Classification of colorectal cancer using clustering and feature selection approaches

Hui Wen Nies; Kauthar Mohd Daud; Muhammad Akmal Remli; Mohd Saberi Mohamad; Safaai Deris; Sigeru Omatu; Shahreen Kasim; Ghazali Sulong

doi:10.1007/978-3-319-60816-7_8

Classification of colorectal cancer using clustering and feature selection approaches

Hui Wen Nies
, Kauthar Mohd Daud
, Muhammad Akmal Remli
, Mohd Saberi Mohamad
, Safaai Deris
, Sigeru Omatu
, Shahreen Kasim
, Ghazali Sulong

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Citations (Scopus)

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.

Original language	English
Title of host publication	11th International Conference on Practical Applications of Computational Biology and Bioinformatics, 2017
Editors	Miguel Rocha, Juan F. De Paz, Tiago Pinto, Florentino Fdez-Riverola, Mohd Saberi Mohamad
Publisher	Springer Verlag
Pages	58-65
Number of pages	8
ISBN (Print)	9783319608150
DOIs	https://doi.org/10.1007/978-3-319-60816-7_8
Publication status	Published - 2017
Externally published	Yes
Event	11th International Conference on Practical Applications of Computational Biology and Bioinformatics, PACBB 2017 - Porto, Portugal Duration: Jun 21 2017 → Jun 23 2017

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	616
ISSN (Print)	2194-5357

Conference

Conference	11th International Conference on Practical Applications of Computational Biology and Bioinformatics, PACBB 2017
Country/Territory	Portugal
City	Porto
Period	6/21/17 → 6/23/17

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Artificial intelligence
Bioinformatics
Cancer classification
Clustering
Feature selection
Gene expression data

ASJC Scopus subject areas

Control and Systems Engineering
General Computer Science

Access to Document

10.1007/978-3-319-60816-7_8

Cite this

Nies, H. W., Daud, K. M., Remli, M. A., Mohamad, M. S., Deris, S., Omatu, S., Kasim, S., & Sulong, G. (2017). Classification of colorectal cancer using clustering and feature selection approaches. In M. Rocha, J. F. De Paz, T. Pinto, F. Fdez-Riverola, & M. S. Mohamad (Eds.), 11th International Conference on Practical Applications of Computational Biology and Bioinformatics, 2017 (pp. 58-65). (Advances in Intelligent Systems and Computing; Vol. 616). Springer Verlag. https://doi.org/10.1007/978-3-319-60816-7_8

Classification of colorectal cancer using clustering and feature selection approaches. / Nies, Hui Wen; Daud, Kauthar Mohd; Remli, Muhammad Akmal et al.
11th International Conference on Practical Applications of Computational Biology and Bioinformatics, 2017. ed. / Miguel Rocha; Juan F. De Paz; Tiago Pinto; Florentino Fdez-Riverola; Mohd Saberi Mohamad. Springer Verlag, 2017. p. 58-65 (Advances in Intelligent Systems and Computing; Vol. 616).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Nies, HW, Daud, KM, Remli, MA, Mohamad, MS, Deris, S, Omatu, S, Kasim, S & Sulong, G 2017, Classification of colorectal cancer using clustering and feature selection approaches. in M Rocha, JF De Paz, T Pinto, F Fdez-Riverola & MS Mohamad (eds), 11th International Conference on Practical Applications of Computational Biology and Bioinformatics, 2017. Advances in Intelligent Systems and Computing, vol. 616, Springer Verlag, pp. 58-65, 11th International Conference on Practical Applications of Computational Biology and Bioinformatics, PACBB 2017, Porto, Portugal, 6/21/17. https://doi.org/10.1007/978-3-319-60816-7_8

Nies HW, Daud KM, Remli MA, Mohamad MS, Deris S, Omatu S et al. Classification of colorectal cancer using clustering and feature selection approaches. In Rocha M, De Paz JF, Pinto T, Fdez-Riverola F, Mohamad MS, editors, 11th International Conference on Practical Applications of Computational Biology and Bioinformatics, 2017. Springer Verlag. 2017. p. 58-65. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-319-60816-7_8

Nies, Hui Wen ; Daud, Kauthar Mohd ; Remli, Muhammad Akmal et al. / Classification of colorectal cancer using clustering and feature selection approaches. 11th International Conference on Practical Applications of Computational Biology and Bioinformatics, 2017. editor / Miguel Rocha ; Juan F. De Paz ; Tiago Pinto ; Florentino Fdez-Riverola ; Mohd Saberi Mohamad. Springer Verlag, 2017. pp. 58-65 (Advances in Intelligent Systems and Computing).

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AU - Remli, Muhammad Akmal

AU - Mohamad, Mohd Saberi

AU - Deris, Safaai

AU - Omatu, Sigeru

AU - Kasim, Shahreen

AU - Sulong, Ghazali

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AB - 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.

KW - Artificial intelligence

KW - Bioinformatics

KW - Cancer classification

KW - Clustering

KW - Feature selection

KW - Gene expression data

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ER -

Classification of colorectal cancer using clustering and feature selection approaches

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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