ArticleBMC bioinformatics2013
Detecting recurrent gene mutation in interaction network context using multi-scale graph diffusion.
Article in BMC bioinformatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 36 citations in OpenAlex.
- Article
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- Sex-Biased Molecular Signature for Overall Survival of Liver Cancer Patients.Biomolecules & therapeutics · 2020Review
- uKIN Combines New and Prior Information with Guided Network Propagation to Accurately Identify Disease Genes.Cell systems · 2020Article
- A census of pathway maps in cancer systems biology.Nature reviews. Cancer · 2020Review
- An Effective Graph Clustering Method to Identify Cancer Driver Modules.Frontiers in bioengineering and biotechnology · 2020Article
- Significant random signatures reveals new biomarker for breast cancer.BMC medical genomics · 2019Article
- The Emerging Potential for Network Analysis to Inform Precision Cancer Medicine.Journal of molecular biology · 2018Review
- MaxMIF: A New Method for Identifying Cancer Driver Genes through Effective Data Integration.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2018Article
- Discovering mutated driver genes through a robust and sparse co-regularized matrix factorization framework with prior information from mRNA expression patterns and interaction network.BMC bioinformatics · 2018Article
- The cJUN NHeLife · 2018Article
- Network-Based Coverage of Mutational Profiles Reveals Cancer Genes.Cell systems · 2017Article
- A novel network regularized matrix decomposition method to detect mutated cancer genes in tumour samples with inter-patient heterogeneity.Scientific reports · 2017Article
- NetNorM: Capturing cancer-relevant information in somatic exome mutation data with gene networks for cancer stratification and prognosis.PLoS computational biology · 2017Article
- Network perturbation by recurrent regulatory variants in cancer.PLoS computational biology · 2017Article
- Computational approaches for the identification of cancer genes and pathways.Wiley interdisciplinary reviews. Systems biology and medicine · 2017Review
- MUFFINN: cancer gene discovery via network analysis of somatic mutation data.Genome biology · 2016Article
- Network-Based Analysis of eQTL Data to Prioritize Driver Mutations.Genome biology and evolution · 2016Article
- Maximum flow approach to prioritize potential drug targets of Mycobacterium tuberculosis H37Rv from protein-protein interaction network.Clinical and translational medicine · 2015Article
- Hi-C Chromatin Interaction Networks Predict Co-expression in the Mouse Cortex.PLoS computational biology · 2015Article
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDelineating the molecular drivers of cancer, i.e. determining cancer genes and the pathways which they deregulate, is an important challenge in cancer research. In this study, we aim to identify pathways of frequently mutated genes by exploiting their network neighborhood encoded in the protein-protein interaction network. To this end, we introduce a multi-scale diffusion kernel and apply it to a large collection of murine retroviral insertional mutagenesis data. The diffusion strength plays the role of scale parameter, determining the size of the network neighborhood that is taken into account. As a result, in addition to detecting genes with frequent mutations in their genomic vicinity, we find genes that harbor frequent mutations in their interaction network context.
resultsWe identify densely connected components of known and putatively novel cancer genes and demonstrate that they are strongly enriched for cancer related pathways across the diffusion scales. Moreover, the mutations in the clusters exhibit a significant pattern of mutual exclusion, supporting the conjecture that such genes are functionally linked. Using multi-scale diffusion kernel, various infrequently mutated genes are found to harbor significant numbers of mutations in their interaction network neighborhood. Many of them are well-known cancer genes.
conclusionsThe results demonstrate the importance of defining recurrent mutations while taking into account the interaction network context. Importantly, the putative cancer genes and networks detected in this study are found to be significant at different diffusion scales, confirming the necessity of a multi-scale analysis.
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