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RECOMB
2001
Springer
14 years 9 months ago
Context-specific Bayesian clustering for gene expression data
The recent growth in genomic data and measurements of genome-wide expression patterns allows us to apply computational tools to examine gene regulation by transcription factors. I...
Yoseph Barash, Nir Friedman
ECCB
2008
IEEE
14 years 3 months ago
SIRENE: supervised inference of regulatory networks
Living cells are the product of gene expression programs that involve the regulated transcription of thousands of genes. The elucidation of transcriptional regulatory networks in ...
Fantine Mordelet, Jean-Philippe Vert
BIBE
2004
IEEE
107views Bioinformatics» more  BIBE 2004»
14 years 19 days ago
Enhanced pClustering and Its Applications to Gene Expression Data
Clustering has been one of the most popular methods to discover useful biological insights from DNA microarray. An interesting paradigm is simultaneous clustering of both genes an...
Sungroh Yoon, Christine Nardini, Luca Benini, Giov...
RECOMB
2001
Springer
14 years 9 months ago
Class discovery in gene expression data
Recent studies (Alizadeh et al, [1]; Bittner et al,[5]; Golub et al, [11]) demonstrate the discovery of putative disease subtypes from gene expression data. The underlying computa...
Amir Ben-Dor, Nir Friedman, Zohar Yakhini
RECOMB
2007
Springer
14 years 9 months ago
Learning Gene Regulatory Networks via Globally Regularized Risk Minimization
Learning the structure of a gene regulatory network from time-series gene expression data is a significant challenge. Most approaches proposed in the literature to date attempt to ...
Yuhong Guo, Dale Schuurmans