ArticleGene regulation and systems biology2010
Inference of cancer-specific gene regulatory networks using soft computing rules.
Article in Gene regulation and systems biology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed, 20 citations in OpenAlex.
- A disease similarity matrix based on the uniqueness of shared genes.BMC medical genomics · 2017Article
- Integrative approach for inference of gene regulatory networks using lasso-based random featuring and application to psychiatric disorders.BMC medical genomics · 2016Article
- Time-Delayed Models of Gene Regulatory Networks.Computational and mathematical methods in medicine · 2015Review
- Quantifying the underlying landscape and paths of cancer.Journal of the Royal Society, Interface · 2014Article
- Large differences in global transcriptional regulatory programs of normal and tumor colon cells.BMC cancer · 2014Article
- Rough set soft computing cancer classification and network: one stone, two birds.Cancer informatics · 2010Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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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