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. 2017 Dec 21;11(Suppl 7):138.
doi: 10.1186/s12918-017-0514-1.

Hadamard Kernel SVM with applications for breast cancer outcome predictions

Hao Jiang  1 , Wai-Ki Ching  2 , Wai-Shun Cheung  2 , Wenpin Hou  2 , Hong Yin  3
Affiliations

Hadamard Kernel SVM with applications for breast cancer outcome predictions

Hao Jiang et al. BMC Syst Biol. .

Abstract

Background: Breast cancer is one of the leading causes of deaths for women. It is of great necessity to develop effective methods for breast cancer detection and diagnosis. Recent studies have focused on gene-based signatures for outcome predictions. Kernel SVM for its discriminative power in dealing with small sample pattern recognition problems has attracted a lot attention. But how to select or construct an appropriate kernel for a specified problem still needs further investigation.

Results: Here we propose a novel kernel (Hadamard Kernel) in conjunction with Support Vector Machines (SVMs) to address the problem of breast cancer outcome prediction using gene expression data. Hadamard Kernel outperform the classical kernels and correlation kernel in terms of Area under the ROC Curve (AUC) values where a number of real-world data sets are adopted to test the performance of different methods.

Conclusions: Hadamard Kernel SVM is effective for breast cancer predictions, either in terms of prognosis or diagnosis. It may benefit patients by guiding therapeutic options. Apart from that, it would be a valuable addition to the current SVM kernel families. We hope it will contribute to the wider biology and related communities.

Keywords: Breast Cancer; Hadamard Kernel; SVM.

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Figures

Fig. 1
Fig. 1
Normalization Effect on Hadamard Kernel: GSE1872
Fig. 2
Fig. 2
Normalization Effect on Hadamard Kernel: GSE32394
Fig. 3
Fig. 3
Normalization Effect on Hadamard Kernel: GSE59246
Fig. 4
Fig. 4
Normalization Effect on Hadamard Kernel: GSE59993
Fig. 5
Fig. 5
Normalization Effect on Hadamard Kernel: GSE25055
Fig. 6
Fig. 6
Normalization Effect on Hadamard Kernel: GSE1379
See this image and copyright information in PMC

References

    1. DeSantis C, Siegel R, Bandi P, Jemal A. Breast cancer statistics. CA Cancer J Clin. 2011;61:408–18. doi: 10.3322/caac.20134. - DOI - PubMed
    1. Society AC. Cancer Facts & Figures. Atlanta: ACS; 2016.
    1. Dudoit S, Fridlyand J, Speed TP. Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data. J Am Stat Assoc. 2002;97(457):77–87. doi: 10.1198/016214502753479248. - DOI
    1. Cox DR. A Gene-Expression Signature as a Predictor of Survival in Breast Cancer. N Engl J Med. 2002;347(25):1999–2009. doi: 10.1056/NEJMoa021967. - DOI - PubMed
    1. Lj V’V, Dai H, Mj VDV, et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature. 2002;415(6871):530–6. doi: 10.1038/415530a. - DOI - PubMed

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