Literature DB >> 27595119

Estimation of Gene Regulatory Networks.

Matthew N McCall1.   

Abstract

Complex gene regulatory networks, not individual genes, control cellular function. Genes and gene products act together to determine cellular phenotypes. Estimation of these networks is necessary to understand cellular mechanisms, detect differences in gene regulation between cell types, and predict cellular response to interventions. A plethora of algorithms have been developed to infer network structure from experimental data. Here we provide a general introduction to the estimation of gene regulatory networks and the classes of proposed algorithms.

Entities:  

Year:  2013        PMID: 27595119      PMCID: PMC5010161     

Source DB:  PubMed          Journal:  Postdoc J        ISSN: 2328-9791


  55 in total

1.  Identification of genetic networks from a small number of gene expression patterns under the Boolean network model.

Authors:  T Akutsu; S Miyano; S Kuhara
Journal:  Pac Symp Biocomput       Date:  1999

2.  Binary analysis and optimization-based normalization of gene expression data.

Authors:  Ilya Shmulevich; Wei Zhang
Journal:  Bioinformatics       Date:  2002-04       Impact factor: 6.937

3.  Importance of input perturbations and stochastic gene expression in the reverse engineering of genetic regulatory networks: insights from an identifiability analysis of an in silico network.

Authors:  Daniel E Zak; Gregory E Gonye; James S Schwaber; Francis J Doyle
Journal:  Genome Res       Date:  2003-11       Impact factor: 9.043

Review 4.  The art and design of genetic screens: RNA interference.

Authors:  Michael Boutros; Julie Ahringer
Journal:  Nat Rev Genet       Date:  2008-06-03       Impact factor: 53.242

5.  Mapping and quantifying mammalian transcriptomes by RNA-Seq.

Authors:  Ali Mortazavi; Brian A Williams; Kenneth McCue; Lorian Schaeffer; Barbara Wold
Journal:  Nat Methods       Date:  2008-05-30       Impact factor: 28.547

6.  Tumor suppressor p53 restricts Ras stimulation of RhoA and cancer cell motility.

Authors:  Mingxuan Xia; Hartmut Land
Journal:  Nat Struct Mol Biol       Date:  2007-02-18       Impact factor: 15.369

7.  Evolvability and hierarchy in rewired bacterial gene networks.

Authors:  Mark Isalan; Caroline Lemerle; Konstantinos Michalodimitrakis; Carsten Horn; Pedro Beltrao; Emanuele Raineri; Mireia Garriga-Canut; Luis Serrano
Journal:  Nature       Date:  2008-04-17       Impact factor: 49.962

8.  Transcriptome-wide noise controls lineage choice in mammalian progenitor cells.

Authors:  Hannah H Chang; Martin Hemberg; Mauricio Barahona; Donald E Ingber; Sui Huang
Journal:  Nature       Date:  2008-05-22       Impact factor: 49.962

9.  Boolean network model predicts cell cycle sequence of fission yeast.

Authors:  Maria I Davidich; Stefan Bornholdt
Journal:  PLoS One       Date:  2008-02-27       Impact factor: 3.240

10.  Unbiased reconstruction of a mammalian transcriptional network mediating pathogen responses.

Authors:  Ido Amit; Manuel Garber; Nicolas Chevrier; Ana Paula Leite; Yoni Donner; Thomas Eisenhaure; Mitchell Guttman; Jennifer K Grenier; Weibo Li; Or Zuk; Lisa A Schubert; Brian Birditt; Tal Shay; Alon Goren; Xiaolan Zhang; Zachary Smith; Raquel Deering; Rebecca C McDonald; Moran Cabili; Bradley E Bernstein; John L Rinn; Alex Meissner; David E Root; Nir Hacohen; Aviv Regev
Journal:  Science       Date:  2009-09-03       Impact factor: 47.728

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  2 in total

1.  Limb Mesoderm and Head Ectomesenchyme Both Express a Core Transcriptional Program During Chondrocyte Differentiation.

Authors:  Patsy Gomez-Picos; Katie Ovens; B Frank Eames
Journal:  Front Cell Dev Biol       Date:  2022-06-17

2.  A Machine Learning Method for Identifying Critical Interactions Between Gene Pairs in Alzheimer's Disease Prediction.

Authors:  Hao Chen; Yong He; Jiadong Ji; Yufeng Shi
Journal:  Front Neurol       Date:  2019-10-31       Impact factor: 4.003

  2 in total

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