Clustering microarrays with predictive weighted ensembles

Smyth, C., and Coomans, D. (2007) Clustering microarrays with predictive weighted ensembles. In: IEEE Synposium on Computational Intelligence and Bioinformatics and Computational Biology 2007. pp. 98-105. From: 2007 IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology, 1-5 April 2007, Honolulu, Hawaii.

[img] PDF
Restricted to Repository staff only

View at Publisher Website: http://ieeexplore.ieee.org/xpl/freeabs_a...
 
5
4


Abstract

Cluster ensembles seek a consensus across many individual partitions and the resulting solution is usually stable. Cluster ensembles are well suited to the analysis of DNA microarrays, where the tremendous size of the dataset can thwart the discovery of stable groups. Post processing cluster ensembles, where each individual partition is weighted according to its relative accuracy improves the performance of the ensemble whilst maintaining its stability. However, weighted cluster ensembles remain relatively unexplored, primarily because there are no common means of assessing the accuracy of individual clustering solutions. This paper describes a technique of creating weighted cluster ensembles suitable for use with microarray datasets. A regression technique is used to obtain individual cluster solutions. Each solution is then weighted according to its predictive accuracy. The consensus partition is obtained using a novel modification to the traditional k-means algorithm which further enforces the predictability of the solution. An estimate of the natural number of clusters can also be obtained using the modified k-means algorithm. Furthermore, a valuable byproduct of this weighted ensemble approach is a variable importance list. The methodology is applied on two well-known microarray datasets with promising results.

Item ID: 3097
Item Type: Conference Item (Research - E1)
ISBN: 1-4244-0710-9
Keywords: cluster ensembles; microarray
Date Deposited: 06 Jul 2009 06:45
FoR Codes: 01 MATHEMATICAL SCIENCES > 0104 Statistics > 010401 Applied Statistics @ 50%
03 CHEMICAL SCIENCES > 0301 Analytical Chemistry > 030106 Quality Assurance, Chemometrics, Traceability and Metrological Chemistry @ 50%
SEO Codes: 86 MANUFACTURING > 8608 Human Pharmaceutical Products > 860803 Human Pharmaceutical Treatments (e.g. Antibiotics) @ 100%
Downloads: Total: 4
More Statistics

Actions (Repository Staff Only)

Item Control Page Item Control Page