Markers improve clustering of CGH data

Markers improve clustering of CGH data

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Article ID: iaor2009351
Country: United Kingdom
Volume: 23
Start Page Number: 450
End Page Number: 457
Publication Date: Jan 2007
Journal: Bioinformatics
Authors: , ,
Keywords: programming: dynamic, heuristics
Abstract:

We consider the problem of clustering a population of Comparative Genomic Hybridization (CGH) data samples using similarity based clustering methods. A key requirement for clustering is to avoid using the noisy aberrations in the CGH samples. We develop a dynamic programming algorithm to identify a small set of important genomic intervals called markers. The advantage of using these markers is that the potentially noisy genomic intervals are excluded during the clustering process. We also develop two clustering strategies using these markers.

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