Abstract
Statistical analysis of high throughput genomic data, such as gene expressions, copy number alterations (CNAs) and single nucleotide polymorphisms (SNPs), has become very popular in cancer studies in recent decades because such analysis can be very helpful to predict whether a patient has a certain cancer or its subtypes. However, due to the high-dimensional nature of the data sets with hundreds of thousands of variables and very small numbers of samples, traditional machine learning approaches, such as Support Vector Machines (SVMs) and Random Forests (RFs), cannot analyze these data efficiently. To overcome this issue, we propose a deep neural network model to predict molecular subtypes of breast cancer using somatic CNAs. Experiments show that our deep model works much better than traditional SVM and RF.
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References
Perou, C.M., et al.: Molecular portraits of human breast tumours. Nature 406, 747–752 (2000)
Beroukhim, R., et al.: Assessing the significance of chromosomal aberrations in cancer: methodology and application to glioma. Proc. Natl. Acad. Sci. 104, 20007–20012 (2007)
Han, L., et al.: The Pan-Cancer analysis of pseudogene expression reveals biologically and clinically relevant tumour subtypes. Nat. Commun. 5, 3963 (2014)
Srivastava, N., et al.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929–1958 (2014)
Russakovsky, O., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115, 211–252 (2015)
Denas, O., Taylor, J.: Deep modeling of gene expression regulation in an erythropoiesis model. In: Representation Learning, ICML Workshop (2013)
Curtis, C., et al.: The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature 486, 346–352 (2012)
Jia, Y., et al.: An open source convolutional architecture for fast feature embedding (2013). http://caffe.berkeleyvision.org
Acknowledgement
This work was supported in part by Canadian Breast Cancer Foundation – Prairies/NWTRegion, Natural Sciences and Engineering Research Council of Canada, Manitoba Research Health Council and University of Manitoba.
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Islam, M.M., Ajwad, R., Chi, C., Domaratzki, M., Wang, Y., Hu, P. (2017). Somatic Copy Number Alteration-Based Prediction of Molecular Subtypes of Breast Cancer Using Deep Learning Model. In: Mouhoub, M., Langlais, P. (eds) Advances in Artificial Intelligence. Canadian AI 2017. Lecture Notes in Computer Science(), vol 10233. Springer, Cham. https://doi.org/10.1007/978-3-319-57351-9_7
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DOI: https://doi.org/10.1007/978-3-319-57351-9_7
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