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A review of computational methods for clustering genes with similar biological functions

Hui, Wen Nies and Zakaria, Zalmiyah and Mohamad, Mohd. Saberi and Weng, Howe Chan and Zaki, Nazar and Sinnott, Richard O. and Napis, Suhaimi and Chamoso, Pablo and Omatu, Sigeru and Corchado, Juan Manuel (2019) A review of computational methods for clustering genes with similar biological functions. Processes, 7 (9). p. 550. ISSN 2227-9717


Official URL: http://dx.doi.org/10.3390/pr7090550


Clustering techniques can group genes based on similarity in biological functions. However, the drawback of using clustering techniques is the inability to identify an optimal number of potential clusters beforehand. Several existing optimization techniques can address the issue. Besides, clustering validation can predict the possible number of potential clusters and hence increase the chances of identifying biologically informative genes. This paper reviews and provides examples of existing methods for clustering genes, optimization of the objective function, and clustering validation. Clustering techniques can be categorized into partitioning, hierarchical, grid-based, and density-based techniques. We also highlight the advantages and the disadvantages of each category. To optimize the objective function, here we introduce the swarm intelligence technique and compare the performances of other methods. Moreover, we discuss the differences of measurements between internal and external criteria to validate a cluster quality. We also investigate the performance of several clustering techniques by applying them on a leukemia dataset. The results show that grid-based clustering techniques provide better classification accuracy; however, partitioning clustering techniques are superior in identifying prognostic markers of leukemia. Therefore, this review suggests combining clustering techniques such as CLIQUE and k-means to yield high-quality gene clusters.

Item Type:Article
Uncontrolled Keywords:Gene clustering, Informative genes
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
ID Code:89030
Deposited By: Widya Wahid
Deposited On:26 Jan 2021 16:41
Last Modified:26 Jan 2021 16:41

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