ArticleBMC bioinformatics2019
A random walk-based method to identify driver genes by integrating the subcellular localization and variation frequency into bipartite graph.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- TGBWDriver: A Cancer Driver Gene Identification Method Based on GraphSAGE and Bidirectional Weighted Feature Aggregation.International journal of molecular sciences · 2026Article
- DriverSub-SVM: a machine learning approach for cancer subtype classification by integrating patient-specific and global driver genes.BMC bioinformatics · 2025Article
- MNMO: discover driver genes from a multi-omics data based-multi-layer network.Bioinformatics (Oxford, England) · 2025Article
- Interpretable identification of cancer genes across biological networks via transformer-powered graph representation learning.Nature biomedical engineering · 2025Article
- Enhancing Molecular Network-Based Cancer Driver Gene Prediction Using Machine Learning Approaches: Current Challenges and Opportunities.Journal of cellular and molecular medicine · 2025Review
- Identification of cancer driver genes based on hierarchical weak consensus model.Health information science and systems · 2024Article
- Prediction of cancer driver genes and mutations: the potential of integrative computational frameworks.Briefings in bioinformatics · 2024Review
- Exploring gene-patient association to identify personalized cancer driver genes by linear neighborhood propagation.BMC bioinformatics · 2024Article
- Network analysis of driver genes in human cancers.Frontiers in bioinformatics · 2024Article
- SMG: self-supervised masked graph learning for cancer gene identification.Briefings in bioinformatics · 2023Article
- Identifying driver pathways based on a parameter-free model and a partheno-genetic algorithm.BMC bioinformatics · 2023Article
- Time-resolved microfluidics unravels individual cellular fates during double-strand break repair.BMC biology · 2022Article
- A novel candidate disease gene prioritization method using deep graph convolutional networks and semi-supervised learning.BMC bioinformatics · 2022Article
- Prioritization of cancer driver gene with prize-collecting steiner tree by introducing an edge weighted strategy in the personalized gene interaction network.BMC bioinformatics · 2022Article
- DriverRWH: discovering cancer driver genes by random walk on a gene mutation hypergraph.BMC bioinformatics · 2022Article
- Driver gene detection through Bayesian network integration of mutation and expression profiles.Bioinformatics (Oxford, England) · 2022Article
- Subcellular Localization Prediction of Human Proteins Using Multifeature Selection Methods.BioMed research international · 2022Article
- Tumor relevant protein functional interactions identified using bipartite graph analyses.Scientific reports · 2021Article
- Ranking cancer drivers via betweenness-based outlier detection and random walks.BMC bioinformatics · 2021Article
- A new precision medicine initiative at the dawn of exascale computing.Signal transduction and targeted therapy · 2021Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
backgroundCancer as a worldwide problem is driven by genomic alterations. With the advent of high-throughput sequencing technology, a huge amount of genomic data generates at every second which offer many valuable cancer information and meanwhile throw a big challenge to those investigators. As the major characteristic of cancer is heterogeneity and most of alterations are supposed to be useless passenger mutations that make no contribution to the cancer progress. Hence, how to dig out driver genes that have effect on a selective growth advantage in tumor cells from those tremendously and noisily data is still an urgent task.
resultsConsidering previous network-based method ignoring some important biological properties of driver genes and the low reliability of gene interactive network, we proposed a random walk method named as Subdyquency that integrates the information of subcellular localization, variation frequency and its interaction with other dysregulated genes to improve the prediction accuracy of driver genes. We applied our model to three different cancers: lung, prostate and breast cancer. The results show our model can not only identify the well-known important driver genes but also prioritize the rare unknown driver genes. Besides, compared with other existing methods, our method can improve the precision, recall and fscore to a higher level for most of cancer types.
conclusionsThe final results imply that driver genes are those prone to have higher variation frequency and impact more dysregulated genes in the common significant compartment. AVAILABILITY: The source code can be obtained at https://github.com/weiba/Subdyquency .
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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 Socratic graph.