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A Comparative Study of Cancer Classification Methods Using Microarray Gene Expression Profile

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Proceedings of the First International Conference on Advanced Data and Information Engineering (DaEng-2013)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 285))

Abstract

Microarray based gene expression profiling has been emerged as an efficient technique for cancer classification, as well as for diagnosis, prognosis, and treatment purposes. The primary task of microarray data classification is to determine a computational model from the given microarray data that can determine the class of unknown samples. In recent times, microarray technique has gained more attraction in both scientific and in industrial fields. It is important to determine the informative genes that cause the cancer to improve early cancer diagnosis and to give effective chemotherapy treatment. Classifying cancer microarray gene expression data is a challenging task because microarray is a high dimensional-low sample dataset with lots of noisy or irrelevant genes and missing data. Therefore, finding an accurate and an effective cancer classification approach is very significant issue in medical domain. In this paper, we will make a comparative study and we will categorize the effective binary classification approaches that have been applied for cancer microarray gene expression profile. Then we conclude by identifying the most accurate classification method that has the highest classification accuracy along with the smallest number of effective genes.

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Authors and Affiliations

  1. King Saud University, College of Computer and Information Sciences, Riyadh, Kingdom of Saudi Arabia

    Hala Alshamlan, Ghada Badr & Yousef Alohali

Authors
  1. Hala Alshamlan
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  2. Ghada Badr
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  3. Yousef Alohali
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Corresponding author

Correspondence to Hala Alshamlan .

Editor information

Editors and Affiliations

  1. Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia

    Tutut Herawan

  2. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Malaysia

    Mustafa Mat Deris

  3. School of Information Technology, Deakin University, Burwood, Victoria, Australia

    Jemal Abawajy

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Alshamlan, H., Badr, G., Alohali, Y. (2014). A Comparative Study of Cancer Classification Methods Using Microarray Gene Expression Profile. In: Herawan, T., Deris, M., Abawajy, J. (eds) Proceedings of the First International Conference on Advanced Data and Information Engineering (DaEng-2013). Lecture Notes in Electrical Engineering, vol 285. Springer, Singapore. https://doi.org/10.1007/978-981-4585-18-7_44

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  • DOI: https://doi.org/10.1007/978-981-4585-18-7_44

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-4585-17-0

  • Online ISBN: 978-981-4585-18-7

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