ArticleBioinformatics (Oxford, England)2016
A new correlation clustering method for cancer mutation analysis.
Article in Bioinformatics (Oxford, England), 2016. 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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Who cites it
6 citing papers in PubMed.
- Network-based clustering unveils interconnected landscapes of genomic and clinical features across myeloid malignancies.Nature communications · 2025Article
- Genomic clustering analysis identifies molecular subtypes of thymic epithelial tumors independent of World Health Organization histologic type.Oncotarget · 2021Article
- Analysis, identification and visualization of subgroups in genomics.Briefings in bioinformatics · 2021Review
- Identifying modules of cooperating cancer drivers.Molecular systems biology · 2021Article
- An Effective Graph Clustering Method to Identify Cancer Driver Modules.Frontiers in bioengineering and biotechnology · 2020Article
- BeWith: A Between-Within method to discover relationships between cancer modules via integrated analysis of mutual exclusivity, co-occurrence and functional interactions.PLoS computational biology · 2017Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
motivationCancer genomes exhibit a large number of different alterations that affect many genes in a diverse manner. An improved understanding of the generative mechanisms behind the mutation rules and their influence on gene community behavior is of great importance for the study of cancer.
resultsTo expand our capability to analyze combinatorial patterns of cancer alterations, we developed a rigorous methodology for cancer mutation pattern discovery based on a new, constrained form of correlation clustering. Our new algorithm, named C AVAILABILITY AND IMPLEMENTATION: The source code for the C
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Registered trials
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