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

Hui Wen Nies, Zalmiyah Zakaria, Mohd Saberi Mohamad, Weng Howe Chan, Nazar Zaki, Richard O. Sinnott, Suhaimi Napis, Pablo Chamoso, Sigeru Omatu, Juan Manuel Corchado

Research output: Contribution to journalReview articlepeer-review

6 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number550
JournalProcesses
Volume7
Issue number9
DOIs
Publication statusPublished - Sep 1 2019

Keywords

  • Biological functions detection
  • Gene clustering
  • Informative genes
  • Swarm intelligence

ASJC Scopus subject areas

  • Bioengineering
  • Chemical Engineering (miscellaneous)
  • Process Chemistry and Technology

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