Literature DB >> 12142007

Linking the genes: inferring quantitative gene networks from microarray data.

Alberto de la Fuente1, Paul Brazhnik, Pedro Mendes.   

Abstract

Modern microarray technology is capable of providing data about the expression of thousands of genes, and even of whole genomes. An important question is how this technology can be used most effectively to unravel the workings of cellular machinery. Here, we propose a method to infer genetic networks on the basis of data from appropriately designed microarray experiments. In addition to identifying the genes that affect a specific other gene directly, this method also estimates the strength of such effects. We will discuss both the experimental setup and the theoretical background.

Mesh:

Year:  2002        PMID: 12142007     DOI: 10.1016/s0168-9525(02)02692-6

Source DB:  PubMed          Journal:  Trends Genet        ISSN: 0168-9525            Impact factor:   11.639


  35 in total

1.  Revealing strengths and weaknesses of methods for gene network inference.

Authors:  Daniel Marbach; Robert J Prill; Thomas Schaffter; Claudio Mattiussi; Dario Floreano; Gustavo Stolovitzky
Journal:  Proc Natl Acad Sci U S A       Date:  2010-03-22       Impact factor: 11.205

2.  Reverse engineering large-scale genetic networks: synthetic versus real data.

Authors:  Luwen Zhang; Mei Xiao; Yong Wang; Wu Zhang
Journal:  J Genet       Date:  2010-04       Impact factor: 1.166

3.  Discriminating direct and indirect connectivities in biological networks.

Authors:  Taek Kang; Richard Moore; Yi Li; Eduardo Sontag; Leonidas Bleris
Journal:  Proc Natl Acad Sci U S A       Date:  2015-09-29       Impact factor: 11.205

4.  Quantifying gene network connectivity in silico: scalability and accuracy of a modular approach.

Authors:  N Yalamanchili; D E Zak; B A Ogunnaike; J S Schwaber; A Kriete; B N Kholodenko
Journal:  Syst Biol (Stevenage)       Date:  2006-07

5.  Untangling the signalling wires.

Authors:  Boris N Kholodenko
Journal:  Nat Cell Biol       Date:  2007-03       Impact factor: 28.824

6.  Untangling the wires: a strategy to trace functional interactions in signaling and gene networks.

Authors:  Boris N Kholodenko; Anatoly Kiyatkin; Frank J Bruggeman; Eduardo Sontag; Hans V Westerhoff; Jan B Hoek
Journal:  Proc Natl Acad Sci U S A       Date:  2002-09-19       Impact factor: 11.205

7.  Genes that code for T cell signaling proteins establish transcriptional regulatory networks during thymus ontogeny.

Authors:  Cláudia Macedo; Danielle A Magalhães; Monique Tonani; Márcia C Marques; Cristina M Junta; Geraldo A S Passos
Journal:  Mol Cell Biochem       Date:  2008-07-03       Impact factor: 3.396

8.  Simulating systems genetics data with SysGenSIM.

Authors:  Andrea Pinna; Nicola Soranzo; Ina Hoeschele; Alberto de la Fuente
Journal:  Bioinformatics       Date:  2011-07-06       Impact factor: 6.937

Review 9.  Systems analysis of high-throughput data.

Authors:  Rosemary Braun
Journal:  Adv Exp Med Biol       Date:  2014       Impact factor: 2.622

Review 10.  Regulation by transcription factors in bacteria: beyond description.

Authors:  Enrique Balleza; Lucia N López-Bojorquez; Agustino Martínez-Antonio; Osbaldo Resendis-Antonio; Irma Lozada-Chávez; Yalbi I Balderas-Martínez; Sergio Encarnación; Julio Collado-Vides
Journal:  FEMS Microbiol Rev       Date:  2009-01       Impact factor: 16.408

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