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Learning Gaussian Graphical Models of Gene Networks with False Discovery Rate Control
Linköping University, Department of Computer and Information Science, Database and information techniques. (ADIT)
2008 (English)In: Proceedings of the 6th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, EvoBIO 2008, Berlin: Springer Verlag , 2008, 165-176 p.Conference paper, Published paper (Refereed)
Abstract [en]

In many cases what matters is not whether a false discovery is made or not but the expected proportion of false discoveries among all the discoveries made, i.e. the so-called false discovery rate (FDR). We present an algorithm aiming at controlling the FDR of edges when learning Gaussian graphical models (GGMs). The algorithm is particularly suitable when dealing with more nodes than samples, e.g. when learning GGMs of gene networks from gene expression data. We illustrate this on the Rosetta compendium [8].

Place, publisher, year, edition, pages
Berlin: Springer Verlag , 2008. 165-176 p.
Series
Lecture Notes in Computer Science, ISSN 0302-9743 (print), 1611-3349 (online) ; 4973
National Category
Computer Science
Identifiers
URN: urn:nbn:se:liu:diva-43596DOI: 10.1007/978-3-540-78757-0_15Local ID: 74353ISBN: 978-3-540-78756-3 (print)ISBN: 978-3-540-78757-0 (print)OAI: oai:DiVA.org:liu-43596DiVA: diva2:264456
Conference
The 6th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, EvoBIO 2008
Available from: 2009-10-10 Created: 2009-10-10 Last updated: 2014-08-19

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Publisher's full texthttp://www.ida.liu.se/~jospe/mnfdrevobio2008b.pdf

Authority records BETA

Peña, Jose M.

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • 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
  • html
  • text
  • asciidoc
  • rtf