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JOURNAL

Gene Regulation and Systems Biology

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Inference of Cancer-specific Gene Regulatory Networks Using Soft Computing Rules

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Publication Date: 31 Mar 2010

Type: Original Research

Journal: Gene Regulation and Systems Biology

Citation: Gene Regulation and Systems Biology 2010:4 19-34

doi: 10.4137/GRSB.S4509

Abstract

Perturbations of gene regulatory networks are essentially responsible for oncogenesis. Therefore, inferring the gene regulatory networks is a key step to overcoming cancer. In this work, we propose a method for inferring directed gene regulatory networks based on soft computing rules, which can identify important cause-effect regulatory relations of gene expression. First, we identify important genes associated with a specific cancer (colon cancer) using a supervised learning approach. Next, we reconstruct the gene regulatory networks by inferring the regulatory relations among the identified genes, and their regulated relations by other genes within the genome. We obtain two meaningful findings. One is that upregulated genes are regulated by more genes than downregulated ones, while downregulated genes regulate more genes than upregulated ones. The other one is that tumor suppressors suppress tumor activators and activate other tumor suppressors strongly, while tumor activators activate other tumor activators and suppress tumor suppressors weakly, indicating the robustness of biological systems. These findings provide valuable insights into the pathogenesis of cancer.


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What Your Colleagues Say About Gene Regulation and Systems Biology
Publishing in Gene Regulation and Systems Biology was a very positive experience.  I was impressed by the fast and uncomplicated submission process as well as the clear and professional peer review process which helped to improve the manuscript.  The Libertas Academica team was very patient and helpful.  I would definitely recommend the journal to other colleagues in the field!
Dr Cameron Moshfegh (Federal Institute of Technology, Switzerland)
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