Biclustering reveals breast cancer tumour subgroups with common clinical features and improves prediction of disease recurrence
Open accessAbstract
BACKGROUND: Many studies have revealed correlations between breast tumour phenotypes, variations in gene expression, and patient survival outcomes. The molecular heterogeneity between breast tumours revealed by these studies has allowed prediction of prognosis and has underpinned stratified therapy, where groups of patients with particular tumour types receive specific treatments. The molecular tests used to predict prognosis and stratify treatment usually utilise fixed sets of genomic biomarkers, with the same biomarker sets being used to test all patients. In this paper we suggest that instead of fixed sets of genomic biomarkers, it may be more effective to use a stratified biomarker approach, where optimal biomarker sets are automatically chosen for particular patient groups, analogous to the choice of optimal treatments for groups of similar patients in stratified therapy. We illustrate the effectiveness of a biclustering approach to select optimal gene sets for determining the prognosis of specific strata of patients, based on potentially overlapping, non-discrete molecular characteristics of tumours. RESULTS: Biclustering identified tightly co-expressed gene sets in the tumours of restricted subgroups of breast cancer patients. The co-expressed genes in these biclusters were significantly enriched for particular biological annotations and gene regulatory modules associated with breast cancer biology. Tumours identified within the same bicluster were more likely to present with similar clinical features. Bicluster membership combined with clinical information could predict patient prognosis in conditional inference tree and ridge regression class prediction models. CONCLUSIONS: The increasing clinical use of genomic profiling demands identification of more effective methods to segregate patients into prognostic and treatment groups. We have shown that biclustering can be used to select optimal gene sets for determining the prognosis of specific strata of patients.
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Even when no sample classification is available, subpopulation-specific modules can be resolved, an approach that has been particularly successful in classifying different cancer subtypes to provide prognostic markers.
“Even when no sample classification is available, subpopulation-specific modules can be resolved, an approach that has been particularly successful in classifying different cancer subtypes to provide prognostic markers [18–20].”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [18]
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1 retracted
Acharya, C. R., Hsu, D. S., Anders, C. K., Anguiano, A., Salter, K. H., Walters, K. S., Redman, R. C., Tuchman, S. A., Moylan, C. A., Mukherjee, S., et al. (2008) · JAMA · doi:10.1001/jama.299.13.1574 · Cited by 108 · see citing works
RetractedMolecular portraits of human breast tumours
Perou, C., Sørlie, T., Eisen, M., van de Rijn, M., Jeffrey, S., Rees, C. A., Pollack, J., Ross, D., Johnsen, H., Akslen, L., et al. (2000) · Nature · doi:10.1038/35021093 · Cited by 16,551 · see citing works
Nishimura, R., Osako, T., Nishiyama, Y., Tashima, R., Nakano, M., Fujisue, M., Toyozumi, Y., & Arima, N. (2013) · Oncology · doi:10.1159/000353099 · Cited by 13,239 · see citing works
Sørlie, T., Perou, C., Tibshirani, R., Aas, T., Geisler, S., Johnsen, H., Hastie, T., Eisen, M., van de Rijn, M., Jeffrey, S., et al. (2001) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.191367098 · Cited by 11,695 · see citing works
Gene expression profiling predicts clinical outcome of breast cancer
van 't Veer, L. J., Dai, H., van de Vijver, M. J., He, Y. D., Hart, A. A. M., Mao, M., Peterse, H. L., van der Kooy, K., Marton, M. J., Witteveen, A. T., et al. (2002) · Nature · doi:10.1038/415530a · Cited by 9,536 · see citing works
Adjusting batch effects in microarray expression data using empirical Bayes methods
Johnson, W. E., Li, C., & Rabinovic, A. (2007) · Biostatistics · doi:10.1093/biostatistics/kxj037 · Cited by 8,658 · see citing works
The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups
METABRIC Group, Curtis, C., Shah, S. P., Chin, S.-F., Turashvili, G., Rueda, O. M., Dunning, M. J., Speed, D., Lynch, A. G., Samarajiwa, S., et al. (2012) · Nature · doi:10.1038/nature10983 · Cited by 6,097 · see citing works
A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer.
Paik, S., Shak, S., Tang, G., Kim, C., Baker, J., Cronin, M., Baehner, F., Walker, M. G., Watson, D., Park, T., et al. (2004) · New England Journal of Medicine · doi:10.1056/nejmoa041588 · Cited by 6,009 · see citing works
Repeated observation of breast tumor subtypes in independent gene expression data sets
Sørlie, T., Tibshirani, R. J., Parker, J. S., Hastie, T. J., Marron, J. S., Nobel, A. B., Deng, S., Johnsen, H., Pesich, R., Geisler, S. B., et al. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0932692100 · Cited by 5,428 · see citing works
Ki67 expression in breast cancer
Elkablawy, M. A., Albasri, A. M., Mohammed, R. A., Hussainy, A. S., Nouh, M. M., & Alhujaily, A. S. (2016) · Saudi Medical Journal · doi:10.15537/smj.2016.2.12285 · Cited by 4,382 · see citing works
Unbiased Recursive Partitioning: A Conditional Inference Framework
Hothorn, T., Hornik, K., & Zeileis, A. (2006) · Journal of Computational and Graphical Statistics · doi:10.1198/106186006x133933 · Cited by 4,021 · see citing works
Diagnosis of multiple cancer types by shrunken centroids of gene expression
Tibshirani, R. J., Hastie, T. J., Narasimhan, B., & Chu, G. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.082099299 · Cited by 2,938 · see citing works
Biclustering of expression data.
Cheng, Y., & Church, G. M. (2000) · PubMed · Cited by 2,098 · see citing works
Sotiriou, C. P., Wirapati, P., Loi, S., Harris, A. L., Fox, S., Smeds, J., Nordgren, H., Farmer, P. J., Praz, V., Haibe‐Kains, B., et al. (2006) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djj052 · Cited by 2,060 · see citing works
Biclustering algorithms for biological data analysis: a survey
Madeira, S. C., & Oliveira, A. L. (2004) · IEEE/ACM Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2004.2 · Cited by 2,049 · see citing works
Discovering statistically significant biclusters in gene expression data
Tanay, A., Sharan, R., & Ron Shamir, A. (2002) · Bioinformatics · doi:10.1093/bioinformatics/18.suppl_1.s136 · Cited by 857 · see citing works
Genetic Reclassification of Histologic Grade Delineates New Clinical Subtypes of Breast Cancer
Ivshina, A. V., George, J., Sen'ko, O. V., Mow, B. M. F., Putti, T. C., Smeds, J., Lindahl, T., Yudi Pawitan, Y., Hall, P. N., Nordgren, H., et al. (2006) · Cancer Research · doi:10.1158/0008-5472.can-05-4414 · Cited by 751 · see citing works
Discovering Local Structure in Gene Expression Data: The Order-Preserving Submatrix Problem
Ben‐Dor, A., Chor, B., Karp, R. M., & Zohar Yakhini, B. (2003) · Journal of Computational Biology · doi:10.1089/10665270360688075 · Cited by 546 · see citing works
Gene expression profiling predicts clinical outcome of breast cancer
(2002) · Nature · doi:10.1038/news020128-6 · Cited by 530 · see citing works
Bonneau, R., Reiss, D. J., Shannon, P., Facciotti, M., Hood, L., Baliga, N. S., & Thorsson, V. (2006) · Genome Biology · doi:10.1186/gb-2006-7-5-r36 · Cited by 510 · see citing works
Iterative signature algorithm for the analysis of large-scale gene expression data
Bergmann, S., Ihmels, J., & Barkai, N. (2003) · Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · doi:10.1103/physreve.67.031902 · Cited by 422 · see citing works
Molecular portraits of breast cancer: tumour subtypes as distinct disease entities
Sørlie, T. (2004) · European Journal of Cancer · doi:10.1016/j.ejca.2004.08.021 · Cited by 416 · see citing works
EXTRACTING CONSERVED GENE EXPRESSION MOTIFS FROM GENE EXPRESSION DATA
Murali, T. M., & Kasif, S. (2002) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789812776303_0008 · Cited by 385 · see citing works
Predicting survival from microarray data—a comparative study
Bøvelstad, H. M., Nygård, S., Størvold, H. L., Aldrin, M., Borgan, Ø., Frigessi, A., & Lingjærde, O. C. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm305 · Cited by 318 · see citing works
Reiss, D. J., Baliga, N. S., & Bonneau, R. (2006) · BMC Bioinformatics · doi:10.1186/1471-2105-7-280 · Cited by 253 · see citing works
Pittman, J. L., Huang, E. S., Dressman, H. K., Horng, C., Cheng, S. H., Tsou, M.-H., Chen, C.-M., Bild, A., Iversen, E. S., Huang, A. T., et al. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0401736101 · Cited by 222 · see citing works
Predictive and prognostic molecular markers for cancer medicine
Mehta, S. Y., Shelling, A. N., Muthukaruppan, A., Lasham, A., Blenkiron, C., Laking, G. R., & Print, C. G. (2010) · Therapeutic Advances in Medical Oncology · doi:10.1177/1758834009360519 · Cited by 210 · see citing works
Self-citationBiclustering of gene expression data by non-smooth non-negative matrix factorization
Carmona‐Sáez, P., Pascual-Marqui, R. D., Tirado, F., Carazo, J. M., & Pascual-Montano, A. (2006) · BMC Bioinformatics · doi:10.1186/1471-2105-7-78 · Cited by 203 · see citing works
Cross‐validated Cox regression on microarray gene expression data
van Houwelingen, H. C., Bruinsma, T. J., Hart, A. A. M., Veer, L. J. V., & Wessels, L. F. A. (2005) · Statistics in Medicine · doi:10.1002/sim.2353 · Cited by 182 · see citing works
Gene Expression Profiling of Breast Cancer: A New Tumor Marker
Veer, L. J. V., Paik, S., & Hayes, D. F. (2005) · Journal of Clinical Oncology · doi:10.1200/jco.2005.12.005 · Cited by 158 · see citing works
Learning biological networks: from modules to dynamics
Bonneau, R. (2008) · Nature Chemical Biology · doi:10.1038/nchembio.122 · Cited by 138 · see citing works
Relative Expression Analysis for Molecular Cancer Diagnosis and Prognosis
Eddy, J. A., Sung, J., Geman, D., & Price, N. D. (2010) · Technology in Cancer Research & Treatment · doi:10.1177/153303461000900204 · Cited by 129 · see citing works
Discovery and validation of breast cancer subtypes
Kapp, A. V., Jeffrey, S. S., Langerød, A., Børresen‐Dale, A., Han, W., Noh, D., Bukholm, I. R., Nicolau, M. M., Brown, P. O., & Tibshirani, R. J. (2006) · BMC Genomics · doi:10.1186/1471-2164-7-231 · Cited by 129 · see citing works
YB-1, the E2F Pathway, and Regulation of Tumor Cell Growth
Lasham, A., Samuel, W., Cao, H., Patel, R., Mehta, R., Stern, J. L., Reid, G., Woolley, A. G., Miller, L. D., Black, M. A., et al. (2011) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djr512 · Cited by 119 · see citing works
Self-citationClassification of Breast Cancer Using Genetic Algorithms and Tissue Microarrays
Dolled-Filhart, M. P., Rydén, L., Cregger, M. A., Jirström, K., Harigopal, M., Camp, R. L., & Rimm, D. L. (2006) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-06-1383 · Cited by 107 · see citing works
Gene Expression Profiling of Breast Cancer
Cheang, M. C. U., van de Rijn, M., & Nielsen, T. O. (2007) · Annual Review of Pathology Mechanisms of Disease · doi:10.1146/annurev.pathmechdis.3.121806.151505 · Cited by 92 · see citing works
Survival prediction from clinico-genomic models - a comparative study
Bøvelstad, H. M., Nygård, S., & Borgan, Ø. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-413 · Cited by 87 · see citing works
Araki, H., Knapp, C., Tsai, P., & Print, C. G. (2012) · FEBS Open Bio · doi:10.1016/j.fob.2012.04.003 · Cited by 80 · see citing works
Self-citationA multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer
Melisko, M. (2005) · The Women s Oncology Review · doi:10.1080/14733400500093379 · Cited by 78 · see citing works
BiGGEsTS: integrated environment for biclustering analysis of time series gene expression data
Gonçalves, J. P., Madeira, S. C., & Oliveira, A. L. (2009) · BMC Research Notes · doi:10.1186/1756-0500-2-124 · Cited by 70 · see citing works
Biclustering of gene expression data using reactive greedy randomized adaptive search procedure
Dharan, S., & Nair, A. S. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-s1-s27 · Cited by 67 · see citing works
Furlan, D., Carnevali, I. W., Bernasconi, B., Sahnane, N., Milani, K., Cerutti, R., Bertolini, V., Chiaravalli, A. M., Bertoni, F., Kwee, I., et al. (2010) · Modern Pathology · doi:10.1038/modpathol.2010.179 · Cited by 28 · see citing works
Prognostic Applications of Gene Expression Signatures in Breast Cancer
Normanno, N., Luca, A. D., Carotenuto, P., Lamura, L., Oliva, I., & D’Alessio, A. (2009) · Oncology · doi:10.1159/000258489 · Cited by 22 · see citing works
Assessment of evaluation criteria for survival prediction from genomic data
Bøvelstad, H. M., & Borgan, Ø. (2011) · Biometrical Journal · doi:10.1002/bimj.201000048 · Cited by 21 · see citing works
Guarneri, V., Barbieri, E., Piacentini, F., Giovannelli, S., Ficarra, G., Frassoldati, A., Maiorana, A., D’Amico, R., & Conte, P. (2010) · The International Journal of Biological Markers · doi:10.1177/172460081002500208 · Cited by 14 · see citing works
Bagging for Biclustering: Application to Microarray Data
Hanczar, B., & Nadif, M. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15880-3_37 · Cited by 14 · see citing works
Noise-robust algorithm for identifying functionally associated biclusters from gene expression data
Ahn, J., Yoon, Y., & Park, S. (2010) · Information Sciences · doi:10.1016/j.ins.2010.10.013 · Cited by 12 · see citing works
(2005) · Breast Diseases A Year Book Quarterly · doi:10.1016/s1043-321x(05)80038-4 · Cited by 11 · see citing works
VisHiC--hierarchical functional enrichment analysis of microarray data
Krushevskaya, D., Peterson, H., Reimand, J., Kull, M., & Vilo, J. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkp435 · Cited by 10 · see citing works
Privacy-preserving models for comparing survival curves using the logrank test
Chen, T., & Zhong, S. (2011) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2011.04.004 · Cited by 10 · see citing works
Gene Expression Profiling of Breast Cancer
Cheang, M. C. U., van de Rijn, M., & Nielsen, T. O. (2007) · Annual Review of Pathology Mechanisms of Disease · doi:10.1146/annurev.pathol.3.121806.151505 · Cited by 8 · see citing works
Biomarker Research in Breast Cancer
Maxwell, C. L. (2010) · Clinical journal of oncology nursing · doi:10.1188/10.cjon.771-783 · Cited by 6 · see citing works
Hierarchical Clustering of Gene Expression Data with Divergence Measure
Liu, W., Wang, T., Chen, S., & Tang, A. (2009) · International Conference on Bioinformatics and Biomedical Engineering · doi:10.1109/icbbe.2009.5162903 · Cited by 4 · see citing works
Shah, S. C., Park, M.-S., Choi, W., Hussain, S., & Bashir, A. K. (2013) · International Conference on Intelligent Robotics and Applications · doi:10.1007/978-3-642-40852-6-13 · Cited by 1 · see citing works
Flagged first, then by citations
1 of the 55 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Gene co-expression analysis for functional classification and gene–disease predictions
van Dam, S., Võsa, U., van der Graaf, A., Franke, L., & de Magalhães, J. P. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbw139 · Cited by 1,086 · see citing works
Lee, J., Yoon, J.-K., Kim, B., Kim, S., Kim, M. A., Lim, H., Bang, D., & Song, Y. S. (2015) · BMC Cancer · doi:10.1186/s12885-015-1077-4 · Cited by 90 · see citing works
Biclustering Methods: Biological Relevance and Application in Gene Expression Analysis
Oghabian, A., Kilpinen, S., Hautaniemi, S., & Czeizler, E. (2014) · PLoS ONE · doi:10.1371/journal.pone.0090801 · Cited by 88 · see citing works
Xie, J., Ma, A., Fennell, A. Y., Ma, Q., & Zhao, J. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bby014 · Cited by 83 · see citing works
Metabolic clusters of breast cancer in relation to gene- and protein expression subtypes
Haukaas, T. H., Euceda, L. R., Giskeødegård, G. F., Lamichhane, S., Krohn, M., Jernström, S., Aure, M. R. R., Lingjærde, O. C., Schlichting, E., Garred, Ø., et al. (2016) · Cancer & Metabolism · doi:10.1186/s40170-016-0152-x · Cited by 76 · see citing works
Analysis of miRNA expression profiles in breast cancer using biclustering
Fiannaca, A., La Rosa, M., La Paglia, L., Rizzo, R., & Urso, A. (2015) · BMC Bioinformatics · doi:10.1186/1471-2105-16-s4-s7 · Cited by 45 · see citing works
Siddique, A. B., Ayoub, N. M., Tajmim, A., Meyer, S. A., Hill, R. A., & Sayed, K. A. E. (2019) · Cancers · doi:10.3390/cancers11050637 · Cited by 39 · see citing works
Biclustering data analysis: a comprehensive survey
Castanho, E. N., Aidos, H., & Madeira, S. C. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae342 · Cited by 35 · see citing works
Zhao, J., Liu, X., Huo, C., Zhao, T., & Ye, H. (2018) · Neuroscience · doi:10.1016/j.neuroscience.2018.07.046 · Cited by 23 · see citing works
Typologies of Suicide: A Critical Literature Review
Martin, J., LaCroix, J. M., Novak, L. A., & Ghahramanlou‐Holloway, M. (2019) · Archives of Suicide Research · doi:10.1080/13811118.2018.1564100 · Cited by 20 · see citing works
Metaheuristic Biclustering Algorithms: From State-of-the-art to Future Opportunities
José-García, A., Jacques, J., Sobanski, V., & Dhaenens, C. (2023) · ACM Computing Surveys · doi:10.1145/3617590 · Cited by 16 · see citing works
Variational algorithms for biclustering models
Vu, D. Q., & Aitkin, M. A. (2015) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2015.02.015 · Cited by 16 · see citing works
Kakati, T., Ahmed, H. A., Bhattacharyya, D. K., & Kalita, J. K. (2018) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2018.05.007 · Cited by 15 · see citing works
Fujita, A., Takahashi, D. Y., Patriota, A. G., & Sato, J. R. (2014) · Statistics in Medicine · doi:10.1002/sim.6292 · Cited by 8 · see citing works
Danziger, S. A., Reiss, D. J., Ratushny, A. V., Smith, J. J., Plaisier, C. L., Aitchison, J. D., & Baliga, N. S. (2015) · BMC Systems Biology · doi:10.1186/1752-0509-9-s2-s1 · Cited by 5 · see citing works
Bioinformatics Advancements for Detecting Epidemic Disease Using Machine Learning Approaches
Baruah, B., & Dutta, M. P. (2020) · Lecture notes in electrical engineering · doi:10.1007/978-981-15-7031-5_100 · Cited by 5 · see citing works
A framework for generalized subspace pattern mining in high-dimensional datasets
Curry, E. W. (2014) · BMC Bioinformatics · doi:10.1186/s12859-014-0355-5 · Cited by 5 · see citing works
Analysis of breast cancer subtypes by AP-ISA biclustering
Yang, L., Shen, Y., Yuan, X., Zhang, J., & Wei, J. (2017) · BMC Bioinformatics · doi:10.1186/s12859-017-1926-z · Cited by 4 · see citing works
Tumor stratification by a novel graph-regularized bi-clique finding algorithm
Ahmadi Adl, A., & Qian, X. (2015) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2015.02.010 · Cited by 3 · see citing works
Renc, P., Orzechowski, P., Byrski, A., Wąs, J., & Moore, J. H. (2021) · Genetic and Evolutionary Computation Conference Companion (GECCO Companion) · doi:10.1145/3449726.3463197 · Cited by 3 · see citing works
Menegollo, M., Bentham, R. B., Henriques, T., Ng, S. Q., Ren, Z., Esculier, C., Agarwal, S., Tong, E. T. Y., Lo, C., Ilangovan, S., et al. (2024) · Cancer Research · doi:10.1158/0008-5472.can-23-3172 · Cited by 2 · see citing works
Optimizing Permutations in Biclustering Algorithms
Shreeram, A., Samantaray, T., & Gupta, C. N. (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-99-7093-3_7 · Cited by 2 · see citing works
ANOCVA: A Nonparametric Statistical Test to Compare Clustering Structures
Patriota, A. G., Vidal, M. C., de Jesus, D. A. C., & Fujita, A. (2018) · Computational biology · doi:10.1007/978-3-319-74974-7_6 · Cited by 1 · see citing works
A Biclustering Approach for Investigating Patterns for Breast Cancer Attributes
Muthukumaran, S., & Velumani, B. (2014) · International Conference on Intelligent Computing Applications (ICICA) · doi:10.1109/icica.2014.14 · Cited by 1 · see citing works
Rank-preserving biclustering algorithm: a case study on miRNA breast cancer
Mandal, K., Sarmah, R., Bhattacharyya, D. K., Kalita, J. K., & Borah, B. (2021) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-020-02271-0 · Cited by 1 · see citing works
A statistical test to identify differences in clustering structuresPreprint
Fujita, A., Takahashi, D. Y., Patriota, A. G., & Sato, J. R. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1311.6732 · Cited by 1 · see citing works
Deep surveys of transcriptional modules with Massive Associative K-biclustering (MAK)Preprint
Joachimiak, M. P., Tuglus, C., Salamzade, R., van der Laan, M. J., & Arkin, A. P. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.08.26.505372 · Cited by 1 · see citing works
Network-regularized bi-clique finding for tumor stratification
Adl, A. A., & Qian, X. (2014) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/2649387.2660832
- Authors
- Wang, Y. K., Print, C. G., & Crampin, E. J.
- Affiliations
- University of Auckland (NZ)
- Corresponding author
- Wang, Yi Kan
- Venue
- BMC Genomics
- Published
- 2013
- Volume
- 14
- Issue
- 1
- Pages
- 102
- ISSN
- 1471-2164 (ISSN-L)
- PMC
- PMC3598775
- OpenAlex
- W2171565067
- Cited by
- 28
- References
- 55
- Citations by year
- 2024: 1 · 2023: 3 · 2022: 1 · 2021: 2 · 2020: 1 · 2019: 2 · 2018: 4 · 2017: 1 · 2016: 2 · 2015: 5 · 2014: 5 · 2013: 1
- FWCI
- 1.94
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- BMC Genomics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Minerva Access (University of Melbourne) (submittedVersion, cc-by)
- Funders
- Royal Society
- Breast Cancer Research Trust
- Royal Society Te Apārangi
- Cancer Research Trust New Zealand
- Marsden Fund
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- breast cancer, biclustering, disease recurrence prediction, prognostic factors, treatment stratification, gene co-expression, gene regulatory modules, patient stratification, conditional inference trees, ridge regression
- MeSH terms
- Adult, Aged, Aged, 80 and over, Algorithms, Breast Neoplasms, Humans, Middle Aged, Recurrence, Biomarkers, Tumor, Cluster Analysis, Disease-Free Survival, Computational Biology, Kaplan-Meier Estimate, Transcriptome
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