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» E-CAST: A Data Mining Algorithm for Gene Expression Data
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AI
2006
Springer
14 years 22 days ago
A New Profile Alignment Method for Clustering Gene Expression Data
We focus on clustering gene expression temporal profiles, and propose a novel, simple algorithm that is powerful enough to find an efficient distribution of genes over clusters. We...
Ataul Bari, Luis Rueda
PAKDD
2010
ACM
203views Data Mining» more  PAKDD 2010»
14 years 1 months ago
Finding Itemset-Sharing Patterns in a Large Itemset-Associated Graph
Both itemset mining and graph mining have been studied independently. Here, we introduce a novel data structure, which is an unweighted graph whose vertices contain itemsets. From ...
Mutsumi Fukuzaki, Mio Seki, Hisashi Kashima, Jun S...
KDD
2001
ACM
169views Data Mining» more  KDD 2001»
14 years 9 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
AIIA
2009
Springer
14 years 3 months ago
Ontology-Driven Co-clustering of Gene Expression Data
Abstract. The huge volume of gene expression data produced by microarrays and other high-throughput techniques has encouraged the development of new computational techniques to eva...
Francesca Cordero, Ruggero G. Pensa, Alessia Visco...
DSS
2007
127views more  DSS 2007»
13 years 9 months ago
Large-scale regulatory network analysis from microarray data: modified Bayesian network learning and association rule mining
We present two algorithms for learning large-scale gene regulatory networks from microarray data: a modified informationtheory-based Bayesian network algorithm and a modified asso...
Zan Huang, Jiexun Li, Hua Su, George S. Watts, Hsi...