ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2019
Prediction of Driver Modules via Balancing Exclusive Coverages of Mutations in Cancer Samples.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Seesaw signatures capture trajectory-like transcriptomic shifts and enable compact tumour cell classification across cancers.Communications biology · 2026Article
- Identifying driver modules based on multi-omics biological networks in prostate cancer.IET systems biology · 2022Article
- Identification of mRNA Signature for Predicting Prognosis Risk of Rectal Adenocarcinoma.Frontiers in genetics · 2022Article
- Identification of Common Driver Gene Modules and Associations between Cancers through Integrated Network Analysis.Global challenges (Hoboken, NJ) · 2021Article
- Fast mutual exclusivity algorithm nominates potential synthetic lethal gene pairs through brute force matrix product computations.Computational and structural biotechnology journal · 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
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Mutual exclusivity of cancer driving mutations is a frequently observed phenomenon in the mutational landscape of cancer. The long tail of rare mutations complicates the discovery of mutually exclusive driver modules. The existing methods usually suffer from the problem that only few genes in some identified modules cover most of the cancer samples. To overcome this hurdle, an efficient method UniCovEx is presented via identifying mutually exclusive driver modules of balanced exclusive coverages. UniCovEx first searches for candidate driver modules with a strong topological relationship in signaling networks using a greedy strategy. It then evaluates the candidate modules by considering their coverage, exclusivity, and balance of coverage, using a novel metric termed exclusive entropy of modules, which measures how balanced the modules are. Finally, UniCovEx predicts sample-specific driver modules by solving a minimum set cover problem using a greedy strategy. When tested on 12 The Cancer Genome Atlas datasets of different cancer types, UniCovEx shows a significant superiority over the previous methods. The software 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.