ArticleOncotarget2017
Identification of driver modules in pan-cancer via coordinating coverage and exclusivity.
Article in Oncotarget, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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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.
The trial behind it
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
10 citing papers in PubMed.
- Seesaw signatures capture trajectory-like transcriptomic shifts and enable compact tumour cell classification across cancers.Communications biology · 2026Article
- MODCAN: driver gene identification based on multi-omics features and differential co-association networks for tumor subtypes.BMC bioinformatics · 2026Article
- MaxCLK: discovery of cancer driver genes via maximal clique and information entropy of modules.Bioinformatics (Oxford, England) · 2023Article
- DriverRWH: discovering cancer driver genes by random walk on a gene mutation hypergraph.BMC bioinformatics · 2022Article
- Identification of Common Driver Gene Modules and Associations between Cancers through Integrated Network Analysis.Global challenges (Hoboken, NJ) · 2021Article
- Identifying modules of cooperating cancer drivers.Molecular systems biology · 2021Article
- FI-Net: Identification of Cancer Driver Genes by Using Functional Impact Prediction Neural Network.Frontiers in genetics · 2020Article
- Prediction of Driver Modules via Balancing Exclusive Coverages of Mutations in Cancer Samples.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2019Article
- Identifying cancer prognostic modules by module network analysis.BMC bioinformatics · 2019Article
- MaxMIF: A New Method for Identifying Cancer Driver Genes through Effective Data Integration.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2018Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
It is widely accepted that cancer is driven by accumulated somatic mutations during the lifetime of an individual. Cancer mutations may target relatively small number of cell functional modules. The heterogeneity in different cancer patients makes it difficult to identify driver mutations or functional modules related to cancer. It is biologically desired to be capable of identifying cancer pathway modules through coordination between coverage and exclusivity. There have been a few approaches developed for this purpose, but they all have limitations in practice due to their computational complexity and prediction accuracy. We present a network based approach, CovEx, to predict the specific patient oriented modules by 1) discovering candidate modules for each considered gene, 2) extracting significant candidates by harmonizing coverage and exclusivity and, 3) further selecting the patient oriented modules based on a set cover model. Applying CovEx to pan-cancer datasets spanning 12 cancer types collecting from public database TCGA, it demonstrates significant superiority over the current leading competitors in performance. It is published under GNU GENERAL PUBLIC LICENSE and the source code is available at: https://sourceforge.net/projects/cancer-pathway/files/.
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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.