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FGCN
2008
IEEE
155views Communications» more  FGCN 2008»
13 years 9 months ago
Modeling the Marginal Distribution of Gene Expression with Mixture Models
We report the results of fitting mixture models to the distribution of expression values for individual genes over a broad range of normal tissues, which we call the marginal expr...
Edward Wijaya, Hajime Harada, Paul Horton
BMCBI
2006
123views more  BMCBI 2006»
13 years 7 months ago
Characterizing disease states from topological properties of transcriptional regulatory networks
Background: High throughput gene expression experiments yield large amounts of data that can augment our understanding of disease processes, in addition to classifying samples. He...
David Tuck, Harriet Kluger, Yuval Kluger
BMCBI
2010
154views more  BMCBI 2010»
13 years 7 months ago
Candidate gene prioritization by network analysis of differential expression using machine learning approaches
Background: Discovering novel disease genes is still challenging for diseases for which no prior knowledge - such as known disease genes or disease-related pathways - is available...
Daniela Nitsch, Joana P. Gonçalves, Fabian ...
ISMB
2004
13 years 8 months ago
Predicting gene regulation by sigma factors in Bacillus subtilis from genome-wide data
Motivation: Sigma factors regulate the expression of genes in Bacillus subtilis at the transcriptional level. First we assess the ability of currently available gene regulatory ne...
Michiel J. L. de Hoon, Yuko Makita, Seiya Imoto, K...
JIB
2006
110views more  JIB 2006»
13 years 7 months ago
Combining biomedical knowledge and transcriptomic data to extract new knowledge on genes
In biomedical research, interpretation of microarray data requires confrontation of data and knowledge from heterogeneous resources, either in the biomedical domain or in genomics...
Emilie Guérin, Gwenaëlle Marquet, Juli...