ArticlePLoS computational biology2013
Interpretation of genomic variants using a unified biological network approach.
Article in PLoS computational biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 106 papers.
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Who cites it
106 citing papers in PubMed, 196 citations in OpenAlex.
- Disentangling Heterogeneous Molecular Networks for Multi-Omics-Driven Cancer Driver Discovery.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- TGBWDriver: A Cancer Driver Gene Identification Method Based on GraphSAGE and Bidirectional Weighted Feature Aggregation.International journal of molecular sciences · 2026Article
- Graph designs for deep learning-based multi-omics integration.Briefings in bioinformatics · 2026Review
- Discovering proteo-transcriptomic networks via biologically informed heterogeneous graph learning.Nucleic acids research · 2026Article
- Expanding the utility of variant effect predictions with phenotype-specific models.Nature communications · 2025Article
- Deep graph convolutional network-based multi-omics integration for cancer driver gene identification.Briefings in bioinformatics · 2025Article
- Identifying potential risk genes for clear cell renal cell carcinoma with deep reinforcement learning.Nature communications · 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
- Towards simplified graph neural networks for identifying cancer driver genes in heterophilic networks.Briefings in bioinformatics · 2024Article
- Tabular deep learning: a comparative study applied to multi-task genome-wide prediction.BMC bioinformatics · 2024Article
- Proteomic and phosphoproteomic landscape of localized prostate cancer unveils distinct molecular subtypes and insights into precision therapeutics.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- A feature extraction framework for discovering pan-cancer driver genes based on multi-omics data.Quantitative biology (Beijing, China) · 2024Article
- Detection of autism spectrum disorder-related pathogenic trio variants by a novel structure-based approach.Molecular autism · 2024Article
- Rare variants in pharmacogenes influence clozapine metabolism in individuals with schizophrenia.European neuropsychopharmacology : the journal of the European College of Neuropsychopharmacology · 2024Article
- Genome-wide prediction of pathogenic gain- and loss-of-function variants from ensemble learning of a diverse feature set.Genome medicine · 2023Article
- Explainable Multilayer Graph Neural Network for cancer gene prediction.Bioinformatics (Oxford, England) · 2023Article
- SMG: self-supervised masked graph learning for cancer gene identification.Briefings in bioinformatics · 2023Article
- SUPREME: multiomics data integration using graph convolutional networks.NAR genomics and bioinformatics · 2023Article
- Unraveling patient heterogeneity in complex diseases through individualized co-expression networks: a perspective.Frontiers in genetics · 2023Article
- Comprehensive characterization of posttranscriptional impairment-related 3'-UTR mutations in 2413 whole genomes of cancer patients.NPJ genomic medicine · 2022Article
46 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The decreasing cost of sequencing is leading to a growing repertoire of personal genomes. However, we are lagging behind in understanding the functional consequences of the millions of variants obtained from sequencing. Global system-wide effects of variants in coding genes are particularly poorly understood. It is known that while variants in some genes can lead to diseases, complete disruption of other genes, called 'loss-of-function tolerant', is possible with no obvious effect. Here, we build a systems-based classifier to quantitatively estimate the global perturbation caused by deleterious mutations in each gene. We first survey the degree to which gene centrality in various individual networks and a unified 'Multinet' correlates with the tolerance to loss-of-function mutations and evolutionary conservation. We find that functionally significant and highly conserved genes tend to be more central in physical protein-protein and regulatory networks. However, this is not the case for metabolic pathways, where the highly central genes have more duplicated copies and are more tolerant to loss-of-function mutations. Integration of three-dimensional protein structures reveals that the correlation with centrality in the protein-protein interaction network is also seen in terms of the number of interaction interfaces used. Finally, combining all the network and evolutionary properties allows us to build a classifier distinguishing functionally essential and loss-of-function tolerant genes with higher accuracy (AUC = 0.91) than any individual property. Application of the classifier to the whole genome shows its strong potential for interpretation of variants involved in mendelian diseases and in complex disorders probed by genome-wide association studies.
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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.