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» Smoothing Gene Expression Using Biological Networks
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BMCBI
2008
174views more  BMCBI 2008»
13 years 9 months ago
Evolutionary approaches for the reverse-engineering of gene regulatory networks: A study on a biologically realistic dataset
Background: Inferring gene regulatory networks from data requires the development of algorithms devoted to structure extraction. When only static data are available, gene interact...
Cédric Auliac, Vincent Frouin, Xavier Gidro...
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...
ICPR
2006
IEEE
14 years 10 months ago
Finding Rule Groups to Classify High Dimensional Gene Expression Datasets
Microarray data provides quantitative information about the transcription profile of cells. To analyze microarray datasets, methodology of machine learning has increasingly attrac...
Jiyuan An, Yi-Ping Phoebe Chen
DILS
2005
Springer
14 years 2 months ago
Hybrid Integration of Molecular-Biological Annotation Data
: We present a new approach to integrate annotation data from public sources for the expression analysis of genes and proteins. Expression data is materialized in a data warehouse ...
Toralf Kirsten, Hong Hai Do, Christine Körner...