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KDD
2001
ACM
169views Data Mining» more  KDD 2001»
14 years 8 months ago
Hierarchical cluster analysis of SAGE data for cancer profiling
In this paper we present a method for clustering SAGE (Serial Analysis of Gene Expression) data to detect similarities and dissimilarities between different types of cancer on the...
Jörg Sander, Monica C. Sleumer, Raymond T. Ng
BMCBI
2005
143views more  BMCBI 2005»
13 years 7 months ago
Gene capture prediction and overlap estimation in EST sequencing from one or multiple libraries
Background: In expressed sequence tag (EST) sequencing, we are often interested in how many genes we can capture in an EST sample of a targeted size. This information provides ins...
Ji-Ping Z. Wang, Bruce G. Lindsay 0002, Liying Cui...
BMCBI
2011
13 years 2 months ago
RedundancyMiner: De-replication of redundant GO categories in microarray and proteomics analysis
Background: The Gene Ontology (GO) Consortium organizes genes into hierarchical categories based on biological process, molecular function and subcellular localization. Tools such...
Barry Zeeberg, Hongfang Liu, Ari B. Kahn, Martin E...
BMCBI
2008
122views more  BMCBI 2008»
13 years 7 months ago
A practical comparison of two K-Means clustering algorithms
Background: Data clustering is a powerful technique for identifying data with similar characteristics, such as genes with similar expression patterns. However, not all implementat...
Gregory A. Wilkin, Xiuzhen Huang
BMCBI
2004
117views more  BMCBI 2004»
13 years 7 months ago
Cancer characterization and feature set extraction by discriminative margin clustering
Background: A central challenge in the molecular diagnosis and treatment of cancer is to define a set of molecular features that, taken together, distinguish a given cancer, or ty...
Kamesh Munagala, Robert Tibshirani, Patrick O. Bro...