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

• Main Authors: Hui, Wen Nies, Zakaria, Zalmiyah, Mohamad, Mohd. Saberi, Weng, Howe Chan, Zaki, Nazar, Sinnott, Richard O., Napis, Suhaimi, Chamoso, Pablo, Omatu, Sigeru, Corchado, Juan Manuel
• Format: Article
• Language: English
• Published: MDPI AG 2019
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://eprints.utm.my/id/eprint/89030/1/ZalmiyahZakaria2019_AReviewofComputationalMethodsforClusteringGenes.pdf; http://eprints.utm.my/id/eprint/89030/; http://dx.doi.org/10.3390/pr7090550
• Institution: Universiti Teknologi Malaysia

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.