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Prediction of high centrality nodes from reverse-engineered transcriptional regulator networks
Mälardalen University, School of Education, Culture and Communication, Educational Sciences and Mathematics. Uppsala University, Sweden. (MAM)
Mälardalen University, School of Education, Culture and Communication, Educational Sciences and Mathematics. (MAM)ORCID iD: 0000-0002-1624-5147
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2016 (English)In: Proocedings of the 4th Stochastic Modeling Techniques and Data Analysis International Conference with Demographics Workshop / [ed] Christos H Skiadas, 2016, 517-531 p.Conference paper, Published paper (Refereed)
Abstract [en]

The prioritization of genes based on their centrality in biological networkshas emerged as a promising technique for the prediction of phenotype related genes.A number of methods have been developed to derive one such type of network, i.e.transcriptional regulatory networks, from expression data. In order to reliably prioritizegenes from such networks, it is crucial to investigate how well the inferencemethods reconstruct the centralities that exist in the true biological system. We haverecently reported that the correlation of centrality rankings between reference andinferred networks is only modest when using an unbiased inference approach. In thisstudy we extend on these results and demonstrate that the correlation remains modestalso when using a biased inference utilizing a priori information about transcriptionfactors. However, we show further that despite this lack of a strong correlation, theinferred networks still allow a signicant prediction of genes with high centralities inthe reference networks.

Place, publisher, year, edition, pages
2016. 517-531 p.
Keyword [en]
Transcriptional network inference, network inference, graph centrality, degree, betweenness.
National Category
Bioinformatics (Computational Biology) Probability Theory and Statistics
Research subject
Mathematics/Applied Mathematics
Identifiers
URN: urn:nbn:se:mdh:diva-36583OAI: oai:DiVA.org:mdh-36583DiVA: diva2:1145889
Conference
4th Stochastic Modeling Techniques and Data Analysis International Conference with Demographics Workshop
Available from: 2017-09-30 Created: 2017-09-30 Last updated: 2017-10-03Bibliographically approved

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Citation style
  • apa
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Language
  • de-DE
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  • nn-NB
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Output format
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