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Inferring Gene Networks using Robust Statistical Techniques

Author

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  • Nadadoor Venkat R.
  • Ben-Zvi Amos
  • Shah Sirish L.

Abstract

Inference of gene networks is an important step in understanding cellular dynamics. In this work, a novel algorithm is proposed for inferring gene networks from gene expression data using linear ordinary differential equations. Under the proposed method, a combination of known statistical tools including partial least squares (PLS), leave-one-out jackknifing, and the Akaike information criterion (AIC) are used for robust estimation of gene connectivity matrix. The proposed approach is tested and validated using a computer simulated gene network model and an experimental data on a nine gene network in Eschericia coli.

Suggested Citation

  • Nadadoor Venkat R. & Ben-Zvi Amos & Shah Sirish L., 2011. "Inferring Gene Networks using Robust Statistical Techniques," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-30, May.
    • Handle: RePEc:bpj:sagmbi:v:10:y:2011:i:1:n:25
    DOI: 10.2202/1544-6115.1658
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    References listed on IDEAS

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