ArticleGenome biology2016
MUFFINN: cancer gene discovery via network analysis of somatic mutation data.
Article in Genome biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 86 papers.
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Who cites it
86 citing papers in PubMed, 166 citations in OpenAlex.
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- A core driver gene set identified based on geMER reveals its potential driver mechanism in pan-cancer.NPJ precision oncology · 2025Article
- Deep graph convolutional network-based multi-omics integration for cancer driver gene identification.Briefings in bioinformatics · 2025Article
- Bigpicc: a graph-based approach to identifying carcinogenic gene combinations from mutation data.BMC bioinformatics · 2025Article
- Identifying potential risk genes for clear cell renal cell carcinoma with deep reinforcement learning.Nature communications · 2025Article
- PRODE recovers essential and context-essential genes through neighborhood-informed scores.Genome biology · 2025Article
- MONet: cancer driver gene identification algorithm based on integrated analysis of multi-omics data and network models.Experimental biology and medicine (Maywood, N.J.) · 2025Article
- Exploring glioma heterogeneity through omics networks: from gene network discovery to causal insights and patient stratification.BioData mining · 2024Article
- IMI-driver: Integrating multi-level gene networks and multi-omics for cancer driver gene identification.PLoS computational biology · 2024Article
- Exploring gene-patient association to identify personalized cancer driver genes by linear neighborhood propagation.BMC bioinformatics · 2024Article
- Explainable Multilayer Graph Neural Network for cancer gene prediction.Bioinformatics (Oxford, England) · 2023Article
- Identifying Cancer Driver Pathways Based on the Mouth Brooding Fish Algorithm.Entropy (Basel, Switzerland) · 2023Article
- Computational Methods Summarizing Mutational Patterns in Cancer: Promise and Limitations for Clinical Applications.Cancers · 2023Review
- Identifying cancer driver genes based on multi-view heterogeneous graph convolutional network and self-attention mechanism.BMC bioinformatics · 2023Article
- Article
- The relapsed acute lymphoblastic leukemia network (ReALLNet): a multidisciplinary project from the spanish society of pediatric hematology and oncology (SEHOP).Frontiers in pediatrics · 2023Article
- DriverMP enables improved identification of cancer driver genes.GigaScience · 2022Article
- A new machine learning method for cancer mutation analysis.PLoS computational biology · 2022Article
- Genetic association and single-cell transcriptome analyses reveal distinct features connecting autoimmunity with cancers.iScience · 2022Article
- The impact of rare germline variants on human somatic mutation processes.Nature communications · 2022Article
26 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A major challenge for distinguishing cancer-causing driver mutations from inconsequential passenger mutations is the long-tail of infrequently mutated genes in cancer genomes. Here, we present and evaluate a method for prioritizing cancer genes accounting not only for mutations in individual genes but also in their neighbors in functional networks, MUFFINN (MUtations For Functional Impact on Network Neighbors). This pathway-centric method shows high sensitivity compared with gene-centric analyses of mutation data. Notably, only a marginal decrease in performance is observed when using 10 % of TCGA patient samples, suggesting the method may potentiate cancer genome projects with small patient populations.
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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.