Literature DB >> 16706727

How scale-free are biological networks.

Raya Khanin1, Ernst Wit.   

Abstract

The concept of scale-free network has emerged as a powerful unifying paradigm in the study of complex systems in biology and in physical and social studies. Metabolic, protein, and gene interaction networks have been reported to exhibit scale-free behavior based on the analysis of the distribution of the number of connections of the network nodes. Here we study 10 published datasets of various biological interactions and perform goodness-of-fit tests to determine whether the given data is drawn from the power-law distribution. Our analysis did not identify a single interaction network that has a nonzero probability of being drawn from the power-law distribution.

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Year:  2006        PMID: 16706727     DOI: 10.1089/cmb.2006.13.810

Source DB:  PubMed          Journal:  J Comput Biol        ISSN: 1066-5277            Impact factor:   1.479


  66 in total

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2.  Modelling protein-protein interaction networks via a stickiness index.

Authors:  Natasa Przulj; Desmond J Higham
Journal:  J R Soc Interface       Date:  2006-10-22       Impact factor: 4.118

Review 3.  Building protein-protein interaction networks with proteomics and informatics tools.

Authors:  Mihaela E Sardiu; Michael P Washburn
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Journal:  Plant Cell       Date:  2012-10-30       Impact factor: 11.277

Review 5.  Assessment of the SRC Inhibition Role in the Efficacy of Breast Cancer Radiotherapy.

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Journal:  J Lasers Med Sci       Date:  2019-12-01

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Journal:  J Stat Softw       Date:  2021-07-10       Impact factor: 6.440

Review 7.  A scale-free systems theory of motivation and addiction.

Authors:  R Andrew Chambers; Warren K Bickel; Marc N Potenza
Journal:  Neurosci Biobehav Rev       Date:  2007-05-03       Impact factor: 8.989

8.  DiffCoEx: a simple and sensitive method to find differentially coexpressed gene modules.

Authors:  Bruno M Tesson; Rainer Breitling; Ritsert C Jansen
Journal:  BMC Bioinformatics       Date:  2010-10-06       Impact factor: 3.169

9.  NETWORK ASSISTED ANALYSIS TO REVEAL THE GENETIC BASIS OF AUTISM.

Authors:  Li Liu; Jing Lei; Kathryn Roeder
Journal:  Ann Appl Stat       Date:  2015-11-02       Impact factor: 2.083

10.  Reverse-engineering the Arabidopsis thaliana transcriptional network under changing environmental conditions.

Authors:  Javier Carrera; Guillermo Rodrigo; Alfonso Jaramillo; Santiago F Elena
Journal:  Genome Biol       Date:  2009-09-15       Impact factor: 13.583

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