ArticleNature computational science2021
An efficient and effective method to identify significantly perturbed subnetworks in cancer.
Article in Nature computational science, 2021. 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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Who cites it
10 citing papers in PubMed, 23 citations in OpenAlex.
- Identifying multigenic modules under selection in the tumor genome.Bioinformatics (Oxford, England) · 2026Article
- Article
- Evaluation and Aggregation of Active Module Identification Algorithms.bioRxiv : the preprint server for biology · 2025Article
- A comprehensive benchmark study of methods for identifying significantly perturbed subnetworks in cancer.Briefings in bioinformatics · 2024Article
- Network modeling links kidney developmental programs and the cancer type-specificity of VHL mutations.NPJ systems biology and applications · 2024Article
- Prostate Cancer Progression Modeling Provides Insight into Dynamic Molecular Changes Associated with Progressive Disease States.Cancer research communications · 2024Article
- Identifying Significantly Perturbed Subnetworks in Cancer Using Multiple Protein-Protein Interaction Networks.Cancers · 2023Article
- Surrogate "Level-Based" Lagrangian Relaxation for mixed-integer linear programming.Scientific reports · 2022Article
- A computational approach for the discovery of significant cancer genes by weighted mutation and asymmetric spreading strength in networks.Scientific reports · 2021Article
- Network propagation-based prioritization of long tail genes in 17 cancer types.Genome biology · 2021Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
The identification of key functional biological networks from high-dimensional genomics data is pivotal for cancer research. Here, we introduce FDRnet, a method for the detection of molecular subnetworks in cancer, which addresses several challenges in pathway analysis. FDRnet detects key subnetworks by solving a mixed-integer linear programming problem, using a given upper bound of false discovery rate (FDR) as a budget constraint, and minimizing a conductance score to find dense subgraphs around seed genes. A large-scale benchmark study was performed on both simulation and cancer genomics data. FDRnet outperformed other methods in the ability to detect functionally homogeneous subnetworks in a scale-free biological network, to control FDRs of the genes in detected subnetworks, to improve computational efficiency and to integrate multi-omics data. By overcoming the limitations of existing approaches, FDRnet can facilitate the detection of key functional pathways in cancer and other genetic diseases.
Identifiers
What OpenQuestion holds
Registered trials
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