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Learning Gaussian Graphical Models of Gene Networks with False Discovery Rate Control

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Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics (EvoBIO 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4973))

Abstract

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].

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Authors and Affiliations

  1. IFM, Linköping University, SE-58183, Linköping, Sweden

    Jose M. Peña

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  1. Jose M. Peña
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Elena Marchiori Jason H. Moore

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Peña, J.M. (2008). Learning Gaussian Graphical Models of Gene Networks with False Discovery Rate Control. In: Marchiori, E., Moore, J.H. (eds) Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. EvoBIO 2008. Lecture Notes in Computer Science, vol 4973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78757-0_15

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  • DOI: https://doi.org/10.1007/978-3-540-78757-0_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-78756-3

  • Online ISBN: 978-3-540-78757-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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