Name
Abstract | ||
|---|---|---|
The identification of regulatory modules is one of the most important tasks in order to discover disease markers. This paper presents a methodology to infer coexpression networks based on local patterns in gene expression data matrix. In the proposed algorithm two steps can clearly be differentiated. Firstly, a Biclustering procedure that uses a Scatter Search schema to find biclusters and, secondly, a network extraction procedure based on linear correlations among the genes of the previously obtained bicluster. Experimental results from Yeast cell Cycle are reported where three different algorithms have been applied. Also, a possible understanding of one of the obtained networks has been presented from a biological point of view. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ISDA.2011.6121804 | Intelligent Systems Design and Applications |
Keywords | Field | DocType |
bioinformatics,diseases,feature extraction,matrix algebra,pattern clustering,biological point of view,disease marker,gene expression data matrix,inferring gene coexpression network,linear correlation,network extraction procedure,scatter search-based biclustering,yeast cell cycle,Biclustering,Gene Coexpression Networks,Gene Expression Data,Scatter Search | Data mining,Gene,Pattern recognition,Pattern clustering,Matrix algebra,Computer science,Feature extraction,Artificial intelligence,Biclustering,Disease markers,Machine learning | Conference |
ISSN | ISBN | Citations |
2164-7143 | 978-1-4577-1676-8 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Juan A. Nepomuceno | 1 | 57 | 7.10 |
| Alicia Troncoso Lora | 2 | 117 | 12.72 |
| Jesús S. Aguilar-ruiz | 3 | 625 | 59.56 |
| Troncoso, A. | 4 | 0 | 0.34 |
| Aguilar-Ruiz, J.S. | 5 | 32 | 3.04 |