ACM Home Page
Please provide us with feedback. Feedback
A clustering algorithm using particle swarm optimization for DNA chip data analysis
Full text PdfPdf (770 KB)
Source Conference On Ubiquitous Information Management And Communication archive
Proceedings of the 3rd International Conference on Ubiquitous Information Management and Communication table of contents
Suwon, Korea
SESSION: Data analysis and mining II table of contents
Pages 664-668  
Year of Publication: 2009
ISBN:978-1-60558-405-8
Authors
Minsoo Lee  Ewha Womans University
Yoonkyoung Lee  Ewha Womans University
Boyeon Meang  Ewha Womans University
Okju Choi  Ewha Womans University
Sponsor
SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 25,   Downloads (12 Months): 87,   Citation Count: 0
Additional Information:

abstract   references   index terms   collaborative colleagues  

Tools and Actions: Request Permissions Request Permissions    Review this Article  
DOI Bookmark: Use this link to bookmark this Article: http://doi.acm.org/10.1145/1516241.1516358
What is a DOI?

ABSTRACT

As DNA chips are becoming increasingly popular as a convenient way to perform vast amounts of experiments related to genes on a single chip, the importance of analyzing the data that is provided by such DNA chips is becoming significant. A very important analysis on DNA chip data would be clustering genes to identify gene groups which have similar properties. Clustering data for DNA chips usually deal with a large search space and has a very fuzzy characteristic. This task can be achieved by applying a clustering technique that mimics the biological world. One such algorithm is the Particle Swarm Optimization algorithm which was recently proposed as a very good candidate to solve such problems. In this paper, we propose a clustering mechanism that is based on the Particle Swarm Optimization algorithm. Our experiments show that the PSO-based clustering algorithm developed can efficiently cluster DNA chip data, and thus be used to extract valuable information from DNA chip data in an accurate yet timely manner.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
Lockhart, D. J., Dong, H. L., Byrne, M. C., Follettie, M. T., Gallo, M. V., Chee, M. S., Mittmann, M., Wang, C. W., Kobayashi, M., Horton, H., Brown, E. L. 1996. Expression monitoring by hybridization to high-density oligonucleotide arrays. Nature Biotechnology. 14(13):1675--1680.
 
2
DeRisi, J. L., Iver, V. R., Brown, P. O. 1997. Exploring the metabolic and genetic control of gene expression on a genomic scale. Science.278(5338):680--686.
 
3
Debouck, C., Goodfellow, P. N. 1999. DNA microarrays in drug discovery and development. Nature Genetics. 21(1 suppl):48--50.
 
4
Bowtell, D., Sambrook, J. 2002. DNA Microarrays. CSHL Press.
 
5
PSO, Particle Swarm Optimization Homepage. http://www.cis.syr.edu/~mohan/pso/.
 
6
 
7
 
8
 
9
Eisen, M. B., Spellman, P. T., Browndagger, P. O., and Botstein, D. 1998. Cluster analysis and display of genome-wide expression patterns, Proceedings of the National Academy of Sciences of the United States of America (PNAS), 95:25.
 
10
Yuqing, P., Xiangdan, H., Shang, L. 2003. The K-means Clustering Algorithm based on Density and Ant colony, IEEE Int. Conf. Neural Networks & Signal Processing Nanjing, China, December 14--17.
 
11
Xiao, X., Dow, E. R., Eberhart, R., Miled, Z. B., Oppelt, R. J. 2003. Gene Clustering Using Self-Organizing Maps and Particle Swarm Optimization. IEEE International Workshop On High Performance Computational Biology.
 
12
Debouck, C., Goodfellow, P. N. 1999. DNA microarrays in drug discovery and development. Nature Genetics. 21(1 suppl):48--50.
 
13
DNA chip. http://mbel.kaist.ac.kr/research/DNAchip_en.html.
 
14
WIKIPEDIA. http://en.wikipedia.org/wiki/Genetic_algorithm.
 
15

Collaborative Colleagues:
Minsoo Lee: colleagues
Yoonkyoung Lee: colleagues
Boyeon Meang: colleagues
Okju Choi: colleagues