ArticlePloS one2012
Exploiting protein-protein interaction networks for genome-wide disease-gene prioritization.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers, 1 of them a synthesis that pooled it.
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Who cites it
56 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
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- Multi-Modal Topology-Aware Graph Neural Network for Robust Chemical-Protein Interaction Prediction.International journal of molecular sciences · 2025Article
- TCoCPIn reveals topological characteristics of chemical protein interaction networks for novel feature discovery.Scientific reports · 2025Article
- PRODE recovers essential and context-essential genes through neighborhood-informed scores.Genome biology · 2025Article
- Article
- Predicting the pro-longevity or anti-longevity effect of model organism genes with enhanced Gaussian noise augmentation-based contrastive learning on protein-protein interaction networks.NAR genomics and bioinformatics · 2024Article
- KDGene: knowledge graph completion for disease gene prediction using interactional tensor decomposition.Briefings in bioinformatics · 2024Article
- XGDAG: explainable gene-disease associations via graph neural networks.Bioinformatics (Oxford, England) · 2023Article
- NIAPU: network-informed adaptive positive-unlabeled learning for disease gene identification.Bioinformatics (Oxford, England) · 2023Article
- Assessing network-based methods in the context of system toxicology.Frontiers in pharmacology · 2023Article
- Identification of risk genes for Alzheimer's disease by gene embedding.Cell genomics · 2022Article
- Network-based machine learning approach to predict immunotherapy response in cancer patients.Nature communications · 2022Article
- WINNER: A network biology tool for biomolecular characterization and prioritization.Frontiers in big data · 2022Article
- Prediction of Adverse Drug Reaction Linked to Protein Targets Using Network-Based Information and Machine Learning.Frontiers in bioinformatics · 2022Article
- Disease gene prediction with privileged information and heteroscedastic dropout.Bioinformatics (Oxford, England) · 2021Article
- A network-based machine-learning framework to identify both functional modules and disease genes.Human genetics · 2021Article
- An ensemble learning approach for modeling the systems biology of drug-induced injury.Biology direct · 2021Article
- Robustness and lethality in multilayer biological molecular networks.Nature communications · 2020Article
- GRP94 promotes brain metastasis by engaging pro-survival autophagy.Neuro-oncology · 2020Article
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Authors and funding
2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Complex genetic disorders often involve products of multiple genes acting cooperatively. Hence, the pathophenotype is the outcome of the perturbations in the underlying pathways, where gene products cooperate through various mechanisms such as protein-protein interactions. Pinpointing the decisive elements of such disease pathways is still challenging. Over the last years, computational approaches exploiting interaction network topology have been successfully applied to prioritize individual genes involved in diseases. Although linkage intervals provide a list of disease-gene candidates, recent genome-wide studies demonstrate that genes not associated with any known linkage interval may also contribute to the disease phenotype. Network based prioritization methods help highlighting such associations. Still, there is a need for robust methods that capture the interplay among disease-associated genes mediated by the topology of the network. Here, we propose a genome-wide network-based prioritization framework named GUILD. This framework implements four network-based disease-gene prioritization algorithms. We analyze the performance of these algorithms in dozens of disease phenotypes. The algorithms in GUILD are compared to state-of-the-art network topology based algorithms for prioritization of genes. As a proof of principle, we investigate top-ranking genes in Alzheimer's disease (AD), diabetes and AIDS using disease-gene associations from various sources. We show that GUILD is able to significantly highlight disease-gene associations that are not used a priori. Our findings suggest that GUILD helps to identify genes implicated in the pathology of human disorders independent of the loci associated with the disorders.
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