ArticleCell systems2017
Network-Based Coverage of Mutational Profiles Reveals Cancer Genes.
Article in Cell systems, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
24 citing papers in PubMed, 52 citations in OpenAlex.
- Tumor ecosystem subtyping of breast cancer based on somatic mutations and network propagation reveals distinct prognostic and genomic landscapes.Frontiers in genetics · 2026Article
- Identifying potential risk genes for clear cell renal cell carcinoma with deep reinforcement learning.Nature communications · 2025Article
- Proteogenomic characterization of skull-base chordoma.Nature communications · 2024Article
- Explainable Multilayer Graph Neural Network for cancer gene prediction.Bioinformatics (Oxford, England) · 2023Article
- Network-based approaches for modeling disease regulation and progression.Computational and structural biotechnology journal · 2023Review
- A new machine learning method for cancer mutation analysis.PLoS computational biology · 2022Article
- Functional stratification of cancer drugs through integrated network similarity.NPJ systems biology and applications · 2022Article
- Comprehensive evaluation of computational methods for predicting cancer driver genes.Briefings in bioinformatics · 2022Article
- A computational approach for the discovery of significant cancer genes by weighted mutation and asymmetric spreading strength in networks.Scientific reports · 2021Article
- Network propagation-based prioritization of long tail genes in 17 cancer types.Genome biology · 2021Article
- Efficient representations of tumor diversity with paired DNA-RNA aberrations.PLoS computational biology · 2021Article
- Identifying Drug Sensitivity Subnetworks with NETPHIX.iScience · 2020Article
- Identification of conserved evolutionary trajectories in tumors.Bioinformatics (Oxford, England) · 2020Article
- uKIN Combines New and Prior Information with Guided Network Propagation to Accurately Identify Disease Genes.Cell systems · 2020Article
- A protein-centric approach for exome variant aggregation enables sensitive association analysis with clinical outcomes.Human mutation · 2020Article
- Integrative analysis of highly mutated genes in hepatitis B virus-related hepatic carcinoma.Cancer medicine · 2020Article
- NExUS: Bayesian simultaneous network estimation across unequal sample sizes.Bioinformatics (Oxford, England) · 2020Article
- Prioritizing Cancer Genes Based on an Improved Random Walk Method.Frontiers in genetics · 2020Article
- 3D spatial organization and network-guided comparison of mutation profiles in Glioblastoma reveals similarities across patients.PLoS computational biology · 2019Article
- Combinatorial Detection of Conserved Alteration Patterns for Identifying Cancer Subnetworks.GigaScience · 2019Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
A central goal in cancer genomics is to identify the somatic alterations that underpin tumor initiation and progression. While commonly mutated cancer genes are readily identifiable, those that are rarely mutated across samples are difficult to distinguish from the large numbers of other infrequently mutated genes. We introduce a method, nCOP, that considers per-individual mutational profiles within the context of protein-protein interaction networks in order to identify small connected subnetworks of genes that, while not individually frequently mutated, comprise pathways that are altered across (i.e., "cover") a large fraction of individuals. By analyzing 6,038 samples across 24 different cancer types, we demonstrate that nCOP is highly effective in identifying cancer genes, including those with low mutation frequencies. Overall, our work demonstrates that combining per-individual mutational information with interaction networks is a powerful approach for tackling the mutational heterogeneity observed across cancers.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.