Inferring cellular networks – a review
TLDR
This review gives an overview of computational and statistical methods to reconstruct cellular networks and deals with conditional independence models including Gaussian graphical models and Bayesian networks.Abstract:
In this review we give an overview of computational and statistical methods to reconstruct cellular networks. Although this area of research is vast and fast developing, we show that most currently used methods can be organized by a few key concepts. The first part of the review deals with conditional independence models including Gaussian graphical models and Bayesian networks. The second part discusses probabilistic and graph-based methods for data from experimental interventions and perturbations.read more
Citations
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Inferring Regulatory Networks from Expression Data Using Tree-Based Methods
TL;DR: This article presents GENIE3, a new algorithm for the inference of GRNs that was best performer in the DREAM4 In Silico Multifactorial challenge and compares favorably with existing algorithms to decipher the genetic regulatory network of Escherichia coli.
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Gene regulatory network inference: Data integration in dynamic models—A review
TL;DR: This review deals with the reconstruction of gene regulatory networks (GRNs) from experimental data through computational methods and approaches are discussed that enable the modelling of the dynamics of Gene regulatory systems.
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A human functional protein interaction network and its application to cancer data analysis
TL;DR: A highly reliable functional interaction network upon expert-curated pathways is built and applied to the analysis of two genome-wide GBM and several other cancer data sets, suggesting common mechanisms in the cancer biology.
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Comparison of co-expression measures: mutual information, correlation, and model based indices
TL;DR: The biweight midcorrelation outperforms MI in terms of elucidating gene pairwise relationships and can safely be replaced by correlation networks when it comes to measuring co-expression relationships in stationary data.
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Noise in biology
TL;DR: This short review covers the recent progress in understanding mechanisms and effects of fluctuations in biological systems of different scales and the basic approaches to their mathematical modeling.
References
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Estimating the Dimension of a Model
TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.
Estimating the dimension of a model
TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.
Journal ArticleDOI
Gene Ontology: tool for the unification of biology
M Ashburner,Catherine A. Ball,Judith A. Blake,David Botstein,Heather Butler,J. M. Cherry,Allan Peter Davis,Kara Dolinski,Selina S. Dwight,J.T. Eppig,Midori A. Harris,David P. Hill,Laurie Issel-Tarver,Andrew Kasarskis,Suzanna E. Lewis,John C. Matese,Joel E. Richardson,M. Ringwald,Gerald M. Rubin,Gavin Sherlock +19 more
TL;DR: The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing.
Book
System Identification: Theory for the User
TL;DR: Das Buch behandelt die Systemidentifizierung in dem theoretischen Bereich, der direkte Auswirkungen auf Verstaendnis and praktische Anwendung der verschiedenen Verfahren zur IdentifIZierung hat.
Journal ArticleDOI
Cluster analysis and display of genome-wide expression patterns
TL;DR: A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression, finding in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function.