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» Clustering Improves the Exploration of Graph Mining Results
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BMCBI
2006
155views more  BMCBI 2006»
14 years 12 months ago
AffyMiner: mining differentially expressed genes and biological knowledge in GeneChip microarray data
Background: DNA microarrays are a powerful tool for monitoring the expression of tens of thousands of genes simultaneously. With the advance of microarray technology, the challeng...
Guoqing Lu, The V. Nguyen, Yuannan Xia, Michael Fr...
PVLDB
2010
112views more  PVLDB 2010»
14 years 10 months ago
On Triangulation-based Dense Neighborhood Graphs Discovery
This paper introduces a new definition of dense subgraph pattern, the DN-graph. DN-graph considers both the size of the sub-structure and the minimum level of interactions betwee...
Nan Wang, Jingbo Zhang, Kian-Lee Tan, Anthony K. H...
BMCBI
2007
162views more  BMCBI 2007»
14 years 12 months ago
Three-Dimensional Phylogeny Explorer: Distinguishing paralogs, lateral transfer, and violation of "molecular clock" assumption w
Background: Construction and interpretation of phylogenetic trees has been a major research topic for understanding the evolution of genes. Increases in sequence data and complexi...
Namshin Kim, Christopher Lee
LREC
2010
141views Education» more  LREC 2010»
15 years 1 months ago
Exploring Knowledge Bases for Similarity
Graph-based similarity over WordNet has been previously shown to perform very well on word similarity. This paper presents a study of the performance of such a graph-based algorit...
Eneko Agirre, Montse Cuadros, German Rigau, Aitor ...
JCB
2002
160views more  JCB 2002»
14 years 11 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, ...