Literature DB >> 10322108

Functional genomics: learning to think about gene expression data.

R Brent1.   

Abstract

Three recent studies of gene expression patterns in whole cells provide examples of the inferences one can make from this type of information. They also provide examples of the non-traditional types of reasoning we will need to use to make such inferences.

Mesh：

Year:  1999        PMID： 10322108     DOI： 10.1016/s0960-9822(99)80208-5

Source DB:  PubMed          Journal:  Curr Biol        ISSN： 0960-9822            Impact factor:   10.834


  3 in total

1.  Metabolomics and machine learning: explanatory analysis of complex metabolome data using genetic programming to produce simple, robust rules.

Authors:  Douglas B Kell
Journal:  Mol Biol Rep       Date:  2002       Impact factor: 2.316

2.  Prosecutor: parameter-free inference of gene function for prokaryotes using DNA microarray data, genomic context and multiple gene annotation sources.

Authors:  Evert Jan Blom; Rainer Breitling; Klaas Jan Hofstede; Jos B T M Roerdink; Sacha A F T van Hijum; Oscar P Kuipers
Journal:  BMC Genomics       Date:  2008-10-21       Impact factor: 3.969

3.  Functional genomics via metabolic footprinting: monitoring metabolite secretion by Escherichia coli tryptophan metabolism mutants using FT-IR and direct injection electrospray mass spectrometry.

Authors:  Naheed N Kaderbhai; David I Broadhurst; David I Ellis; Royston Goodacre; Douglas B Kell
Journal:  Comp Funct Genomics       Date:  2003
  3 in total

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