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Open Access Highly Accessed Research article

Prognostic gene signatures for patient stratification in breast cancer - accuracy, stability and interpretability of gene selection approaches using prior knowledge on protein-protein interactions

Yupeng Cun and Holger FröhlichHolger Fröhlich*

Author Affiliations

Algorithmic Bioinformatics, Bonn-Aachen International Center for IT, Dahlmannstraße. 2, 53113, Bonn, Germany

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BMC Bioinformatics 2012, 13:69  doi:10.1186/1471-2105-13-69

Published: 1 May 2012

Additional files

Additional file 1:

Table S1. Known drug targets for breast neoplasms.

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Additional file 2:

Figure S1. Stability of each gene selection methods. The y-axis shows the fraction of genes, being selected 1–10, 11–20, 21–30, 31–40, 41–50, 51–60, 61–70, 71–80, 81–90 and 91–100 times. PAM (prediction analysis of microarray data), sigGenNB (SAM + Naïve Bayes), sigGenSVM (SAM + SVM),SCAD-SVM, HHSVM (Huberized Hinge loss SVM), RFE (Recursive Feature Elimination), RRFE (Reweighted Recursive Feature Elimination), graphK (graph diffusion kernels for SVMs), graphKp (p-step random walk graph kernel for SVMs), networkSVM (Network-based SVM), PAC (Pathway Activity Classification), aveExp-Path (average pathway expression), HubClassify (classification by significant hub genes), pathBoost.

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Additional file 3:

Figure S2. Median AUC values across all datasets.

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Additional file 4:

Table S2. Tukey's post-hoc test analysis for AUC values (5% significance cutoff).

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Additional file 5:

Table S3. Tukey's post-hoc test analysis for SI values (5% significance cutoff).

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Additional file 6:

Figure S3. Number of selected genes per method.

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Additional file 7:

Figure S4. Fraction of differentially expression genes in signatures.

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