Transcriptional Gene Regulatory Network Reconstruction Through Cross Platform Gene Network Fusion

  • Muhammad Shoaib B. Sehgal
  • Iqbal Gondal
  • Laurence Dooley
  • Ross Coppel
  • Goh Kiah Mok
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4774)


Microarray gene expression data is used to model differential activity in Gene Regulatory Networks (GRN) to elucidate complex cellular processes, though network modeling is susceptible to errors due to both noisy nature of gene expression data and platform bias. This intuitively provided the motivation for the development of an innovative technique, which effectively integrates GRN using cross-platform data to minimize the two aforementioned effects. This paper presents a GRN integration (GeNi) framework that fuses cross-platform GRN to remove platform and experimental bias using the Dempster Shafer Theory of Evidence. The proposed model estimates gene co-regulation strength by using mutual information and removes spurious co-regulations through data processing inequality. The method automatically adapts to the data distribution using Belief theory, which does not require a preset threshold to accept co-regulated links. GeNi is applied to identify common cancer-related regulatory links in ten different datasets generated by different microarray platforms including cDNA and Affymetrix arrays. Experimental results demonstrate that GeNi can be effectively applied for GRN reconstruction and cross-platform gene network fusion for any gene expression data.


Mutual Information Gene Regulatory Network Microarray Gene Expression Data Cross Platform Belief Theory 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Muhammad Shoaib B. Sehgal
    • 1
    • 3
  • Iqbal Gondal
    • 1
    • 3
  • Laurence Dooley
    • 1
  • Ross Coppel
    • 2
    • 3
  • Goh Kiah Mok
    • 4
  1. 1.Faculty of IT, Monash University, Churchill VIC. 3842Australia
  2. 2.Department of Microbiology, Monash University Clayton VIC. 3800Australia
  3. 3.Victorian Bioinformatics Consortium Wellington Road, Clayton VIC., 3800Australia
  4. 4.SIMTech Institute of Technology, Nanyang DriveSingapore

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