A SOFTWARE REVIEW: CLUSTERING ALGORITHM SOFTWARE FOR MICROARRAY EXPRESSION DATA

Published 30 June 2020 •  vol 139  • 


Authors:

 

Law Chow Kuan, Artificial Intelligence and Bioinformatics Research Group, Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia, Malaysia
Lee Seet Sun, Artificial Intelligence and Bioinformatics Research Group, Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia, Malaysia
Mohd Saberi Mohamad, Artificial Intelligence and Bioinformatics Research Group, Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia, Malaysia
Safaai Deris, Artificial Intelligence and Bioinformatics Research Group, Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia, Malaysia
Roselina Sallehuddin, Soft Computing Research Group, Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia, Malaysia

Abstract:

 

Clustering techniques have become an apparent need in many bioinformatics applications. It is a useful exploratory technique for the analysis of gene expression data. Many different heuristic clustering algorithms have been proposed in the field of microarray clustering. This paper reviews a total of 8 software used for clustering. Each software is described in detail about how the clustering algorithm is implemented. Besides that, the advantages and limitations of the software are also listed out. Finally, this paper includes a conclusion made based on the software review.

Keywords:

 

Clustering, Software Review, EXCAVATOR, Mfuzz, Genesis, StackPACK, SOTArray and MATLAB Spectral Clustering Package 1.1, CLUSTER 3.0, MCLUSTER

References:

 

[1] C. Fraley and A. E. Raftery. Enhanced Model-Based Clustering, Density Estimation and Discriminant Analysis Software: MCLUST.Journal of Classification 20:263-286 (2003).
[2] Y. Xu, D. Xu, V. Olman, and L. Wang. EXCAVATOR: a computer program for gene expression data analysis, Nuclear Acid Research, 31(19), 5582-5589 (2003).
[3] L. Kumar and M. Futschik. Mfuzz: A software package for soft clustering of microarray data. Bioinformation. 2(1): 5–7 (2007).
[4] Alexander Sturn. Genesis. (2001).
[5] R. A. George. StackPACK. Briefing in Bioinformatics 2 (4): 388-404 (2001).
[6] C. Fraley and A. E. Raftery. (2003). Parallel Spectral Clustering in Distributed Systems. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 33(3): 568-586 (2011).
[7] C. Fraley, A. E. Raftery. MCLUST Version 3: An R Package for Normal Mixture Modeling and Model-Based Clustering. Technical Report No. 504. University of Washington, USA (2007).

Citations:

 

APA:
Kuan, L. C., Sun, L. S., Mohamad, M. S., Deris, S., & Sallehuddin, R (2020). A Software Review: Clustering Algorithm Software for Microarray Expression Data. International Journal of Advanced Science and Technology (IJAST), ISSN: 2005-4238(Print); 2207-6360 (Online), NADIA, 139, 39-54. doi: 10.33832/ijast.2020.139.05.

MLA:
Kuan, Law Chow, et al. “A Software Review: Clustering Algorithm Software for Microarray Expression Data.” International Journal of Advanced Science and Technology, ISSN: 2005-4238(Print); 2207-6360 (Online), NADIA, vol. 139, 2020, pp. 39-54. IJAST, http://article.nadiapub.com/IJAST/Vol139/5.html.

IEEE:
[1] L. C. Kuan, L. S. Sun, M. S. Mohamad, S. Deris, and R. Sallehuddin, "The Educational Games Application Using Smartphone in Learning Mathematics for Elementary School Students." International Journal of Advanced Science and Technology (IJAST), ISSN: 2005-4238(Print); 2207-6360 (Online), NADIA, vol. 139, pp. 39-54, June 2020.