ArticlePLoS computational biology2019
A novel network control model for identifying personalized driver genes in cancer.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed.
- A Novel Cancer Driver Genes Identification Method Based on Self-Supervised Dual Masked Graph Autoencoder.Interdisciplinary sciences, computational life sciences · 2026Article
- PICDGI: A framework for predicting cancer driver genes through dynamic gene-gene interaction modeling of single-cell data.PLoS computational biology · 2026Article
- CTAS: a network control theory-based approach to identify key regulatory TFs of AS events during epithelial-mesenchymal transition.Briefings in bioinformatics · 2026Article
- Controllability Analysis of Intercellular Protein-Protein Interaction Networks.Computational and structural biotechnology journal · 2026Article
- LogicSR: prior-guided symbolic regression for gene regulatory network inference from single-cell transcriptomics data.Briefings in bioinformatics · 2025Article
- TARGET-SL: precision essential gene prediction using driver prioritisation and synthetic lethality.Briefings in bioinformatics · 2025Article
- Review
- Enhancing Molecular Network-Based Cancer Driver Gene Prediction Using Machine Learning Approaches: Current Challenges and Opportunities.Journal of cellular and molecular medicine · 2025Review
- Multimodal multiobjective optimization with structural network control principles to optimize personalized drug targets for drug discovery of individual patients.Briefings in bioinformatics · 2024Article
- A practically efficient algorithm for identifying critical control proteins in directed probabilistic biological networks.NPJ systems biology and applications · 2024Article
- Target control of linear directed networks based on the path cover problem.Scientific reports · 2024Article
- Personalized Driver Gene Prediction Using Graph Convolutional Networks with Conditional Random Fields.Biology · 2024Article
- Evaluation of single-sample network inference methods for precision oncology.NPJ systems biology and applications · 2024Article
- Exploring gene-patient association to identify personalized cancer driver genes by linear neighborhood propagation.BMC bioinformatics · 2024Article
- Measuring criticality in control of complex biological networks.NPJ systems biology and applications · 2024Article
- Control Theory and Systems Biology: Potential Applications in Neurodegeneration and Search for Therapeutic Targets.International journal of molecular sciences · 2023Review
- Identifying the personalized driver gene sets maximally contributing to abnormality of transcriptome phenotype in glioblastoma multiforme individuals.Molecular oncology · 2023Article
- SWEET: a single-sample network inference method for deciphering individual features in disease.Briefings in bioinformatics · 2023Article
- WMDS.net: a network control framework for identifying key players in transcriptome programs.Bioinformatics (Oxford, England) · 2023Article
- Discovery of Drug-Responsive Phenomic Alteration-Related Driver Genes in the Treatment of Coronary Heart Disease.Pharmacogenomics and personalized medicine · 2023Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Although existing computational models have identified many common driver genes, it remains challenging to identify the personalized driver genes by using samples of an individual patient. Recently, the methods of exploiting the structure-based control principles of complex networks provide new clues for identifying minimum number of driver nodes to drive the state transition of large-scale complex networks from an initial state to the desired state. However, the structure-based network control methods cannot be directly applied to identify the personalized driver genes due to the unknown network dynamics of the personalized system. Here we proposed the personalized network control model (PNC) to identify the personalized driver genes by employing the structure-based network control principle on genetic data of individual patients. In PNC model, we firstly presented a paired single sample network construction method to construct the personalized state transition network for capturing the phenotype transitions between healthy and disease states. Then, we designed a novel structure-based network control method from the Feedback Vertex Sets-based control perspective to identify the personalized driver genes. The wide experimental results on 13 cancer datasets from The Cancer Genome Atlas firstly showed that PNC model outperforms current state-of-the-art methods, in terms of F-measures for identifying cancer driver genes enriched in the gold-standard cancer driver gene lists. Furthermore, these results showed that personalized driver genes can be explored by their network characteristics even when they are hidden factors in transcription and mutation profiles. Our PNC gives novel insights and useful tools into understanding the tumor heterogeneity in cancer. The PNC package and data resources used in this work can be freely downloaded from https://github.com/NWPU-903PR/PNC.
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