_CR1","doi-asserted-by":"publisher","first-page":"1675","DOI":"10.1038\/nbt1296-1675","volume":"14","author":"DJ Lockhart","year":"1996","unstructured":"Lockhart DJ, Dong H, Byrne MC, Follettie MT, Gallo MV, Chee MS, Mittmann M, Wang C, Kobayashi M, Horton H: Expression monitoring by hybridization to high-density oligonucleotide arrays. Nat Biotechnol. 1996, 14 (13): 1675-1680. 10.1038\/nbt1296-1675.","journal-title":"Nat Biotechnol"},{"issue":"5235","key":"2668_CR2","doi-asserted-by":"publisher","first-page":"467","DOI":"10.1126\/science.270.5235.467","volume":"270","author":"M Schena","year":"1995","unstructured":"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.","journal-title":"Science"},{"issue":"Suppl 1","key":"2668_CR3","first-page":"91","volume":"45","author":"J Quackenbush","year":"2006","unstructured":"Quackenbush J: Computational approaches to analysis of DNA microarray data. Methods Inf Med. 2006, 45 (Suppl 1): 91-103.","journal-title":"Methods Inf Med"},{"issue":"6","key":"2668_CR4","doi-asserted-by":"publisher","first-page":"418","DOI":"10.1038\/35076576","volume":"2","author":"J Quackenbush","year":"2001","unstructured":"Quackenbush J: Computational analysis of microarray data. Nat Rev Genet. 2001, 2 (6): 418-427. 10.1038\/35076576.","journal-title":"Nat Rev Genet"},{"issue":"1\u20132","key":"2668_CR5","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1016\/S0022-1759(01)00307-6","volume":"250","author":"J Dopazoa","year":"2001","unstructured":"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\u20132): 93-112. 10.1016\/S0022-1759(01)00307-6.","journal-title":"Journal of Immunological Methods"},{"issue":"12","key":"2668_CR6","doi-asserted-by":"publisher","first-page":"951","DOI":"10.1038\/nrd961","volume":"1","author":"A Butte","year":"2002","unstructured":"Butte A: The use and analysis of microarray data. Nat Rev Drug Discov. 2002, 1 (12): 951-960. 10.1038\/nrd961.","journal-title":"Nat Rev Drug Discov"},{"issue":"13","key":"2668_CR7","doi-asserted-by":"publisher","first-page":"1357","DOI":"10.2174\/1568026043387773","volume":"4","author":"H Hackl","year":"2004","unstructured":"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.","journal-title":"Curr Top Med Chem"},{"issue":"1","key":"2668_CR8","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1093\/bioinformatics\/19.1.90","volume":"19","author":"KE Lee","year":"2003","unstructured":"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.","journal-title":"Bioinformatics"},{"issue":"2","key":"2668_CR9","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1142\/S0219720005001004","volume":"3","author":"C Ding","year":"2005","unstructured":"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.","journal-title":"J Bioinform Comput Biol"},{"issue":"15","key":"2668_CR10","doi-asserted-by":"publisher","first-page":"1924","DOI":"10.1093\/bioinformatics\/btl196","volume":"22","author":"J Gould","year":"2006","unstructured":"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.","journal-title":"Bioinformatics"},{"key":"2668_CR11","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1186\/1471-2105-8-74","volume":"8","author":"JJ Chen","year":"2007","unstructured":"Chen JJ, Tsai CA, Tzeng S, Chen CH: Gene selection with multiple ordering criteria. BMC Bioinformatics. 2007, 8: 74-10.1186\/1471-2105-8-74.","journal-title":"BMC Bioinformatics"},{"issue":"3","key":"2668_CR12","doi-asserted-by":"publisher","first-page":"627","DOI":"10.1142\/S0219720005001211","volume":"3","author":"S Mukherjee","year":"2005","unstructured":"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.","journal-title":"J Bioinform Comput Biol"},{"issue":"1","key":"2668_CR13","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1016\/j.compbiolchem.2007.01.001","volume":"31","author":"Z Su","year":"2007","unstructured":"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.","journal-title":"Comput Biol Chem"},{"issue":"9","key":"2668_CR14","doi-asserted-by":"publisher","first-page":"1151","DOI":"10.1038\/nbt1239","volume":"24","author":"L Shi","year":"2006","unstructured":"Shi L, Reid LH, Jones WD, Shippy R, Warrington JA, Baker SC, Collins PJ, de Longueville F, Kawasaki ES, Lee KY: 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.","journal-title":"Nat Biotechnol"},{"issue":"Suppl 2","key":"2668_CR15","doi-asserted-by":"publisher","first-page":"S12","DOI":"10.1186\/1471-2105-6-S2-S12","volume":"6","author":"L Shi","year":"2005","unstructured":"Shi L, Tong W, Fang H, Scherf U, Han J, Puri RK, Frueh FW, Goodsaid FM, Guo L, Su Z: Cross-platform comparability of microarray technology: intra-platform consistency and appropriate data analysis procedures are essential. BMC Bioinformatics. 2005, 6 (Suppl 2): S12-10.1186\/1471-2105-6-S2-S12.","journal-title":"BMC Bioinformatics"},{"issue":"1","key":"2668_CR16","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.copbio.2007.11.003","volume":"19","author":"L Shi","year":"2008","unstructured":"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.","journal-title":"Curr Opin Biotechnol"},{"issue":"1","key":"2668_CR17","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/34.824819","volume":"22","author":"AK Jain","year":"2000","unstructured":"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.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"3","key":"2668_CR18","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1109\/34.75512","volume":"13","author":"SJ Raudys","year":"1991","unstructured":"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.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"1","key":"2668_CR19","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1093\/bioinformatics\/bti736","volume":"22","author":"HH Zhang","year":"2006","unstructured":"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.","journal-title":"Bioinformatics"},{"issue":"1\u20132","key":"2668_CR20","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1016\/S0004-3702(97)00063-5","volume":"97","author":"AL Bluma","year":"1997","unstructured":"Bluma AL, Langley P: Selection of relevant features and examples in machine learning. Artificial Intelligence. 1997, 97 (1\u20132): 245-271. 10.1016\/S0004-3702(97)00063-5.","journal-title":"Artificial Intelligence"},{"issue":"10","key":"2668_CR21","doi-asserted-by":"publisher","first-page":"6562","DOI":"10.1073\/pnas.102102699","volume":"99","author":"C Ambroise","year":"2002","unstructured":"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. 10.1073\/pnas.102102699.","journal-title":"Proc Natl Acad Sci USA"},{"key":"2668_CR22","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1186\/1471-2105-7-3","volume":"7","author":"R Diaz-Uriarte","year":"2006","unstructured":"Diaz-Uriarte R, Alvarez de Andres S: Gene selection and classification of microarray data using random forest. BMC Bioinformatics. 2006, 7: 3-10.1186\/1471-2105-7-3.","journal-title":"BMC Bioinformatics"},{"issue":"2","key":"2668_CR23","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1093\/bioinformatics\/bth469","volume":"21","author":"L Ein-Dor","year":"2005","unstructured":"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.","journal-title":"Bioinformatics"},{"key":"2668_CR24","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1186\/1471-2105-7-235","volume":"7","author":"C Lai","year":"2006","unstructured":"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: 235-10.1186\/1471-2105-7-235.","journal-title":"BMC Bioinformatics"},{"issue":"12","key":"2668_CR25","doi-asserted-by":"publisher","first-page":"1131","DOI":"10.1093\/bioinformatics\/17.12.1131","volume":"17","author":"L Li","year":"2001","unstructured":"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.","journal-title":"Bioinformatics"},{"key":"2668_CR26","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1186\/1471-2105-5-136","volume":"5","author":"B Liu","year":"2004","unstructured":"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: 136-10.1186\/1471-2105-5-136.","journal-title":"BMC Bioinformatics"},{"issue":"1","key":"2668_CR27","doi-asserted-by":"publisher","first-page":"370","DOI":"10.1186\/1471-2105-8-370","volume":"8","author":"JG Zhang","year":"2007","unstructured":"Zhang JG, Deng HW: Gene selection for classification of microarray data based on the Bayes error. BMC Bioinformatics. 2007, 8 (1): 370-10.1186\/1471-2105-8-370.","journal-title":"BMC Bioinformatics"},{"issue":"1","key":"2668_CR28","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/j.compbiolchem.2004.11.001","volume":"29","author":"Y Wang","year":"2005","unstructured":"Wang Y, Tetko IV, Hall MA, Frank E, Facius A, Mayer KF, Mewes HW: Gene selection from microarray data for cancer classification\u2013a machine learning approach. Comput Biol Chem. 2005, 29 (1): 37-46. 10.1016\/j.compbiolchem.2004.11.001.","journal-title":"Comput Biol Chem"},{"key":"2668_CR29","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1186\/1471-2105-7-95","volume":"7","author":"EK Tang","year":"2006","unstructured":"Tang EK, Suganthan PN, Yao X: Gene selection algorithms for microarray data based on least squares support vector machine. BMC Bioinformatics. 2006, 7: 95-10.1186\/1471-2105-7-95.","journal-title":"BMC Bioinformatics"},{"issue":"3","key":"2668_CR30","doi-asserted-by":"publisher","first-page":"412","DOI":"10.1093\/bioinformatics\/btm579","volume":"24","author":"L Wang","year":"2008","unstructured":"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.","journal-title":"Bioinformatics"},{"issue":"2","key":"2668_CR31","first-page":"123","volume":"24","author":"L Breiman","year":"1996","unstructured":"Breiman L: Bagging predictors. Machine Learning. 1996, 24 (2): 123-140.","journal-title":"Machine Learning"},{"key":"2668_CR32","volume-title":"Pirouette User Guide","author":"InfoMetrix","year":"2007","unstructured":"InfoMetrix: Multivariate Data Analysis Version 4.0. Pirouette User Guide. 2007"},{"issue":"9458","key":"2668_CR33","doi-asserted-by":"publisher","first-page":"488","DOI":"10.1016\/S0140-6736(05)17866-0","volume":"365","author":"S Michiels","year":"2005","unstructured":"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.","journal-title":"Lancet"},{"issue":"1","key":"2668_CR34","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1093\/jnci\/95.1.14","volume":"95","author":"R Simon","year":"2003","unstructured":"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.","journal-title":"Journal of the National Cancer Institute"},{"issue":"19","key":"2668_CR35","doi-asserted-by":"publisher","first-page":"3755","DOI":"10.1093\/bioinformatics\/bti429","volume":"21","author":"LF Wessels","year":"2005","unstructured":"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.","journal-title":"Bioinformatics"},{"issue":"3\u20134","key":"2668_CR36","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1089\/106652700750050943","volume":"7","author":"A Ben-Dor","year":"2000","unstructured":"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\u20134): 559-583. 10.1089\/106652700750050943.","journal-title":"J Comput Biol"},{"key":"2668_CR37","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-2440-0","volume-title":"The Nature of Statistical Learning Theory","author":"VN Vapnik","year":"1995","unstructured":"Vapnik VN: The Nature of Statistical Learning Theory. 1995, New York: Springer-Verlag New York, Inc, 1","edition":"1"},{"issue":"13","key":"2668_CR38","doi-asserted-by":"publisher","first-page":"4564","DOI":"10.1021\/ac0522299","volume":"78","author":"U Lutz","year":"2006","unstructured":"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.","journal-title":"Anal Chem"},{"issue":"46","key":"2668_CR39","doi-asserted-by":"publisher","first-page":"44399","DOI":"10.1074\/jbc.M206902200","volume":"277","author":"SE Jarvis","year":"2002","unstructured":"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.","journal-title":"Journal of Biological Chemistry"},{"key":"2668_CR40","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1186\/1471-2105-9-42","volume":"9","author":"IM Gana Dresen","year":"2008","unstructured":"Gana Dresen IM, Boes T, Huesing J, Neuhaeuser M, Joeckel KH: New resampling method for evaluating stability of clusters. BMC Bioinformatics. 2008, 9: 42-10.1186\/1471-2105-9-42.","journal-title":"BMC Bioinformatics"},{"issue":"5","key":"2668_CR41","doi-asserted-by":"publisher","first-page":"682","DOI":"10.1093\/bioinformatics\/btn017","volume":"24","author":"L Brehelin","year":"2008","unstructured":"Brehelin L, Gascuel O, Martin O: Using repeated measurements to validate hierarchical gene clusters. Bioinformatics. 2008, 24 (5): 682-688. 10.1093\/bioinformatics\/btn017.","journal-title":"Bioinformatics"},{"issue":"9","key":"2668_CR42","doi-asserted-by":"publisher","first-page":"1090","DOI":"10.1093\/bioinformatics\/btg038","volume":"19","author":"S Dudoit","year":"2003","unstructured":"Dudoit S, Fridlyand J: Bagging to improve the accuracy of a clustering procedure. Bioinformatics. 2003, 19 (9): 1090-1099. 10.1093\/bioinformatics\/btg038.","journal-title":"Bioinformatics"},{"issue":"18","key":"2668_CR43","doi-asserted-by":"publisher","first-page":"3583","DOI":"10.1093\/bioinformatics\/bth447","volume":"20","author":"M Dettling","year":"2004","unstructured":"Dettling M: BagBoosting for tumor classification with gene expression data. Bioinformatics. 2004, 20 (18): 3583-3593. 10.1093\/bioinformatics\/bth447.","journal-title":"Bioinformatics"},{"issue":"6","key":"2668_CR44","doi-asserted-by":"publisher","first-page":"553","DOI":"10.1016\/j.compbiomed.2005.04.001","volume":"36","author":"Y Peng","year":"2006","unstructured":"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.","journal-title":"Comput Biol Med"},{"issue":"9","key":"2668_CR45","doi-asserted-by":"publisher","first-page":"1979","DOI":"10.1093\/bioinformatics\/bti294","volume":"21","author":"WJ Fu","year":"2005","unstructured":"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.","journal-title":"Bioinformatics"},{"issue":"29","key":"2668_CR46","doi-asserted-by":"publisher","first-page":"5320","DOI":"10.1002\/sim.2968","volume":"26","author":"W Jiang","year":"2007","unstructured":"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.","journal-title":"Stat Med"},{"issue":"12","key":"2668_CR47","doi-asserted-by":"publisher","first-page":"6745","DOI":"10.1073\/pnas.96.12.6745","volume":"96","author":"U Alon","year":"1999","unstructured":"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. 10.1073\/pnas.96.12.6745.","journal-title":"Proc Natl Acad Sci USA"},{"issue":"8","key":"2668_CR48","doi-asserted-by":"crossref","first-page":"816","DOI":"10.1038\/nm733","volume":"8","author":"DG Beer","year":"2002","unstructured":"Beer DG, Kardia SL, Huang CC, Giordano TJ, Levin AM, Misek DE, Lin L, Chen G, Gharib TG, Thomas DG: Gene-expression profiles predict survival of patients with lung adenocarcinoma. Nat Med. 2002, 8 (8): 816-824.","journal-title":"Nat Med"},{"issue":"24","key":"2668_CR49","doi-asserted-by":"publisher","first-page":"13790","DOI":"10.1073\/pnas.191502998","volume":"98","author":"A Bhattacharjee","year":"2001","unstructured":"Bhattacharjee A, Richards WG, Staunton J, Li C, Monti S, Vasa P, Ladd C, Beheshti J, Bueno R, Gillette M: Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses. Proc Natl Acad Sci USA. 2001, 98 (24): 13790-13795. 10.1073\/pnas.191502998.","journal-title":"Proc Natl Acad Sci USA"},{"issue":"6","key":"2668_CR50","doi-asserted-by":"publisher","first-page":"1929","DOI":"10.1091\/mbc.02-02-0023.","volume":"13","author":"X Chen","year":"2002","unstructured":"Chen X, Cheung ST, So S, Fan ST, Barry C, Higgins J, Lai KM, Ji J, Dudoit S, Ng IO: Gene expression patterns in human liver cancers. Molecular Biology of the Cell. 2002, 13 (6): 1929-1939. 10.1091\/mbc.02-02-0023..","journal-title":"Molecular Biology of the Cell"},{"key":"2668_CR51","first-page":"4963","volume":"62","author":"GJ Gordon","year":"2002","unstructured":"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.","journal-title":"Cancer Research"},{"issue":"6870","key":"2668_CR52","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/415436a","volume":"415","author":"SL Pomeroy","year":"2002","unstructured":"Pomeroy SL, Tamayo P, Gaasenbeek M, Sturla LM, Angelo M, McLaughlin ME, Kim JY, Goumnerova LC, Black PM, Lau C: Prediction of central nervous system embryonal tumour outcome based on gene expression. Nature. 2002, 415 (6870): 436-442. 10.1038\/415436a.","journal-title":"Nature"},{"issue":"25","key":"2668_CR53","doi-asserted-by":"publisher","first-page":"1937","DOI":"10.1056\/NEJMoa012914","volume":"346","author":"A Rosenwald","year":"2002","unstructured":"Rosenwald A, Wright G, Chan WC, Connors JM, Campo E, Fisher RI, Gascoyne RD, Muller-Hermelink HK, Smeland EB, Giltnane JM: 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.","journal-title":"N Engl J Med"},{"issue":"1","key":"2668_CR54","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1038\/nm0102-68","volume":"8","author":"MA Shipp","year":"2002","unstructured":"Shipp MA, Ross KN, Tamayo P, Weng AP, Kutok JL, Aguiar RC, Gaasenbeek M, Angelo M, Reich M, Pinkus GS: 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.","journal-title":"Nat Med"},{"issue":"2","key":"2668_CR55","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1016\/S1535-6108(02)00030-2","volume":"1","author":"D Singh","year":"2002","unstructured":"Singh D, Febbo PG, Ross K, Jackson DG, Manola J, Ladd C, Tamayo P, Renshaw AA, D'Amico AV, Richie JP: Gene expression correlates of clinical prostate cancer behavior. Cancer Cell. 2002, 1 (2): 203-209. 10.1016\/S1535-6108(02)00030-2.","journal-title":"Cancer Cell"},{"issue":"2","key":"2668_CR56","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1016\/S1535-6108(02)00032-6","volume":"1","author":"EJ Yeoh","year":"2002","unstructured":"Yeoh EJ, Ross ME, Shurtleff SA, Williams WK, Patel D, Mahfouz R, Behm FG, Raimondi SC, Relling MV, Patel A: 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.","journal-title":"Cancer Cell"},{"issue":"6871","key":"2668_CR57","doi-asserted-by":"publisher","first-page":"530","DOI":"10.1038\/415530a","volume":"415","author":"LJ van 't Veer","year":"2002","unstructured":"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: Gene expression profiling predicts clinical outcome of breast cancer. Nature. 2002, 415 (6871): 530-536. 10.1038\/415530a.","journal-title":"Nature"},{"key":"2668_CR58","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1016\/0031-3203(76)90014-5","volume":"8","author":"S Wold","year":"1976","unstructured":"Wold S: Pattern Recognition by Means of Disjoint Principle Components Models. Pattern Recognition. 1976, 8: 127-139. 10.1016\/0031-3203(76)90014-5.","journal-title":"Pattern Recognition"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/1471-2105-9-S9-S9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/1471-2105-9-S9-S9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/1471-2105-9-S9-S9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,8,31]],"date-time":"2021-08-31T21:38:16Z","timestamp":1630445896000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/1471-2105-9-S9-S9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2008,8]]},"references-count":58,"journal-issue":{"issue":"S9","published-print":{"date-parts":[[2008,8]]}},"alternative-id":["2668"],"URL":"https:\/\/doi.org\/10.1186\/1471-2105-9-s9-s9","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2008,8]]},"assertion":[{"value":"12 August 2008","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"S9"}}