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» Evaluation of clustering algorithms for gene expression data
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CIKM
2004
Springer
15 years 2 months ago
Mining gene expression datasets using density-based clustering
Given the recent advancement of microarray technologies, we present a density-based clustering approach for the purpose of co-expressed gene cluster identification. The underlyin...
Seokkyung Chung, Jongeun Jun, Dennis McLeod
RECOMB
2002
Springer
15 years 9 months ago
A new approach to analyzing gene expression time series data
We present algorithms for time-series gene expression analysis that permit the principled estimation of unobserved timepoints, clustering, and dataset alignment. Each expression p...
Ziv Bar-Joseph, Georg Gerber, David K. Gifford, To...
BMCBI
2008
138views more  BMCBI 2008»
14 years 9 months ago
Methods for simultaneously identifying coherent local clusters with smooth global patterns in gene expression profiles
Background: The hierarchical clustering tree (HCT) with a dendrogram [1] and the singular value decomposition (SVD) with a dimension-reduced representative map [2] are popular met...
Yin-Jing Tien, Yun-Shien Lee, Han-Ming Wu, Chun-Ho...
BMCBI
2008
133views more  BMCBI 2008»
14 years 9 months ago
A Web-based and Grid-enabled dChip version for the analysis of large sets of gene expression data
Background: Microarray techniques are one of the main methods used to investigate thousands of gene expression profiles for enlightening complex biological processes responsible f...
Luca Corradi, Marco Fato, Ivan Porro, Silvia Scagl...
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CBMS
2005
IEEE
14 years 11 months ago
Biclustering of Expression Data Using Simulated Annealing
In gene expression data a bicluster is a subset of genes and a subset of conditions which show correlating levels of expression. However, the problem of finding significant biclu...
Kenneth Bryan, Padraig Cunningham, Nadia Bolshakov...