An Improved Pearson’s Correlation Proximity-Based Hierarchical Clustering for Mining Biological Association between Genes
Joint Authors
Booma, P. M.
Prabhakaran, S.
Dhanalakshmi, R.
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-06-16
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
Microarray gene expression datasets has concerned great awareness among molecular biologist, statisticians, and computer scientists.
Data mining that extracts the hidden and usual information from datasets fails to identify the most significant biological associations between genes.
A search made with heuristic for standard biological process measures only the gene expression level, threshold, and response time.
Heuristic search identifies and mines the best biological solution, but the association process was not efficiently addressed.
To monitor higher rate of expression levels between genes, a hierarchical clustering model was proposed, where the biological association between genes is measured simultaneously using proximity measure of improved Pearson's correlation (PCPHC).
Additionally, the Seed Augment algorithm adopts average linkage methods on rows and columns in order to expand a seed PCPHC model into a maximal global PCPHC (GL-PCPHC) model and to identify association between the clusters.
Moreover, a GL-PCPHC applies pattern growing method to mine the PCPHC patterns.
Compared to existing gene expression analysis, the PCPHC model achieves better performance.
Experimental evaluations are conducted for GL-PCPHC model with standard benchmark gene expression datasets extracted from UCI repository and GenBank database in terms of execution time, size of pattern, significance level, biological association efficiency, and pattern quality.
American Psychological Association (APA)
Booma, P. M.& Prabhakaran, S.& Dhanalakshmi, R.. 2014. An Improved Pearson’s Correlation Proximity-Based Hierarchical Clustering for Mining Biological Association between Genes. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-10.
https://search.emarefa.net/detail/BIM-1049316
Modern Language Association (MLA)
Booma, P. M.…[et al.]. An Improved Pearson’s Correlation Proximity-Based Hierarchical Clustering for Mining Biological Association between Genes. The Scientific World Journal No. 2014 (2014), pp.1-10.
https://search.emarefa.net/detail/BIM-1049316
American Medical Association (AMA)
Booma, P. M.& Prabhakaran, S.& Dhanalakshmi, R.. An Improved Pearson’s Correlation Proximity-Based Hierarchical Clustering for Mining Biological Association between Genes. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-10.
https://search.emarefa.net/detail/BIM-1049316
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1049316