An Improved Pearson’s Correlation Proximity-Based Hierarchical Clustering for Mining Biological Association between Genes
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 wi...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/357873 |
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| author | P. M. Booma S. Prabhakaran R. Dhanalakshmi |
| author_facet | P. M. Booma S. Prabhakaran R. Dhanalakshmi |
| author_sort | P. M. Booma |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-7016462957bd41ca9e6f6b4d57dc6373 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-7016462957bd41ca9e6f6b4d57dc63732025-02-03T06:06:24ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/357873357873An Improved Pearson’s Correlation Proximity-Based Hierarchical Clustering for Mining Biological Association between GenesP. M. Booma0S. Prabhakaran1R. Dhanalakshmi2Department of Computer and Engineering, KCG College of Technology, KCG Nagar, Rajiv Gandhi Salai, Karapakkam, Chennai, Tamil Nadu 600097, IndiaDepartment of Computer Science and Engineering, SRM University, SRM Nagar, Kattankulathur, Kanchipuram, National Highway 45, Potheri, Tamil Nadu 603203, IndiaDepartment of Computer and Engineering, KCG College of Technology, KCG Nagar, Rajiv Gandhi Salai, Karapakkam, Chennai, Tamil Nadu 600097, IndiaMicroarray 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.http://dx.doi.org/10.1155/2014/357873 |
| spellingShingle | P. M. Booma S. Prabhakaran R. Dhanalakshmi An Improved Pearson’s Correlation Proximity-Based Hierarchical Clustering for Mining Biological Association between Genes The Scientific World Journal |
| title | An Improved Pearson’s Correlation Proximity-Based Hierarchical Clustering for Mining Biological Association between Genes |
| title_full | An Improved Pearson’s Correlation Proximity-Based Hierarchical Clustering for Mining Biological Association between Genes |
| title_fullStr | An Improved Pearson’s Correlation Proximity-Based Hierarchical Clustering for Mining Biological Association between Genes |
| title_full_unstemmed | An Improved Pearson’s Correlation Proximity-Based Hierarchical Clustering for Mining Biological Association between Genes |
| title_short | An Improved Pearson’s Correlation Proximity-Based Hierarchical Clustering for Mining Biological Association between Genes |
| title_sort | improved pearson s correlation proximity based hierarchical clustering for mining biological association between genes |
| url | http://dx.doi.org/10.1155/2014/357873 |
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