Inferring quantitative models of regulatory networks from expression data
نویسندگان
چکیده
منابع مشابه
Inferring quantitative models of regulatory networks from expression data
MOTIVATION Genetic networks regulate key processes in living cells. Various methods have been suggested to reconstruct network architecture from gene expression data. However, most approaches are based on qualitative models that provide only rough approximations of the underlying events, and lack the quantitative aspects that are critical for understanding the proper function of biomolecular sy...
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Over the past few years, the advent of microarray technology has enabled the simultaneous measurement of the expression levels of thousands of genes. When the expression levels of these genes are measured at multiple time points during an experiment, the result is a temporal expression profile. These expression profiles may be processed to extract the underlying gene regulatory network relation...
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One of the pressing open problems of computational systems biology is the elucidation of the topology of genetic regulatory networks (GRNs) using high throughput genomic data, in particular microarray gene expression data. The Dialogue for Reverse Engineering Assessments and Methods (DREAM) challenge aims to evaluate the success of GRN inference algorithms on benchmarks of simulated data. In th...
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The modeling of gene networks from transcriptional expression data is an important tool in biomedical research to reveal signaling pathways and to identify treatment targets. Current gene network modeling is primarily based on the use of Gaussian graphical models applied to continuous data, which give a closed-form marginal likelihood. In this paper, we extend network modeling to discrete data,...
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ژورنال
عنوان ژورنال: Bioinformatics
سال: 2004
ISSN: 1367-4803,1460-2059
DOI: 10.1093/bioinformatics/bth941