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Loss of Conservation of Graph Centralities in Reverse-engineered Transcriptional Regulatory Networks
Uppsala University, Sweden. (MAM)
Uppsala University, Sweden.
Mälardalen University, School of Education, Culture and Communication, Educational Sciences and Mathematics. (MAM)ORCID iD: 0000-0002-1624-5147
Uppsala University, Sweden.
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(English)In: Methodology and Computing in Applied Probability, ISSN 1387-5841, E-ISSN 1573-7713, ISSN 1387-5841Article in journal (Refereed) In press
Abstract [en]

Graph centralities are commonly used to identify and prioritize disease genes in transcriptional regulatory networks. Studies on small networks of experimentally validated protein-protein interactions underpin the general validity of this approach and extensions of such findings have recently been proposed for networks inferred from gene expression data. However, it is largely unknown how well gene centralities are preserved between the underlying biological interactions and the networks inferred from gene expression data. Specifically, while previous studies have evaluated the performance of inference methods on synthetic gene expression, it has not been established how the choice of inference method affects individual centralities in the network. Here, we compare two gene centrality measures between reference networks and networks inferred from corresponding simulated gene expression data, using a number of commonly used network inference methods. The results indicate that the centrality of genes is only moderately conserved for all of the inference methods used. In conclusion, caution should be exercised when inspecting centralities in reverse-engineered networks and further work will be required to establish the use of such networks for prioritizing disease genes.

Place, publisher, year, edition, pages
Springer.
Keyword [en]
Transcriptional regulatory network inference,  Simulated gene expression,  Graph centrality
National Category
Probability Theory and Statistics Bioinformatics and Systems Biology Bioinformatics (Computational Biology)
Research subject
Mathematics/Applied Mathematics
Identifiers
URN: urn:nbn:se:mdh:diva-36593DOI: 10.1007/s11009-017-9554-7OAI: oai:DiVA.org:mdh-36593DiVA: diva2:1145932
Funder
Swedish Childhood Cancer Foundation
Available from: 2017-09-30 Created: 2017-10-01 Last updated: 2017-10-03Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
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  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
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