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Second order optimization for the inference of gene regulatory pathways

Author

Listed:
  • Das Mouli

    (Machine Intelligence Unit (MIU), Indian Statistical Institute (I.S.I), 203, B. T. Road, Kolkata 700 108, India)

  • Murthy Chivukula A.

    (Machine Intelligence Unit (MIU), Indian Statistical Institute (I.S.I), 203, B. T. Road, Kolkata 700 108, India)

  • De Rajat K.

    (Machine Intelligence Unit (MIU), Indian Statistical Institute (I.S.I), 203, B. T. Road, Kolkata 700 108, India)

Abstract

With the increasing availability of experimental data on gene interactions, modeling of gene regulatory pathways has gained special attention. Gradient descent algorithms have been widely used for regression and classification applications. Unfortunately, results obtained after training a model by gradient descent are often highly variable. In this paper, we present a new second order learning rule based on the Newton’s method for inferring optimal gene regulatory pathways. Unlike the gradient descent method, the proposed optimization rule is independent of the learning parameter. The flow vectors are estimated based on biomass conservation. A set of constraints is formulated incorporating weighting coefficients. The method calculates the maximal expression of the target gene starting from a given initial gene through these weighting coefficients. Our algorithm has been benchmarked and validated on certain types of functions and on some gene regulatory networks, gathered from literature. The proposed method has been found to perform better than the gradient descent learning. Extensive performance comparison with the extreme pathway analysis method has underlined the effectiveness of our proposed methodology.

Suggested Citation

  • Das Mouli & Murthy Chivukula A. & De Rajat K., 2014. "Second order optimization for the inference of gene regulatory pathways," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 13(1), pages 19-33, February.
    • Handle: RePEc:bpj:sagmbi:v:13:y:2014:i:1:p:19-33:n:2
    DOI: 10.1515/sagmb-2012-0021
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    References listed on IDEAS

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    1. Ferhat Ay & Fei Xu & Tamer Kahveci, 2009. "Scalable Steady State Analysis of Boolean Biological Regulatory Networks," PLOS ONE, Public Library of Science, vol. 4(12), pages 1-9, December.
    2. Christian L Barrett & Bernhard O Palsson, 2006. "Iterative Reconstruction of Transcriptional Regulatory Networks: An Algorithmic Approach," PLOS Computational Biology, Public Library of Science, vol. 2(5), pages 1-10, May.
    3. Tian Hong & Jianhua Xing & Liwu Li & John J Tyson, 2011. "A Mathematical Model for the Reciprocal Differentiation of T Helper 17 Cells and Induced Regulatory T Cells," PLOS Computational Biology, Public Library of Science, vol. 7(7), pages 1-13, July.
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