Name
Title | ||
|---|---|---|
Gene subsets extraction based on Mutual-Information-based Minimum Spanning Trees model. |
Abstract | ||
|---|---|---|
In microarray data analysis, filter methods with low time complexity neglect correlation among genes. Metrics to calculate the correlation in some of the methods can not effectively reflect function similarity among genes and time complexity is based on the whole gene set. Therefore, a novel selection model called Mutual-Information-based Minimum Spanning Trees (MIMST) is proposed in this paper, which first uses filter methods to remove non-relevant genes, then computes the interdependence of top-ranked genes, and eliminates the redundant genes. The empirical results show that MIMST can find the smallest significant genes subset with higher classification accuracy compared with other methods. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1504/IJCBDD.2009.028823 | I. J. Computational Biology and Drug Design |
Keywords | Field | DocType |
microarray gene expression data analysis,mst,minimum spanning trees,mutual information,gene selection,minimum spanning tree,microarray data analysis,filtering | Gene,Microarray analysis techniques,Spanning tree,Artificial intelligence,Time complexity,Gene selection,Pattern recognition,Filter (signal processing),Correlation,Mutual information,Bioinformatics,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
2 | 2 | 1756-0756 |
Citations | PageRank | References |
0 | 0.34 | 13 |
Authors | ||
4 | ||