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» Semantic Mining and Analysis of Gene Expression Data
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BMCBI
2004
205views more  BMCBI 2004»
13 years 8 months ago
A combinational feature selection and ensemble neural network method for classification of gene expression data
Background: Microarray experiments are becoming a powerful tool for clinical diagnosis, as they have the potential to discover gene expression patterns that are characteristic for...
Bing Liu, Qinghua Cui, Tianzi Jiang, Songde Ma
AUSAI
2005
Springer
14 years 2 months ago
Finding Similar Patterns in Microarray Data
Abstract. In this paper we propose a clustering algorithm called sCluster for analysis of gene expression data based on pattern-similarity. The algorithm captures the tight cluster...
Xiangsheng Chen, Jiuyong Li, Grant Daggard, Xiaodi...
CSB
2004
IEEE
106views Bioinformatics» more  CSB 2004»
14 years 21 days ago
A Theoretical Analysis of Gene Selection
A great deal of recent research has focused on the challenging task of selecting differentially expressed genes from microarray data (`gene selection'). Numerous gene selecti...
Sach Mukherjee, Stephen J. Roberts
JMLR
2010
125views more  JMLR 2010»
13 years 3 months ago
On utility of gene set signatures in gene expression-based cancer class prediction
Machine learning methods that can use additional knowledge in their inference process are central to the development of integrative bioinformatics. Inclusion of background knowled...
Minca Mramor, Marko Toplak, Gregor Leban, Tomaz Cu...
BMCBI
2005
112views more  BMCBI 2005»
13 years 8 months ago
Vector analysis as a fast and easy method to compare gene expression responses between different experimental backgrounds
Background: Gene expression studies increasingly compare expression responses between different experimental backgrounds (genetic, physiological, or phylogenetic). By focusing on ...
Rainer Breitling, Patrick Armengaud, Anna Amtmann