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Cancer Classification Using Gene Expression Profiling: Application of the Filter Approach with the Clustering Algorithm

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Proceedings of the Ninth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2017) (SoCPaR 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 737))

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In this paper, we investigate the classification accuracy of different cancers based on microarray expression values. For this purpose, we have used hybridization between a filter selection method and a clustering method to select relevant features in each cancer dataset. Our work is carried out in two steps. First, we examine the effect of the filter selection methods on the classification accuracy before clustering. The studied filter selection methods are SNR, ReliefF, Correlation Coefficient and Mutual Information. The K Nearest Neighbor, Support Vector Machine and Linear Discriminant Analyses classifier were used for supervised classification task.

In the second step, the same investigation is carried out, but the feature selection task is preceded by a k-means clustering operation.

Obtained results showed that the best classification accuracies were obtained (for leukemia, colon, prostate, lung and lymphoma cancers datasets) for SNR method. After adding the clustering step to the phase of the feature subset selection, the classification accuracy has been increased for the four selection methods SNR, ReliefF, Correlation Coefficient, and Mutual Information.

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  1. Bouazza, S.H., Hamdi, N., Zeroual, A., Auhmani, K.: Gene-expression-based cancer classification through feature selection with KNN and SVM classifiers. In: 2015 Intelligent Systems and Computer Vision (ISCV) (2015)

    Google Scholar 

  2. Vincent, I., Kwon, K.-R., Lee, S.-H., Moon, K.-S.: Acute lymphoid leukemia classification using two-step neural network classifier, May 2015

    Google Scholar 

  3. Logique floue et algorithmes génétiques pour le pré-traitement de données de biopuces et la sélection de gènes, thèse de doctorat, edmundobonilla huerta (2008)

    Google Scholar 

  4. El Akadi, A.: Contribution to select relevant features in supervised classification: application to the selection of genes for DNA chips and facial characteristics (2012)

    Google Scholar 

  5. Zhang, L., Chen, Y., Abraham, A.: Hybrid flexible neural tree approach for leukemia cancer classification. In: World Congress on Information and Communication Technologies (2011)

    Google Scholar 

  6. Park, C., Cho, S.B.: Evolutionary ensemble classifier for lymphoma and colon cancer classification. In: Conference: Evolutionary Computation (2003).

  7. Singh, D., Febbo, P.G., Ross, K., Jackson, D.G., Manola, J., Ladd, C., Tamayo, P., Renshaw, A.A., D’Amico, A.V., Richie, J.P., Lander, E.S., Loda, M., Kantoff, P.W., Golub, T.R., Sellers, W.R.: Cancer Cell: March 2002, vol. 1, 28 Feb 2002

    Google Scholar 

  8. Gordon, G.J., Jensen, R.V., Hsiao, L.L., Gullans, S.R., Blumenstock, J.E., Ramaswamy, S., Richards, W.G., Sugarbaker, D.J., Bueno, R.: Translation of microarray data into clinically relevant cancer diagnostic tests using gene expression ratios in lung cancer and mesothelioma. Cancer Res. 62, 4963–4967 (2002)

    Google Scholar 

  9. Shipp, M.A., Ross, K.N., Tamayo, P., et al.: Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. Nat. Med. 8(1), 68–74 (2002)

    Article  Google Scholar 

  10. Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. JMLR 3, 1157–1182 (2003)

    MATH  Google Scholar 

  11. Cuperlovic-Cuf, M., Belacel, N., Ouellette, R.J.: Determination of tumour marker genes from gene expression data. DDT 10(6), 429–437 (2005)

    Article  Google Scholar 

  12. Kira, K., Rendell, L.: A practical approach to feature selection, pp. 249–256 (1992)

    Google Scholar 

  13. Robnik-Šikonja, M., Kononenko, I.: Theoretical and empirical analysis of relieff and rrelieff. Mach. Learn. 53(1–2), 23–69 (2003)

    Article  MATH  Google Scholar 

  14. Egghe, L., Leydesdorff, L.: The relation between Pearson’s correlation coefficient r and Salton’s cosine measure. J. Am. Soc. Inf. Sci. Technol. 60, 1027–1036 (2009).

    Article  Google Scholar 

  15. Shannon, E.: A mathematical theory of communication. Bell Syst. Tech. J. 27, 623–654 (1948)

    Article  MathSciNet  MATH  Google Scholar 

  16. MacKay, D.: An example inference task: clustering. Information Theory, Inference and Learning Algorithm, pp. 284–292. Cambridge University Press, Cambridge (2003). Chapter 20. ISBN 0-521-64298-1. MR 2012999

    Google Scholar 

  17. Smola, A.J., Schölkopf, B.: A tutorial on support vector regression. Stat. Comput. 14(3), 199–222 (2004)

    Article  MathSciNet  Google Scholar 

  18. Sergey, Y.: Sensors and biosensors, MEMS technologies and its applications. In: Advances in Sensors: Reviews, vol. 2. Par Sergey Yurish (2014)

    Google Scholar 

  19. Pehlivanlı, A.Ç.: A novel feature selection scheme for high-dimensional data sets: four-staged feature selection. J. Appl. Stat. 43, 1140–1154 (2015)

    Article  MathSciNet  Google Scholar 

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Correspondence to Sara Haddou Bouazza .

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Haddou Bouazza, S., Auhmani, K., Zeroual, A., Hamdi, N. (2018). Cancer Classification Using Gene Expression Profiling: Application of the Filter Approach with the Clustering Algorithm. In: Abraham, A., Haqiq, A., Muda, A., Gandhi, N. (eds) Proceedings of the Ninth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2017). SoCPaR 2017. Advances in Intelligent Systems and Computing, vol 737. Springer, Cham.

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