Abstract
Accurate cancer classification and responses to treatment are important in clinical cancer research since cancer acts as a family of gene-based diseases. Microarray technology has widely developed to measure gene expression level changes under normal and experimental conditions. Normally, gene expression data are high dimensional and characterized by small sample sizes. Thus, feature selection is needed to find the smallest number of informative genes and improve the classification accuracy and the biological interpretability results. Due to some feature selection methods neglect the interactions among genes, thus, clustering is used to group the similar genes together. Besides, the quality of the selected data can determine the effectiveness of the classifiers. This research proposed clustering and feature selection approaches to classify the gene expression data of colorectal cancer. Subsequently, a feature selection approach based on centroid clustering provide higher classification accuracy compared with other approaches.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Aliahmadipour, L., Eslami, E.: GHFHC: generalized hesitant fuzzy hierarchical clustering algorithm. Int. J. Intell. Syst. 31, 855–871 (2016)
Alon, U., Barkai, N., Notterman, D.A., Gish, K., Ybarra, S., Mack, D., Levine, A.J.: Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc. Nat. Acad. Sci. 96(12), 6745–6750 (1999)
Arakawa, Y., Shimada, M., Utsunomiya, T., Imura, S., Morine, Y., Ikemoto, T., Mori, H., Kanamoto, M., Iwahashi, S., Saito, Y., Takasu, C.: Gene profile in the spleen under massive partial hepatectomy using complementary DNA microarray and pathway analysis. J. Gastroenterol. Hepatol. 29, 1645–1653 (2014). doi:10.1111/jgh.12573
Bajo, J., De Paz, J.F., RodrÃguez, S., González, A.: A new clustering algorithm applying a hierarchical method neural network. Logic JIGPL 19, 304–314 (2010)
Bolón-Canedo, V., Sánchez-Maroño, N., Alonso-Betanzos, A., BenÃtez, J.M., Herrera, F.: A review of microarray datasets and applied feature selection methods. Inf. Sci. 282, 111–135 (2014). doi:10.1016/j.ins.2014.05.042
Campo, L., Aliaga, I.J., De Paz, J.F., GarcÃa, A.E., Bajo, J., Villarubia, G., Corchado, J.M.: Retreatment predictions in odontology by means of CBR systems. Comput. Intell. Neurosci. 2016, 39 (2016)
Chan, W.H., Mohamad, M.S., Deris, S., Corchado, J.M., Omatu, S., Ibrahim, Z., Kasim, S.: An improved gSVM-SCADL2 with firefly algorithm for identification of informative genes and pathways. Int. J. Bioinf. Res. Appl. 12(1), 72–93 (2016)
Chen, T.S., Tsai, T.H., Chen, Y.T., Lin, C.C., Chen, R.C., Li, S.Y., Chen, H.Y.: A combined K-means and hierarchical clustering method for improving the clustering efficiency of microarray. In: Proceedings of 2005 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2005, pp. 405–408. IEEE, December 2005
Davidson, I., Ravi, S.S.: Agglomerative hierarchical clustering with constraints: theoretical and empirical results. In: European Conference on Principles of Data Mining and Knowledge Discovery, pp. 59–70. Springer, Heidelberg, October 2005
De Paz, J.F., Bajo, J., López, V.F., Corchado, J.M.: Biomedic organizations: an intelligent dynamic architecture for KDD. Inf. Sci. 224, 49–61 (2013)
Garzón, J.A.C., González, J.R.: A gene selection approach based on clustering for classification tasks in colon cancer. ADCAIJ Adv. Distrib. Comput. Artif. Intell. J. 4(3), 1–10 (2015)
Ghalwash, M.F., Cao, X.H., Stojkovic, I., Obradovic, Z.: Structured feature selection using coordinate descent optimization. BMC Bioinf. 17(1), 158 (2016)
Guyon, I., Weston, J., Barnhill, S., Vapnik, V.: Gene selection for cancer classification using support vector machines. Mach. Learn. 46, 389–422 (2002). doi:10.1023/A:1012487302797
Hall, M.A.: Correlation-based feature selection for machine learning (Doctoral dissertation, The University of Waikato) (1999)
Hancer, E., Karaboga, D.: A comprehensive survey of traditional, merge-split and evolutionary approaches proposed for determination of cluster number. Swarm Evol. Comput. 32, 49–67 (2016)
Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. (CSUR) 31(3), 264–323 (1999)
Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis, vol. 344. John Wiley & Sons, Hoboken, NJ, USA (1990)
Kavya, D.S., Desai, C.D.: Comparative Analysis of K means clustering sequentially and parallely. Int. Res. J. Eng. Technol. 3(4), 2311–2315 (2016)
Kelly, D.L., Rizzino, A.: DNA microarray analyses of genes regulated during the differentiation of embryonic stem cells. Mol. Reprod. Dev. 56, 113–123 (2000)
Khanmohammadi, S., Adibeig, N., Shanehbandy, S.: An improved overlapping k-means clustering method for medical applications. Expert Syst. Appl. 67, 12–18 (2017)
Kothandan, R., Biswas, S.: Identifying microRNAs involved in cancer pathway using support vector machines. Comput. Biol. Chem. 55, 31–36 (2015)
Maroco, J., Silva, D., Rodrigues, A., Guerreiro, M., Santana, I., de Mendonça, A.: Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests. BMC Res. Notes 4(1), 299 (2011)
Mohamad, M., Omatu, S., Deris, S., Misman, M., Yoshioka, M.: Selecting informative genes from microarray data by using hybrid methods for cancer classification. Artif. Life Robot. 13, 414–417 (2009). doi:10.1007/s10015-008-0534-4
Moorthy, K., Mohamad, M.S.: Random forest for gene selection and microarray data classification. Bioinformation 7, 142–146 (2011). doi:10.6026/97320630007142
Önskog, J., Freyhult, E., Landfors, M., Rydén, P., Hvidsten, T.R.: Classification of microarrays; synergistic effects between normalization, gene selection and machine learning. BMC Bioinf. 12, 390 (2011). doi:10.1186/1471-2105-12-390
Roffo, G., Melzi, S., Cristani, M.: Infinite feature selection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4202–4210 (2015)
Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987)
Seetha, H., Murty, M.N., Saravanan, R.: Classification by majority voting in feature partitions. Int. J. Inf. Decis. Sci. 8(2), 109–124 (2016)
Tarek, S., Elwahab, R.A., Shoman, M.: Cancer classification ensemble system based on gene expression profiles. In: 2016 5th International Conference on Electronic Devices, Systems and Applications (2016)
Vattani, A.: k-means requires exponentially many iterations even in the plane. Discrete Comput. Geom. 45(4), 596–616 (2011)
Wang, Y., Tetko, I.V., Hall, M.A., Frank, E., Facius, A., Mayer, K.F., Mewes, H.W.: Gene selection from microarray data for cancer classification—a machine learning approach. Comput. Biol. Chem. 29(1), 37–46 (2005)
Zaki, N.M., Deris, S., Illias, R.: Application of string kernels in protein sequence classification. Appl. Bioinf. 4(1), 45–52 (2005)
Zheng, B., Yoon, S.W., Lam, S.S.: Breast cancer diagnosis based on feature extraction using a hybrid of K-means and support vector machine algorithms. Expert Syst. Appl. 41(4), 1476–1482 (2014)
Acknowledgements
We would like to thank Universiti Teknologi Malaysia for funding this research through GUP Research Grants (grant numbers: Q.J130000.2528.12H12 and Q.J130000.2528.11H05). This research is also funded by Malaysian Ministry of Higher Education under a fundamental research grant (grant number: 1559).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Nies, H.W. et al. (2017). Classification of Colorectal Cancer Using Clustering and Feature Selection Approaches. In: Fdez-Riverola, F., Mohamad, M., Rocha, M., De Paz, J., Pinto, T. (eds) 11th International Conference on Practical Applications of Computational Biology & Bioinformatics. PACBB 2017. Advances in Intelligent Systems and Computing, vol 616. Springer, Cham. https://doi.org/10.1007/978-3-319-60816-7_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-60816-7_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-60815-0
Online ISBN: 978-3-319-60816-7
eBook Packages: EngineeringEngineering (R0)