Abstract
Support vector machines (SVM) and deep convolutional neural networks (DCNNs) are state-of-the-art classification techniques in many real-world applications. Our investigation aims at proposing a hybrid model combining DCNNs and SVM (called DCNN-SVM) to effectively predict very-high-dimensional gene expression data. The DCNN-SVM trains the DCNNs model to automatically extract features from microarray gene expression data and followed which the DCNN-SVM learns a non-linear SVM model to classify gene expression data. Numerical test results on 15 microarray datasets from Array Expression and Medical Database (Kent Ridge) show that our proposed DCNN-SVM is more accurate than the classical DCNNs algorithm, SVM, random forests.
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References
Brazma, A., et al.: ArrayExpress a public repository for microarray gene expression data at the EBI. Nucleic Acids Res. 31(1), 68–71 (2003)
Edgar, R., Domrachev, M., Lash, A.E.: Gene expression omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 30(1), 207–210 (2002)
Schena, M., et al.: Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science (New York then Washington) 467–470 (1995)
Pinkel, D., et al.: High resolution analysis of DNA copy number variation using comparative genomic hybridization to microarrays. Nat. Genet. 20(2) (1998)
Brown, M.P.S., et al.: Support vector machine classification of microarray gene expression data. University of California, Santa Cruz, Technical Report UCSC-CRL-99-09 (1999)
Furey, T.S., Cristianini, N., Duffy, N., Bednarski, D.W., Schummer, M., Haussler, D.: Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 16(10), 906–914 (2000)
Guyon, I., Weston, J., Barnhill, S., Vapnik, V.: Gene selection for cancer classification using support vector machines. Mach. Learn. 46(1), 389–422 (2002)
Hasri, N.N.M., et al.: Improved support vector machine using multiple SVM-RFE for cancer classification. Int. J. Adv. Sci. Eng. Inf. Technol. 7(4–2), 1589–1594 (2017)
Yeang, C.H., Ramaswamy, S., Tamayo, P., Mukherjee, S., Rifkin, R.M., Angelo, M., Reich, M., Lander, E., Mesirov, J., Golub, T.: Molecular classification of multiple tumor types. Bioinformatics 17(suppl-1), S316–S322 (2001)
Li, J., Liu, H.: Ensembles of cascading trees. In: 2003 Third IEEE International Conference on Data Mining, ICDM 2003, pp. 585–588. IEEE (2003)
Li, J., Liu, H., Ng, S.K., Wong, L.: Discovery of significant rules for classifying cancer diagnosis data. Bioinformatics 19(suppl-2), ii93–ii102 (2003)
Tsai, M.H., et al.: A decision tree based classifier to analyze human ovarian cancer cDNA microarray datasets. J. Med. Syst. 40(1), 21 (2016)
DÃaz-Uriarte, R., De Andres, S.A.: Gene selection and classification of microarray data using random forest. BMC Bioinf. 7(1), 3 (2006)
Do, T.N., Lenca, P., Lallich, S., Pham, N.K.: Classifying very-high-dimensional data with random forests of oblique decision trees. In: Advances in Knowledge Discovery and Management, pp. 39–55. Springer (2010)
Tan, A.C., Gilbert, D.: Ensemble machine learning on gene expression data for cancer classification. Bioinformatics (2003)
Dettling, M.: Bagboosting for tumor classification with gene expression data. Bioinformatics 20(18), 3583–3593 (2004)
Krizhevsky, A., et al.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Lai, S., Xu, L., Liu, K., Zhao, J.: Recurrent convolutional neural networks for text classification. AAAI 333, 2267–2273 (2015)
Min, S., Lee, B., Yoon, S.: Deep learning in bioinformatics. Brief. Bioinf. (2016). https://doi.org/10.1093/bib/bbw068
Suykens, J.A., Vandewalle, J.: Training multilayer perceptron classifiers based on a modified support vector method. IEEE Trans. Neural Netw. 10(4), 907–911 (1999)
Bellili, A., Gilloux, M., Gallinari, P.: An hybrid MLP-SVM handwritten digit recognizer. In: Proceedings of the Sixth International Conference on Document Analysis and Recognition 2001, pp. 28–32. IEEE (2001)
Niu, X.X., Suen, C.Y.: A novel hybrid CNN-SVM classifier for recognizing handwritten digits. Pattern Recognit. 45(4), 1318–1325 (2012)
Nagi, J., et al.: Convolutional neural support vector machines: hybrid visual pattern classifiers for multi-robot systems. In: 2012 11th International Conference on Machine Learning and Applications (ICMLA), vol. 1, pp. 27–32. IEEE (2012)
Cao, G., Wang, S., Wei, B., Yin, Y., Yang, G.: A hybrid CNN-RF method for electron microscopy images segmentation. Tissue Eng. J. Biomim. Biomater. Tissue Eng. 18, 2 (2013)
**yan, L., Huiqing, L.: Kent ridge bio-medical data set repository (2002)
Vapnik, V.: Statistical Learning Theory, vol. 1. Wiley, New York (1998)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Hubel, D., Wiesel, T.: Shape and arrangement of columns in cat’s striate cortex. J. Physiol. 165(3), 559–568 (1963)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)
Burges, C.J.: A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Discov. 2(2), 121–167 (1998)
Kreßel, U.H.G.: Pairwise classification and support vector machines. In: Advances in Kernel Methods, pp. 255–268. MIT press (1999)
Cristianini, N., Shawe Taylor, J.: An introduction to support vector machines and other kernel-based learning methods. Cambridge university press (2000)
Huang, F., LeCun, Y.: Large-scale learning with SVM and convolutional nets for generic object recognition. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2006)
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 27:1–27:27 (2011). Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
Abadi, M., et al.: TensorFlow: large-scale machine learning on heterogeneous systems (2015). Software available from http://www.tensorflow.org
Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Gordon, G.J., et al.: Translation of microarray data into clinically relevant cancer diagnostic tests using gene expression ratios in lung cancer and mesothelioma. Cancer Res. 62(17), 4963–4967 (2002)
Singh, D., et al.: Gene expression correlates of clinical prostate cancer behavior. Cancer Cell 1(2), 203–209 (2002)
Veer, V., et al.: Gene expression profiling predicts clinical outcome of breast cancer. Nature 415(6871), 530–536 (2002)
Bhattacharjee, A., et al.: Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses. Proc. Natl. Acad. Sci. 98(24), 13790–13795 (2001)
Subramanian, A., et al.: Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. U. S. A. 102(43), 15545–15550 (2005)
Wong, T.T.: Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation. Pattern Recognit. 48(9), 2839–2846 (2015)
Diederik, P., Kingma, J.B.: Adam: a method for stochastic optimization. In: Proceedings of the 3rd International Conference on Learning Representations (ICLR) (2014)
Hsu, C.W., et al.: A practical guide to support vector classification (2003)
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Huynh, PH., Nguyen, VH., Do, TN. (2018). A Coupling Support Vector Machines with the Feature Learning of Deep Convolutional Neural Networks for Classifying Microarray Gene Expression Data. In: Sieminski, A., Kozierkiewicz, A., Nunez, M., Ha, Q. (eds) Modern Approaches for Intelligent Information and Database Systems. Studies in Computational Intelligence, vol 769. Springer, Cham. https://doi.org/10.1007/978-3-319-76081-0_20
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