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Paper   IPM / Biological / 13716
School of Biological Sciences
  Title:   CN: A Consensus Algorithm for Inferring Gene Regulatory Networks Using SORDER Algorithm and Conditional Mutual Information Test
  Author(s): 
1.  R. Aghdam.
2.  M. Ganjali.
3.  X. Zhang.
4.  C. Eslahchi.
  Status:   Published
  Journal: Molecular BioSystems
  Vol.:  DOI: 10.1039/C4MB00413B
  Year:  2015
  Pages:   942-949
  Supported by:  IPM
  Abstract:
Inferring gene regulatory networks (GRNs) from gene expression data is a major challenge in systems biology. Path Consistency (PC) algorithm is one of the popular methods in this field. However, as an order dependent algorithm, PC algorithm is not robust, because it achieves different network topologies if gene orders are permuted. In addition, the performance of this algorithm depends on the threshold value used for independence tests. Consequently, selecting suitable sequential ordering of nodes and an appropriate threshold value for the inputs of PC algorithm are challenges to infer a good GRN. In this work, we propose a heuristic algorithm, namely SORDER, to find a suitable sequential ordering of nodes. Based on SORDER algorithm and a suitable interval threshold for conditional mutual information (CMI) tests, a network inference method, namely Consensus Network (CN), is developed. In the proposed method, for each edge of the complete graph, a weighted value is defined. This value is considered as the reliability value of dependency between two nodes. The final inferred network, by CN algorithm, contains edges which their reliability value of dependency is more than a defined threshold. The effectiveness of this method is benchmarked through several networks from DREAM challenge and the widely used SOS DNA repair network in Escherichia coli. The results indicate that the CN algorithm is suitable for learning GRNs and it considerably improves the precision of network inference. The source of data sets and codes are available at http://bs.ipm.ir/softwares/CN. DOI: 10.1039/C4MB00413B

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