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Transcriptional Network Inference from Functional Similarity and Expression Data: A Global Supervised Approach

Listed author(s):
  • Ambroise Jérôme

    (Université Catholique de Louvain)

  • Robert Annie

    (Université Catholique de Louvain)

  • Macq Benoit

    (Université Catholique de Louvain)

  • Gala Jean-Luc

    (Université Catholique de Louvain)

Registered author(s):

    An important challenge in system biology is the inference of biological networks from postgenomic data. Among these biological networks, a gene transcriptional regulatory network focuses on interactions existing between transcription factors (TFs) and and their corresponding target genes. A large number of reverse engineering algorithms were proposed to infer such networks from gene expression profiles, but most current methods have relatively low predictive performances. In this paper, we introduce the novel TNIFSED method (Transcriptional Network Inference from Functional Similarity and Expression Data), that infers a transcriptional network from the integration of correlations and partial correlations of gene expression profiles and gene functional similarities through a supervised classifier. In the current work, TNIFSED was applied to predict the transcriptional network in Escherichia coli and in Saccharomyces cerevisiae, using datasets of 445 and 170 affymetrix arrays, respectively. Using the area under the curve of the receiver operating characteristics and the F-measure as indicators, we showed the predictive performance of TNIFSED to be better than unsupervised state-of-the-art methods. TNIFSED performed slightly worse than the supervised SIRENE algorithm for the target genes identification of the TF having a wide range of yet identified target genes but better for TF having only few identified target genes. Our results indicate that TNIFSED is complementary to the SIRENE algorithm, and particularly suitable to discover target genes of "orphan" TFs.

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    Article provided by De Gruyter in its journal Statistical Applications in Genetics and Molecular Biology.

    Volume (Year): 11 (2012)
    Issue (Month): 1 (January)
    Pages: 1-24

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    Handle: RePEc:bpj:sagmbi:v:11:y:2012:i:1:n:2
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    1. Peng, Jie & Wang, Pei & Zhou, Nengfeng & Zhu, Ji, 2009. "Partial Correlation Estimation by Joint Sparse Regression Models," Journal of the American Statistical Association, American Statistical Association, vol. 104(486), pages 735-746.
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