Title: A study on the most common algorithms implemented for cancer gene search and classifications

Authors: Murad M. Al-Rajab; Joan Lu

Addresses: School of Computing and Engineering, University of Huddersfield, Huddersfield, UK ' School of Computing and Engineering, University of Huddersfield, Huddersfield, UK

Abstract: Understanding gene expression is an important factor for cancer diagnosis. One target of this understanding is implementing cancer gene search and classification methods. However, cancer gene search and classification is a challenge in that there is no obvious exact algorithm that can be implemented individually for various cancer cells. In this paper, a research is conducted through the most common top-ranked algorithms implemented for cancer gene search and classification, and on how they are implemented to reach a better performance. The paper will distinguish algorithms implemented for bio-image analysis for cancer cells and algorithms implemented based on DNA microarray data. The paper will also explore the road map towards presenting the most current algorithms implemented for cancer gene search and classification, as well as focusing on the importance of search algorithms and how they are implemented to enhance search and the factors that affect the performance.

Keywords: cancer genes; search algorithms; classification algorithms; performance evaluation; data mining; bioinformatics; cancer gene search; cancer gene classification; gene expression; cancer diagnosis; bio-image analysis; cancer cells; DNA microarray data.

DOI: 10.1504/IJDMB.2016.074685

International Journal of Data Mining and Bioinformatics, 2016 Vol.14 No.2, pp.159 - 176

Received: 12 Feb 2015
Accepted: 04 May 2015
Published online: 13 Feb 2016 *

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