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Clustering Algorithms: Their Application to Gene Expression Data

Oyelade, O. J. and Isewon, Itunuoluwa and Oladipupo, O. O. and Aromolaran, Olufemi and Uwoghiren, Efosa and Ameh, Faridah and Achas, Moses and Adebiyi, Ezekiel (2016) Clustering Algorithms: Their Application to Gene Expression Data. Libertas Academica Freedom to Research, 10. pp. 237-268.

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Gene expression data hide vital information required to understand the biological process that takes place in a particular organism in relation to its environment. Deciphering the hidden patterns in gene expression data proffers a prodigious preference to strengthen the understanding of functional genomics. The complexity of biological networks and the volume of genes present increase the challenges of comprehending and interpretation of the resulting mass of data, which consists of millions of measurements; these data also inhibit vagueness, imprecision, and noise. Therefore, the use of clustering techniques is a first step toward addressing these challenges, which is essential in the data mining process to reveal natural structures and iden-tify interesting patterns in the underlying data. The clustering of gene expression data has been proven to be useful in making known the natural structure inherent in gene expression data, understanding gene functions, cellular processes, and subtypes of cells, mining useful information from noisy data, and understanding gene regulation. The other benefit of clustering gene expression data is the identification of homology, which is very important in vaccine design. This review examines the various clustering algorithms applicable to the gene expression data in order to discover and provide useful knowledge of the appropriate clustering technique that will guarantee stability and high degree of accuracy in its analysis procedure.

Item Type: Article
Uncontrolled Keywords: clustering algorithm, homology, biological process, gene expression data, bioinformatics
Subjects: Q Science > Q Science (General)
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science
Depositing User: Mrs Patricia Nwokealisi
Date Deposited: 16 May 2017 11:23
Last Modified: 15 Jun 2017 17:42

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