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» Clustering cancer gene expression data: a comparative study
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112
Voted
BMCBI
2010
214views more  BMCBI 2010»
14 years 11 months ago
AutoSOME: a clustering method for identifying gene expression modules without prior knowledge of cluster number
Background: Clustering the information content of large high-dimensional gene expression datasets has widespread application in "omics" biology. Unfortunately, the under...
Aaron M. Newman, James B. Cooper
137
Voted
BMCBI
2008
160views more  BMCBI 2008»
14 years 11 months ago
Predicting cancer involvement of genes from heterogeneous data
Background: Systematic approaches for identifying proteins involved in different types of cancer are needed. Experimental techniques such as microarrays are being used to characte...
Ramon Aragues, Chris Sander, Baldo Oliva
KDD
2004
ACM
142views Data Mining» more  KDD 2004»
16 years 1 days ago
Meta-classification of Multi-type Cancer Gene Expression Data
Massive publicly available gene expression data consisting of different experimental conditions and microarray platforms introduce new challenges in data mining when integrating m...
Benny Y. M. Fung, Vincent T. Y. Ng
146
Voted
BMCBI
2011
14 years 6 months ago
A novel approach to the clustering of microarray data via nonparametric density estimation
Background: Cluster analysis is a crucial tool in several biological and medical studies dealing with microarray data. Such studies pose challenging statistical problems due to di...
Riccardo De Bin, Davide Risso