{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T18:03:56Z","timestamp":1760983436195},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"1","content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"published-print":{"date-parts":[[2010,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:sec>\n            <jats:title>Background<\/jats:title>\n            <jats:p>Differential co-expression analysis is an emerging strategy for characterizing disease related dysregulation of gene expression regulatory networks. Given pre-defined sets of biological samples, such analysis aims at identifying genes that are co-expressed in one, but not in the other set of samples.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>We developed a novel probabilistic framework for jointly uncovering contexts (i.e. groups of samples) with specific co-expression patterns, and groups of genes with different co-expression patterns across such contexts. In contrast to current clustering and bi-clustering procedures, the implicit similarity measure in this model used for grouping biological samples is based on the clustering structure of genes within each sample and not on traditional measures of gene expression level similarities. Within this framework, biological samples with widely discordant expression patterns can be placed in the same context as long as the co-clustering structure of genes is concordant within these samples. To the best of our knowledge, this is the first method to date for unsupervised differential co-expression analysis in this generality. When applied to the problem of identifying molecular subtypes of breast cancer, our method identified reproducible patterns of differential co-expression across several independent expression datasets. Sample groupings induced by these patterns were highly informative of the disease outcome. Expression patterns of differentially co-expressed genes provided new insights into the complex nature of the ER<jats:italic>\u03b1<\/jats:italic> regulatory network.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusions<\/jats:title>\n            <jats:p>We demonstrated that the use of the co-clustering structure as the similarity measure in the unsupervised analysis of sample gene expression profiles provides valuable information about expression regulatory networks.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/1471-2105-11-234","type":"journal-article","created":{"date-parts":[[2010,5,7]],"date-time":"2010-05-07T18:14:35Z","timestamp":1273256075000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["A semi-parametric Bayesian model for unsupervised differential co-expression analysis"],"prefix":"10.1186","volume":"11","author":[{"given":"Johannes M","family":"Freudenberg","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Siva","family":"Sivaganesan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael","family":"Wagner","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mario","family":"Medvedovic","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2010,5,7]]},"reference":[{"key":"3691_CR1","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1038\/nrg1749","volume":"7","author":"DB Allison","year":"2006","unstructured":"Allison DB, Cui X, Page GP, Sabripour M: Microarray data analysis: from disarray to consolidation and consensus. Nat Rev Genet 2006, 7: 55\u201365. 10.1038\/nrg1749","journal-title":"Nat Rev Genet"},{"key":"3691_CR2","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1089\/omi.2006.10.507","volume":"10","author":"N Belacel","year":"2006","unstructured":"Belacel N, Wang Q, Cuperlovic-Culf M: Clustering methods for microarray gene expression data. OMICS 2006, 10: 507\u2013531. 10.1089\/omi.2006.10.507","journal-title":"OMICS"},{"key":"3691_CR3","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1016\/j.compbiomed.2007.11.001","volume":"38","author":"G Kerr","year":"2008","unstructured":"Kerr G, Ruskin HJ, Crane M, Doolan P: Techniques for clustering gene expression data. Comput Biol Med 2008, 38: 283\u2013293. 10.1016\/j.compbiomed.2007.11.001","journal-title":"Comput Biol Med"},{"key":"3691_CR4","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: 6745\u20136750. 10.1073\/pnas.96.12.6745","journal-title":"Proc Natl Acad Sci USA"},{"key":"3691_CR5","first-page":"93","volume":"8","author":"Y Cheng","year":"2000","unstructured":"Cheng Y, Church GM: Biclustering of expression data. Proc Int Conf Intell Syst Mol Biol 2000, 8: 93\u2013103.","journal-title":"Proc Int Conf Intell Syst Mol Biol"},{"issue":"Suppl 1","key":"3691_CR6","doi-asserted-by":"publisher","first-page":"S136","DOI":"10.1093\/bioinformatics\/18.suppl_1.S136","volume":"18","author":"A Tanay","year":"2002","unstructured":"Tanay A, Sharan R, Shamir R: Discovering statistically significant biclusters in gene expression data. Bioinformatics 2002, 18(Suppl 1):S136-S144.","journal-title":"Bioinformatics"},{"key":"3691_CR7","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1093\/bioinformatics\/btl560","volume":"23","author":"X Liu","year":"2007","unstructured":"Liu X, Wang L: Computing the maximum similarity bi-clusters of gene expression data. Bioinformatics 2007, 23: 50\u201356. 10.1093\/bioinformatics\/btl560","journal-title":"Bioinformatics"},{"key":"3691_CR8","doi-asserted-by":"publisher","first-page":"1122","DOI":"10.1093\/bioinformatics\/btl060","volume":"22","author":"A Prelic","year":"2006","unstructured":"Prelic A, Bleuler S, Zimmermann P, Wille A, Buhlmann P, Gruissem W, Hennig L, Thiele L, Zitzler E: A systematic comparison and evaluation of biclustering methods for gene expression data. Bioinformatics 2006, 22: 1122\u20131129. 10.1093\/bioinformatics\/btl060","journal-title":"Bioinformatics"},{"key":"3691_CR9","doi-asserted-by":"publisher","first-page":"1337","DOI":"10.1038\/nbt890","volume":"21","author":"Z Bar-Joseph","year":"2003","unstructured":"Bar-Joseph Z, Gerber GK, Lee TI, Rinaldi NJ, Yoo JY, Robert F, Gordon DB, Fraenkel E, Jaakkola TS, Young RA, et al.: Computational discovery of gene modules and regulatory networks. Nat Biotechnol 2003, 21: 1337\u20131342. 10.1038\/nbt890","journal-title":"Nat Biotechnol"},{"key":"3691_CR10","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1038\/ng1165","volume":"34","author":"E Segal","year":"2003","unstructured":"Segal E, Shpira M, Regev A, Pe'er D, Koller D, Friedman N: Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nat Genet 2003, 34: 166\u2013176. 10.1038\/ng1165","journal-title":"Nat Genet"},{"key":"3691_CR11","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1186\/1471-2105-7-280","volume":"7","author":"DJ Reiss","year":"2006","unstructured":"Reiss DJ, Baliga NS, Bonneau R: Integrated biclustering of heterogeneous genome-wide datasets for the inference of global regulatory networks. BMC Bioinformatics 2006, 7: 280. 10.1186\/1471-2105-7-280","journal-title":"BMC Bioinformatics"},{"key":"3691_CR12","doi-asserted-by":"publisher","first-page":"3267","DOI":"10.1093\/bioinformatics\/btp588","volume":"25","author":"C Huttenhower","year":"2009","unstructured":"Huttenhower C, Mutungu KT, Indik N, Yang W, Schroeder M, Forman JJ, Troyanskaya OG, Coller HA: Detailing regulatory networks through large scale data integration. Bioinformatics 2009, 25: 3267\u20133274. 10.1093\/bioinformatics\/btp588","journal-title":"Bioinformatics"},{"key":"3691_CR13","doi-asserted-by":"publisher","first-page":"4348","DOI":"10.1093\/bioinformatics\/bti722","volume":"21","author":"JK Choi","year":"2005","unstructured":"Choi JK, Yu U, Yoo OJ, Kim S: Differential coexpression analysis using microarray data and its application to human cancer. Bioinformatics 2005, 21: 4348\u20134355. 10.1093\/bioinformatics\/bti722","journal-title":"Bioinformatics"},{"key":"3691_CR14","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1186\/1471-2105-10-109","volume":"10","author":"SB Cho","year":"2009","unstructured":"Cho SB, Kim J, Kim JH: Identifying set-wise differential co-expression in gene expression microarray data. BMC Bioinformatics 2009, 10: 109. 10.1186\/1471-2105-10-109","journal-title":"BMC Bioinformatics"},{"key":"3691_CR15","volume-title":"Bioinformatics","author":"Y Choi","year":"2009","unstructured":"Choi Y, Kendziorski C: Statistical Methods for Gene Set Co-expression Analysis. Bioinformatics 2009."},{"key":"3691_CR16","doi-asserted-by":"publisher","first-page":"3146","DOI":"10.1093\/bioinformatics\/bth379","volume":"20","author":"Y Lai","year":"2004","unstructured":"Lai Y, Wu B, Chen L, Zhao H: A statistical method for identifying differential gene-gene co-expression patterns. Bioinformatics 2004, 20: 3146\u20133155. 10.1093\/bioinformatics\/bth379","journal-title":"Bioinformatics"},{"issue":"Suppl 1","key":"3691_CR17","doi-asserted-by":"publisher","first-page":"i194","DOI":"10.1093\/bioinformatics\/bth909","volume":"20","author":"D Kostka","year":"2004","unstructured":"Kostka D, Spang R: Finding disease specific alterations in the co-expression of genes. Bioinformatics 2004, 20(Suppl 1):i194-i199. 10.1093\/bioinformatics\/bth909","journal-title":"Bioinformatics"},{"key":"3691_CR18","doi-asserted-by":"publisher","first-page":"509","DOI":"10.1186\/1471-2105-7-509","volume":"7","author":"M Watson","year":"2006","unstructured":"Watson M: CoXpress: differential co-expression in gene expression data. BMC Bioinformatics 2006, 7: 509. 10.1186\/1471-2105-7-509","journal-title":"BMC Bioinformatics"},{"key":"3691_CR19","doi-asserted-by":"publisher","first-page":"e1000382","DOI":"10.1371\/journal.pcbi.1000382","volume":"5","author":"NJ Hudson","year":"2009","unstructured":"Hudson NJ, Reverter A, Dalrymple BP: A differential wiring analysis of expression data correctly identifies the gene containing the causal mutation. PLoS Comput Biol 2009, 5: e1000382. 10.1371\/journal.pcbi.1000382","journal-title":"PLoS Comput Biol"},{"key":"3691_CR20","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1214\/aos\/1176342360","volume":"1","author":"TS Ferguson","year":"1973","unstructured":"Ferguson TS: A Bayesian analysis of some nonparametric problems. The Annals of Statistics 1973, 1: 209\u2013230. 10.1214\/aos\/1176342360","journal-title":"The Annals of Statistics"},{"key":"3691_CR21","doi-asserted-by":"publisher","first-page":"249","DOI":"10.2307\/1390653","volume":"9","author":"RM Neal","year":"2000","unstructured":"Neal RM: Markov Chain Sampling Methods for Dirichlet Process Mixture Models. Journal of Computational and Graphical Statistics 2000, 9: 249\u2013265. 10.2307\/1390653","journal-title":"Journal of Computational and Graphical Statistics"},{"key":"3691_CR22","volume-title":"Critical Assessment of Microarray Data Analysis (CAMDA)","author":"M Medvedovic","year":"2000","unstructured":"Medvedovic M: Identifying statistically significant patterns of expression via Bayesian Infinite Mixture Models. Critical Assessment of Microarray Data Analysis (CAMDA) 2000."},{"key":"3691_CR23","doi-asserted-by":"publisher","first-page":"1194","DOI":"10.1093\/bioinformatics\/18.9.1194","volume":"18","author":"M Medvedovic","year":"2002","unstructured":"Medvedovic M, Sivaganesan S: Bayesian infinite mixture model based clustering of gene expression profiles. Bioinformatics 2002, 18: 1194\u20131206. 10.1093\/bioinformatics\/18.9.1194","journal-title":"Bioinformatics"},{"key":"3691_CR24","doi-asserted-by":"publisher","first-page":"1222","DOI":"10.1093\/bioinformatics\/bth068","volume":"20","author":"M Medvedovic","year":"2004","unstructured":"Medvedovic M, Yeung KY, Bumgarner RE: Bayesian mixture model based clustering of replicated microarray data. Bioinformatics 2004, 20: 1222\u20131232. 10.1093\/bioinformatics\/bth068","journal-title":"Bioinformatics"},{"key":"3691_CR25","volume-title":"BIOKDD","author":"M Medvedovic","year":"2004","unstructured":"Medvedovic M, Guo J: Bayesian Model-Averaging in Unsupervised Learing From Microarray Data. BIOKDD 2004."},{"key":"3691_CR26","doi-asserted-by":"publisher","first-page":"1737","DOI":"10.1093\/bioinformatics\/btl184","volume":"22","author":"X Liu","year":"2006","unstructured":"Liu X, Sivaganesan S, Yeung KY, Guo J, Bumgarner RE, Medvedovic M: Context-specific infinite mixtures for clustering gene expression profiles across diverse microarray dataset. Bioinformatics 2006, 22: 1737\u20131744. 10.1093\/bioinformatics\/btl184","journal-title":"Bioinformatics"},{"key":"3691_CR27","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1186\/1471-2105-8-283","volume":"8","author":"X Liu","year":"2007","unstructured":"Liu X, Jessen WJ, Sivaganesan S, Aronow BJ, Medvedovic M: Bayesian hierarchical model for transcriptional module discovery by jointly modeling gene expression and ChIP-chip data. BMC Bioinformatics 2007, 8: 283. 10.1186\/1471-2105-8-283","journal-title":"BMC Bioinformatics"},{"key":"3691_CR28","doi-asserted-by":"publisher","first-page":"10869","DOI":"10.1073\/pnas.191367098","volume":"98","author":"T Sorlie","year":"2001","unstructured":"Sorlie T, Perou CM, Tibshirani R, Aas T, Geisler S, Johnsen H, Hastie T, Eisen MB, van de RM, Jeffrey SS, et al.: Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci USA 2001, 98: 10869\u201310874. 10.1073\/pnas.191367098","journal-title":"Proc Natl Acad Sci USA"},{"key":"3691_CR29","doi-asserted-by":"publisher","first-page":"530","DOI":"10.1038\/415530a","volume":"415","author":"V van'","year":"2002","unstructured":"van' V, Dai H, van d V, He YD, Hart AA, Mao M, Peterse HL, van der KK, Marton MJ, Witteveen AT, et al.: Gene expression profiling predicts clinical outcome of breast cancer. Nature 2002, 415: 530\u2013536. 10.1038\/415530a","journal-title":"Nature"},{"key":"3691_CR30","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1093\/jnci\/djj052","volume":"98","author":"C Sotiriou","year":"2006","unstructured":"Sotiriou C, Wirapati P, Loi S, Harris A, Fox S, Smeds J, Nordgren H, Farmer P, Praz V, Haibe-Kains B, et al.: Gene Expression Profiling in Breast Cancer: Understanding the Molecular Basis of Histologic Grade To Improve Prognosis. J Natl Cancer Inst 2006, 98: 262\u2013272. 10.1093\/jnci\/djj052","journal-title":"J Natl Cancer Inst"},{"key":"3691_CR31","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1186\/1471-2164-7-96","volume":"7","author":"Z Hu","year":"2006","unstructured":"Hu Z, Fan C, Oh DS, Marron JS, He X, Qaqish BF, Livasy C, Carey LA, Reynolds E, Dressler L, et al.: The molecular portraits of breast tumors are conserved across microarray platforms. BMC Genomics 2006, 7: 96. 10.1186\/1471-2164-7-96","journal-title":"BMC Genomics"},{"key":"3691_CR32","doi-asserted-by":"publisher","first-page":"790","DOI":"10.1056\/NEJMra0801289","volume":"360","author":"C Sotiriou","year":"2009","unstructured":"Sotiriou C, Pusztai L: Gene-expression signatures in breast cancer. N Engl J Med 2009, 360: 790\u2013800. 10.1056\/NEJMra0801289","journal-title":"N Engl J Med"},{"key":"3691_CR33","volume-title":"Probabilistic Networks and Expert Systems","author":"RG Cowell","year":"1999","unstructured":"Cowell RG, Dawid PA, Lauritzen SL, Spiegelhalter DJ: Probabilistic Networks and Expert Systems. New York: Springer; 1999."},{"key":"3691_CR34","doi-asserted-by":"publisher","first-page":"D885","DOI":"10.1093\/nar\/gkn764","volume":"37","author":"T Barrett","year":"2009","unstructured":"Barrett T, Troup DB, Wilhite SE, Ledoux P, Rudnev D, Evangelista C, Kim IF, Soboleva A, Tomashevsky M, Marshall KA, et al.: NCBI GEO: archive for high-throughput functional genomic data. Nucleic Acids Res 2009, 37: D885-D890. 10.1093\/nar\/gkn764","journal-title":"Nucleic Acids Res"},{"key":"3691_CR35","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1186\/1471-2164-11-27","volume":"11","author":"K Shinde","year":"2010","unstructured":"Shinde K, Phatak M, Freudenberg JM, Chen J, Li Q, Joshi VK, Hu Z, Ghosh K, Meller J, Medvedovic M: Genomics Portals: integrative web-platform for mining genomics data. BMC Genomics 2010, 11: 27. 10.1186\/1471-2164-11-27","journal-title":"BMC Genomics"},{"key":"3691_CR36","doi-asserted-by":"publisher","first-page":"1282","DOI":"10.1093\/bioinformatics\/btl099","volume":"22","author":"S Barkow","year":"2006","unstructured":"Barkow S, Bleuler S, Prelic A, Zimmermann P, Zitzler E: BicAT: a biclustering analysis toolbox. Bioinformatics 2006, 22: 1282\u20131283. 10.1093\/bioinformatics\/btl099","journal-title":"Bioinformatics"},{"key":"3691_CR37","doi-asserted-by":"publisher","first-page":"5405","DOI":"10.1158\/0008-5472.CAN-07-5206","volume":"68","author":"M Schmidt","year":"2008","unstructured":"Schmidt M, Bohm D, von TC, Steiner E, Puhl A, Pilch H, Lehr HA, Hengstler JG, Kolbl H, Gehrmann M: The humoral immune system has a key prognostic impact in node-negative breast cancer. Cancer Res 2008, 68: 5405\u20135413. 10.1158\/0008-5472.CAN-07-5206","journal-title":"Cancer Res"},{"key":"3691_CR38","doi-asserted-by":"publisher","first-page":"1289","DOI":"10.1038\/ng1901","volume":"38","author":"JS Carroll","year":"2006","unstructured":"Carroll JS, Meyer CA, Song J, Li W, Geistlinger TR, Eeckhoute J, Brodsky AS, Keeton EK, Fertuck KC, Hall GF, et al.: Genome-wide analysis of estrogen receptor binding sites. Nat Genet 2006, 38: 1289\u20131297. 10.1038\/ng1901","journal-title":"Nat Genet"},{"key":"3691_CR39","doi-asserted-by":"publisher","first-page":"2200","DOI":"10.1093\/bioinformatics\/btn374","volume":"24","author":"B Haibe-Kains","year":"2008","unstructured":"Haibe-Kains B, Desmedt C, Sotiriou C, Bontempi G: A comparative study of survival models for breast cancer prognostication based on microarray data: does a single gene beat them all? Bioinformatics 2008, 24: 2200\u20132208. 10.1093\/bioinformatics\/btn374","journal-title":"Bioinformatics"},{"key":"3691_CR40","doi-asserted-by":"publisher","first-page":"13550","DOI":"10.1073\/pnas.0506230102","volume":"102","author":"LD Miller","year":"2005","unstructured":"Miller LD, Smeds J, George J, Vega VB, Vergara L, Ploner A, Pawitan Y, Hall P, Klaar S, Liu ET, et al.: From The Cover: An expression signature for p53 status in human breast cancer predicts mutation status, transcriptional effects, and patient survival. PNAS 2005, 102: 13550\u201313555. 10.1073\/pnas.0506230102","journal-title":"PNAS"},{"key":"3691_CR41","doi-asserted-by":"publisher","first-page":"3207","DOI":"10.1158\/1078-0432.CCR-06-2765","volume":"13","author":"C Desmedt","year":"2007","unstructured":"Desmedt C, Piette F, Loi S, Wang Y, Lallemand F, Haibe-Kains B, Viale G, Delorenzi M, Zhang Y, d'Assignies MS, et al.: Strong time dependence of the 76-gene prognostic signature for node-negative breast cancer patients in the TRANSBIG multicenter independent validation series. Clin Cancer Res 2007, 13: 3207\u20133214. 10.1158\/1078-0432.CCR-06-2765","journal-title":"Clin Cancer Res"},{"key":"3691_CR42","doi-asserted-by":"publisher","first-page":"R953","DOI":"10.1186\/bcr1325","volume":"7","author":"Y Pawitan","year":"2005","unstructured":"Pawitan Y, Bjohle J, Amler L, Borg AL, Egyhazi S, Hall P, Han X, Holmberg L, Huang F, Klaar S, et al.: Gene expression profiling spares early breast cancer patients from adjuvant therapy: derived and validated in two population-based cohorts. Breast Cancer Res 2005, 7: R953-R964. 10.1186\/bcr1325","journal-title":"Breast Cancer Res"},{"key":"3691_CR43","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1186\/1471-2164-9-239","volume":"9","author":"S Loi","year":"2008","unstructured":"Loi S, Haibe-Kains B, Desmedt C, Wirapati P, Lallemand F, Tutt AM, Gillet C, Ellis P, Ryder K, Reid JF, et al.: Predicting prognosis using molecular profiling in estrogen receptor-positive breast cancer treated with tamoxifen. BMC Genomics 2008, 9: 239. 10.1186\/1471-2164-9-239","journal-title":"BMC Genomics"},{"key":"3691_CR44","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1093\/nar\/gkm945","volume":"36","author":"V Bourdeau","year":"2008","unstructured":"Bourdeau V, Deschenes J, Laperriere D, Aid M, White JH, Mader S: Mechanisms of primary and secondary estrogen target gene regulation in breast cancer cells. Nucl Acids Res 2008, 36: 76\u201393. 10.1093\/nar\/gkm945","journal-title":"Nucl Acids Res"},{"key":"3691_CR45","doi-asserted-by":"publisher","first-page":"535","DOI":"10.1677\/jme.1.01677","volume":"34","author":"JG Moggs","year":"2005","unstructured":"Moggs JG, Murphy TC, Lim FL, Moore DJ, Stuckey R, Antrobus K, Kimber I, Orphanides G: Anti-proliferative effect of estrogen in breast cancer cells that re-express ER{alpha} is mediated by aberrant regulation of cell cycle genes. J Mol Endocrinol 2005, 34: 535\u2013551. 10.1677\/jme.1.01677","journal-title":"J Mol Endocrinol"},{"key":"3691_CR46","doi-asserted-by":"publisher","first-page":"353","DOI":"10.1038\/nature04296","volume":"439","author":"AH Bild","year":"2006","unstructured":"Bild AH, Yao G, Chang JT, Wang Q, Potti A, Chasse D, Joshi MB, Harpole D, Lancaster JM, Berchuck A, et al.: Oncogenic pathway signatures in human cancers as a guide to targeted therapies. Nature 2006, 439: 353\u2013357. 10.1038\/nature04296","journal-title":"Nature"},{"key":"3691_CR47","volume-title":"Endocr Relat Cancer","author":"WJ Welboren","year":"2009","unstructured":"Welboren WJ, Sweep FCGJ, Span P, Stunnenberg H: Genomic actions of estrogen receptor {alpha}: what are the targets and how are they regulated? Endocr Relat Cancer 2009. ERC-09 ERC-09"},{"key":"3691_CR48","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1186\/1755-8794-1-11","volume":"1","author":"JD Mosley","year":"2008","unstructured":"Mosley JD, Keri RA: Cell cycle correlated genes dictate the prognostic power of breast cancer gene lists. BMC Med Genomics 2008, 1: 11. 10.1186\/1755-8794-1-11","journal-title":"BMC Med Genomics"},{"key":"3691_CR49","doi-asserted-by":"publisher","first-page":"747","DOI":"10.1038\/35021093","volume":"406","author":"CM Perou","year":"2000","unstructured":"Perou CM, Sorlie T, Eisen MB, van de RM, Jeffrey SS, Rees CA, Pollack JR, Ross DT, Johnsen H, Akslen LA, et al.: Molecular portraits of human breast tumours. Nature 2000, 406: 747\u2013752. 10.1038\/35021093","journal-title":"Nature"},{"key":"3691_CR50","doi-asserted-by":"publisher","first-page":"8418","DOI":"10.1073\/pnas.0932692100","volume":"100","author":"T Sorlie","year":"2003","unstructured":"Sorlie T, Tibshirani R, Parker J, Hastie T, Marron JS, Nobel A, Deng S, Johnsen H, Pesich R, Geisler S, et al.: Repeated observation of breast tumor subtypes in independent gene expression data sets. Proc Natl Acad Sci USA 2003, 100: 8418\u20138423. 10.1073\/pnas.0932692100","journal-title":"Proc Natl Acad Sci USA"},{"key":"3691_CR51","doi-asserted-by":"publisher","first-page":"398","DOI":"10.1080\/01621459.1990.10476213","volume":"85","author":"EA Gelfand","year":"1990","unstructured":"Gelfand EA, Smith FMA: Sampling-based approaches to calculating marginal densities. Journal of the American Statistical Association 1990, 85: 398\u2013409. 10.2307\/2289776","journal-title":"Journal of the American Statistical Association"},{"key":"3691_CR52","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1093\/bioinformatics\/19.2.185","volume":"19","author":"BM Bolstad","year":"2003","unstructured":"Bolstad BM, Irizarry RA, Astrand M, Speed TP: A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 2003, 19: 185\u2013193. 10.1093\/bioinformatics\/19.2.185","journal-title":"Bioinformatics"},{"key":"3691_CR53","doi-asserted-by":"publisher","first-page":"e175","DOI":"10.1093\/nar\/gni179","volume":"33","author":"M Dai","year":"2005","unstructured":"Dai M, Wang P, Boyd AD, Kostov G, Athey B, Jones EG, Bunney WE, Myers RM, Speed TP, Akil H, et al.: Evolving gene\/transcript definitions significantly alter the interpretation of GeneChip data. Nucl Acids Res 2005, 33: e175. 10.1093\/nar\/gni179","journal-title":"Nucl Acids Res"},{"key":"3691_CR54","doi-asserted-by":"publisher","first-page":"644","DOI":"10.1126\/science.1117679","volume":"310","author":"SA Tomlins","year":"2005","unstructured":"Tomlins SA, Rhodes DR, Perner S, Dhanasekaran SM, Mehra R, Sun XW, Varambally S, Cao X, Tchinda J, Kuefer R, et al.: Recurrent fusion of TMPRSS2 and ETS transcription factor genes in prostate cancer. Science 2005, 310: 644\u2013648. 10.1126\/science.1117679","journal-title":"Science"},{"key":"3691_CR55","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1186\/1471-2105-10-234","volume":"10","author":"JM Freudenberg","year":"2009","unstructured":"Freudenberg JM, Joshi VK, Hu Z, Medvedovic M: CLEAN: CLustering Enrichment ANalysis. BMC Bioinformatics 2009, 10: 234. 10.1186\/1471-2105-10-234","journal-title":"BMC Bioinformatics"},{"key":"3691_CR56","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1093\/bioinformatics\/btn592","volume":"25","author":"MA Sartor","year":"2009","unstructured":"Sartor MA, Leikauf GD, Medvedovic M: LRpath: a logistic regression approach for identifying enriched biological groups in gene expression data. Bioinformatics 2009, 25: 211\u2013217. 10.1093\/bioinformatics\/btn592","journal-title":"Bioinformatics"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/1471-2105-11-234.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,1]],"date-time":"2021-09-01T12:15:47Z","timestamp":1630498547000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/1471-2105-11-234"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2010,5,7]]},"references-count":56,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2010,12]]}},"alternative-id":["3691"],"URL":"https:\/\/doi.org\/10.1186\/1471-2105-11-234","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2010,5,7]]},"assertion":[{"value":"17 December 2009","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 May 2010","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 May 2010","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"234"}}