Literature DB >> 11557882

Blazing pathways through genetic mountains.

D K Gifford1.   

Abstract

It is now widely accepted that high-throughput data sources will shed essential understanding on the inner workings of cellular and organism function. One key challenge is to distill the results of such experiments into an interpretable computational form that will be the basis of a predictive model. A predictive model represents the gold standard in understanding a biological system and will permit us to investigate the underlying cause of diseases and help us to develop therapeutics. Here I explore how discoveries can be based on high-throughput data sources and discuss how independent discoveries can be assembled into a comprehensive picture of cellular function.

Mesh:

Year:  2001        PMID: 11557882     DOI: 10.1126/science.1065113

Source DB:  PubMed          Journal:  Science        ISSN: 0036-8075            Impact factor:   47.728


  3 in total

1.  Towards a new age of virtual ADME/TOX and multidimensional drug discovery.

Authors:  Sean Ekins; Bruno Boulanger; Peter W Swaan; Maggie A Z Hupcey
Journal:  J Comput Aided Mol Des       Date:  2002 May-Jun       Impact factor: 3.686

Review 2.  Towards a new age of virtual ADME/TOX and multidimensional drug discovery.

Authors:  Sean Ekins; Bruno Boulanger; Peter W Swaan; Maggie A Z Hupcey
Journal:  Mol Divers       Date:  2002       Impact factor: 2.943

3.  The five-gene-network data analysis with local causal discovery algorithm using causal Bayesian networks.

Authors:  Changwon Yoo; Erik M Brilz
Journal:  Ann N Y Acad Sci       Date:  2009-03       Impact factor: 5.691

  3 in total

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