Transcriptional gene regulatory network reconstruction through cross platform gene network fusion

  • Authors:

  • Muhammad Shoaib B. Sehgal;Iqbal Gondal;Laurence Dooley;Ross Coppel;Goh Kiah Mok

  • Affiliations:

  • Faculty of IT, Monash University, Churchill, VIC, Australia and Victorian Bioinformatics Consortium, Clayton, VIC, Australia;Faculty of IT, Monash University, Churchill, VIC, Australia and Victorian Bioinformatics Consortium, Clayton, VIC, Australia;Faculty of IT, Monash University, Churchill, VIC, Australia;Department of Microbiology, Monash University, Clayton, VIC, Australia and Victorian Bioinformatics Consortium, Clayton, VIC, Australia;SIMTech Institute of Technology, Singapore

  • Venue:
  • PRIB'07 Proceedings of the 2nd IAPR international conference on Pattern recognition in bioinformatics
  • Year:
  • 2007

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Abstract

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 coregulation strength by using mutual information and removes spurious coregulations 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.