ArticlePLoS computational biology2012
Enhancing the prioritization of disease-causing genes through tissue specific protein interaction networks.
Article in PLoS computational biology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 92 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
92 citing papers in PubMed, 169 citations in OpenAlex.
- Strategies for constructing context-specific protein-protein interaction networks.Briefings in bioinformatics · 2026Review
- In vivo mapping of protein-protein interactions of schizophrenia risk factors generates an interconnected disease network.Schizophrenia (Heidelberg, Germany) · 2026Article
- Assessing the repurposing potential of disease-modifying antirheumatic drug targets to reduce Alzheimer's disease risk: a Mendelian randomization study.Brain, behavior, & immunity - health · 2026Article
- CLinNET: An Interpretable and Uncertainty-Aware Deep Learning Framework for Multi-Modal Clinical Genomics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Increasing pathogenic germline variant diagnosis rates in precision medicine: current best practices and future opportunities.Human genomics · 2025Review
- GETgene-AI: a framework for prioritizing actionable cancer drug targets.Frontiers in systems biology · 2025Article
- Neurogenetic underpinnings of nicotine use severity: Integrating the brain transcriptomes and GWAS variants via network approaches.Psychiatry research · 2024Article
- SPIDER: constructing cell-type-specific protein-protein interaction networks.Bioinformatics advances · 2024Article
- Predicting molecular mechanisms of hereditary diseases by using their tissue-selective manifestation.Molecular systems biology · 2023Article
- Effective Subject Representation based on Multi-omics Disease Networks using Graph Embedding.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2022Article
- Gene co-expression analyses of health(span) across multiple species.NAR genomics and bioinformatics · 2022Article
- Distinct translatome changes in specific neural populations precede electroencephalographic changes in prion-infected mice.PLoS pathogens · 2022Article
- GLIDER: function prediction from GLIDE-based neighborhoods.Bioinformatics (Oxford, England) · 2022Article
- Article
- IID 2021: towards context-specific protein interaction analyses by increased coverage, enhanced annotation and enrichment analysis.Nucleic acids research · 2022Article
- Construction and contextualization approaches for protein-protein interaction networks.Computational and structural biotechnology journal · 2022Review
- Scalable module detection for attributed networks with applications to breast cancer.Journal of applied statistics · 2022Article
- Decoding disease: from genomes to networks to phenotypes.Nature reviews. Genetics · 2021Review
- Analysing omics data sets with weighted nodes networks (WNNets).Scientific reports · 2021Article
- Disease gene prediction with privileged information and heteroscedastic dropout.Bioinformatics (Oxford, England) · 2021Article
32 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The prioritization of candidate disease-causing genes is a fundamental challenge in the post-genomic era. Current state of the art methods exploit a protein-protein interaction (PPI) network for this task. They are based on the observation that genes causing phenotypically-similar diseases tend to lie close to one another in a PPI network. However, to date, these methods have used a static picture of human PPIs, while diseases impact specific tissues in which the PPI networks may be dramatically different. Here, for the first time, we perform a large-scale assessment of the contribution of tissue-specific information to gene prioritization. By integrating tissue-specific gene expression data with PPI information, we construct tissue-specific PPI networks for 60 tissues and investigate their prioritization power. We find that tissue-specific PPI networks considerably improve the prioritization results compared to those obtained using a generic PPI network. Furthermore, they allow predicting novel disease-tissue associations, pointing to sub-clinical tissue effects that may escape early detection.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.