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» Evaluation of clustering algorithms for gene expression data
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COMPLIFE
2005
Springer
16 years 6 days ago
Robust Perron Cluster Analysis for Various Applications in Computational Life Science
In the present paper we explain the basic ideas of Robust Perron Cluster Analysis (PCCA+) and exemplify the different application areas of this new and powerful method. Recently, ...
Marcus Weber, Susanna Kube
BMCBI
2005
189views more  BMCBI 2005»
15 years 6 months ago
Quantitative inference of dynamic regulatory pathways via microarray data
Background: The cellular signaling pathway (network) is one of the main topics of organismic investigations. The intracellular interactions between genes in a signaling pathway ar...
Wen-Chieh Chang, Chang-Wei Li, Bor-Sen Chen
BMCBI
2007
157views more  BMCBI 2007»
15 years 6 months ago
Constructing gene co-expression networks and predicting functions of unknown genes by random matrix theory
Background: Large-scale sequencing of entire genomes has ushered in a new age in biology. One of the next grand challenges is to dissect the cellular networks consisting of many i...
Feng Luo, Yunfeng Yang, Jianxin Zhong, Haichun Gao...
BMCBI
2002
136views more  BMCBI 2002»
15 years 6 months ago
Making sense of EST sequences by CLOBBing them
Background: Expressed sequence tags (ESTs) are single pass reads from randomly selected cDNA clones. They provide a highly cost-effective method to access and identify expressed g...
John Parkinson, David B. Guiliano, Mark L. Blaxter
166
Voted
DMKD
2003
ACM
96views Data Mining» more  DMKD 2003»
15 years 12 months ago
Using transposition for pattern discovery from microarray data
We analyze expression matrices to identify a priori interesting sets of genes, e.g., genes that are frequently co-regulated. Such matrices provide expression values for given biol...
François Rioult, Jean-François Bouli...