A Framework for Path Analysis in Gene Regulatory Networks

  • Ramesh Ram
  • Madhu Chetty
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4774)


The inference of a network structure from microarray data providing dynamical information about the underlying Gene regulatory network is an important and still outstanding problem. Recently, a causal modeling approach was presented in our publications to recover the structure of this network. However, issues like spurious arcs and time delay were not dealt with previously. The graph-theoretical measure d-separation provides criteria to recover the network structure edge-by-edge by calculating the partial correlation. However, the estimation of partial correlations from small sample sizes is a practical problem. As our approach attempts to find networks that closely match the observed partial correlation constraints in the data, main aim to this paper is to attempt to maximize the scoring metric used. In this paper, we formulate a framework for path analysis as a post processing step after learning gene regulatory network using causal modeling. The approach is tested with both artificial and real gene regulatory network scenario and the structure recovered after post processing better fits the data.


Causal model d-separation conditional independence 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Ramesh Ram
    • 1
  • Madhu Chetty
    • 1
  1. 1.Gippsland School of IT, Monash University, Churchill, Victoria 3842Australia

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