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BMCBI
2008
104views more  BMCBI 2008»
14 years 9 months ago
Missing value imputation improves clustering and interpretation of gene expression microarray data
Background: Missing values frequently pose problems in gene expression microarray experiments as they can hinder downstream analysis of the datasets. While several missing value i...
Johannes Tuikkala, Laura Elo, Olli Nevalainen, Ter...
JCB
2002
160views more  JCB 2002»
14 years 9 months ago
Inference from Clustering with Application to Gene-Expression Microarrays
There are many algorithms to cluster sample data points based on nearness or a similarity measure. Often the implication is that points in different clusters come from different u...
Edward R. Dougherty, Junior Barrera, Marcel Brun, ...
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IJSI
2008
122views more  IJSI 2008»
14 years 9 months ago
Mining Gene Expression Data using Domain Knowledge
Biology is now an information-intensive science and various research areas, like molecular biology, evolutionary biology or environmental biology, heavily depend on the availabilit...
Nicolas Pasquier, Claude Pasquier, Laurent Brisson...
BMCBI
2008
126views more  BMCBI 2008»
14 years 9 months ago
Methods for evaluating gene expression from Affymetrix microarray datasets
Background: Affymetrix high density oligonucleotide expression arrays are widely used across all fields of biological research for measuring genome-wide gene expression. An import...
Ning Jiang, Lindsey J. Leach, Xiaohua Hu, Elena Po...
BIBE
2004
IEEE
120views Bioinformatics» more  BIBE 2004»
15 years 1 months ago
Identifying Projected Clusters from Gene Expression Profiles
In microarray gene expression data, clusters may hide in subspaces. Traditional clustering algorithms that make use of similarity measurements in the full input space may fail to ...
Kevin Y. Yip, David W. Cheung, Michael K. Ng, Kei-...