A review of computational methods for clustering genes with similar biological functions
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...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI
2019
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| Online Access: | http://psasir.upm.edu.my/id/eprint/38286/ http://psasir.upm.edu.my/id/eprint/38286/1/38286.pdf |
| _version_ | 1848848837296259072 |
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| author | Nies, Hui Wen Zakaria, Zalmiyah Mohamad, Mohd Saberi Chan, Weng Howe Zaki, Nazar Sinnott, Richard O. Napis, Suhaimi Chamoso, Pablo Omatu, Sigeru Corchado, Juan Manuel |
| author_facet | Nies, Hui Wen Zakaria, Zalmiyah Mohamad, Mohd Saberi Chan, Weng Howe Zaki, Nazar Sinnott, Richard O. Napis, Suhaimi Chamoso, Pablo Omatu, Sigeru Corchado, Juan Manuel |
| author_sort | Nies, Hui Wen |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T09:40:50Z |
| format | Article |
| id | upm-38286 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T09:40:50Z |
| publishDate | 2019 |
| publisher | MDPI |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-382862020-05-04T16:12:08Z http://psasir.upm.edu.my/id/eprint/38286/ A review of computational methods for clustering genes with similar biological functions Nies, Hui Wen Zakaria, Zalmiyah Mohamad, Mohd Saberi Chan, Weng Howe Zaki, Nazar Sinnott, Richard O. Napis, Suhaimi Chamoso, Pablo Omatu, Sigeru Corchado, Juan Manuel 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. MDPI 2019 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/38286/1/38286.pdf Nies, Hui Wen and Zakaria, Zalmiyah and Mohamad, Mohd Saberi and Chan, Weng Howe 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). art. no. 550. pp. 1-18. ISSN 2227-9717 https://www.mdpi.com/2227-9717/7/9/550 10.3390/pr7090550 |
| spellingShingle | Nies, Hui Wen Zakaria, Zalmiyah Mohamad, Mohd Saberi Chan, Weng Howe Zaki, Nazar Sinnott, Richard O. Napis, Suhaimi Chamoso, Pablo Omatu, Sigeru Corchado, Juan Manuel A review of computational methods for clustering genes with similar biological functions |
| title | A review of computational methods for clustering genes with similar biological functions |
| title_full | A review of computational methods for clustering genes with similar biological functions |
| title_fullStr | A review of computational methods for clustering genes with similar biological functions |
| title_full_unstemmed | A review of computational methods for clustering genes with similar biological functions |
| title_short | A review of computational methods for clustering genes with similar biological functions |
| title_sort | review of computational methods for clustering genes with similar biological functions |
| url | http://psasir.upm.edu.my/id/eprint/38286/ http://psasir.upm.edu.my/id/eprint/38286/ http://psasir.upm.edu.my/id/eprint/38286/ http://psasir.upm.edu.my/id/eprint/38286/1/38286.pdf |