Combining Location and Expression Data for Principled Discovery of Genetic Regulatory Network Models

By Alexander J. Hartemink, David K. Gifford, Tommi S. Jaakkola, Richard A. Young. Pacific Symposium on Biocomputing, 2001

321 citations recorded by the source corpus.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/098598971a54fdb3c00f804ec3cb0ea8f2b3709f

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Citations in the corpus

Thirteen Years of Building Constraint-Based In Silico Models of Escherichia coli (2003, methodology): “Tools that should aid in this effort include RegulonDB, a database containing information about gene regulation in E. coli that is publicly available (47), and methods that extract gene regulatory networks from transcriptomic data (24).”
Inferring cellular networks – a review (2007, methodology): “[69] use a discretization coalescence method, which incrementally reduces the number of discretization levels for each gene while preserving as much total mutual information between genes as possible.”
A Practical Approach to Microarray Data Analysis (2003, background): “, 2002) as well as for the genetic network analysis (Friedman et al., 2000; Hartemink et al., 2001) with microarray data.”
From patterns to pathways: gene expression data analysis comes of age (2002, background): “…focus further on ﬁnding probabilistically supported gene interactions or on combining these into subnetworks 82,83 , on modeling ‘latent’ or hidden variables representing biological information unavailable to the model 84,85 , and on incorporating prior biological knowledge or annotation 82,86 .”
Computational prediction of transcription-factor binding site locations (2003, background): “…kinds of transcription-factor binding-site data, together with mRNA expression analysis, protein-interaction databases and prior genetic and biochemical data in the literature, will allow the construction of more detailed connectivity maps of transcriptional regulatory networks [10,13,121-125].”
Advances to Bayesian network inference for generating causal networks from observational biological data (2004, background): “Bayesian network (BN) inference algorithms have shown particular promise (Hartemink et al., 2002; Husmeier, 2003; Smith et al., 2002, 2003), because unlike most other modeling frameworks, they can capture many types of relationships between variables.”
Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles (2007, background): “Pioneering efforts to identify regulatory interactions on a genome scale have used machine-learning algorithms to identify cis-regulatory motifs or transcription factor target genes using a large set of expression arrays [2–18], genomewide location analysis chromatin immunoprecipitation (ChIP-Chip) ”
Gene regulatory network inference: Data integration in dynamic models - A review (2009, methodology): “Some authors reconstruct networks that are restricted to a hierarchical structure, e.g. Hartemink et al. (2002) using a BN formalism, whereas others forbid short loops.”