Genetic network inference: from co-expression clustering to reverse engineering

By Patrik D'haeseleer, Shoudan Liang, Roland Somogyi. Bioinform., 2000

1,038 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/06119fafe929e406d04468eee6bc82d593edd191

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In Molecular Biology

Citations in the corpus

Weighted Gene Coexpression Network Analysis: State of the Art (2010, background): “The global gene expression is a result of the collective behavior of individual regulatory pathways D’haeseleer et al., 2000). in DNA trigger molecular networks that cause .”
The road to modularity (2007, neutral): “Methods used to reveal co-regulated target genes involve clustering genes on the basis of the similarities in gene expression change”
Systems biology of stem cell fate and cellular reprogramming (2009, background): “Extracting putative functional connections between elements in the parts list often requires some computational input, either by reverse engineering regulatory networks from specific experimentally derived datasets using sophisticated computational inference technique”
Gene regulatory network inference: Data integration in dynamic models - A review (2009, methodology): “Afterwards, cluster-representative enes or the mean expression level of all genes in a cluster might be sed for GRN inference (D’haeseleer et al., 2000; Wahde and Hertz, 000; Mjolsness et al., 2000; van Someren et al., 2000; Guthke et l., 2005, 2007; Bonneau et al., 2006). Clustering does not guaran”
Dynamic modeling of genetic networks using genetic algorithm and S-system (2003, methodology): “conventional methods have aimed mainly at pathwayfinding (D’haeseleer et al., 2000), the future research focuses more on dynamic changes and interactions among biological objects.”
Inverse statistical problems: from the inverse Ising problem to data science (2017, background): “See [64] for a review of clustering and Boolean network inference and [96] for a review of Boolean network inference.”
Observing and Interpreting Correlations in Metabolic Networks (2003, background): “The correlation coefficient implicitly defines a ‘distance’ between metabolites and usually serves as a starting point for many data mining tools, such as various types of clustering algorithms (D’haeseleer et al., 2000).”
Harnessing naturally randomized transcription to infer regulatory relationships among genes (2007, background): “Model selection approaches The concepts of 'causality' and 'regulation' have been utilized in different ways in previous reports concerning the construction of biologic networks [29,30,32,56-60].”
Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks (2003, methodology): “Several approaches to the reverse engineering of genetic regulatory networks from gene expression data have been explored, reviewed, for instance, by D’haeseleer et al. (2000) and De Jong (2002) .”