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Open Access Highly Accessed Research article

Prediction of breast cancer prognosis using gene set statistics provides signature stability and biological context

Gad Abraham, Adam Kowalczyk, Sherene Loi, Izhak Haviv and Justin Zobel*

BMC Bioinformatics 2010, 11:277  doi:10.1186/1471-2105-11-277

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Persistent Digital Object Identifiers (DOIs) now available

Gad Abraham   (2011-11-08 16:16)  NICTA VRL, Dept Computer Science and Software Engineering, The University of Melbourne

The input data, computational model, and output data from this study have been persistently identified using DOIs (minted using the Australian National Data Service (ANDS) data citation service http://ands.org.au/cite-data/), and each can be re-used and cited independently:

Input Microarray Dataset:
Abraham, G; Kowalczyk, A; Loi, S; Haviv, I; Zobel, J. (2011) Five human breast cancer microarray gene expression datasets. Computer Science and Software Engineering, The University of Melbourne. doi:10.4225/02/4E9F695934393

Computational Model:
Abraham, G; Kowalczyk, A; Loi, S; Haviv, I; Zobel, J. (2011) Computational Model for Gene Set Analysis to predict breast cancer prognosis based on microarray gene expression data. Computer Science and Software Engineering, The University of Melbourne. doi:10.4225/02/4E9F69C011BC8

Output Data:
Abraham, G; Kowalczyk, A; Loi, S; Haviv, I; Zobel, J. (2011) Prognostic gene set signatures derived from breast cancer microarray gene expression data. Computer Science and Software Engineering, The University of Melbourne. doi:10.4225/02/4E9F69F7AE206

Competing interests

None

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