ArticleBMC bioinformatics2016
PCAN: phenotype consensus analysis to support disease-gene association.
Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- A Hypergraph powered approach to Phenotype-driven Gene Prioritization and Rare Disease Prediction.Scientific reports · 2025Article
- Single-cell data combined with phenotypes improves variant interpretation.BMC genomics · 2025Article
- A clinical knowledge graph-based framework to prioritize candidate genes for facilitating diagnosis of Mendelian diseases and rare genetic conditions.BMC bioinformatics · 2025Article
- CADA: phenotype-driven gene prioritization based on a case-enriched knowledge graph.NAR genomics and bioinformatics · 2021Article
- A pipeline-friendly software tool for genome diagnostics to prioritize genes by matching patient symptoms to literature.Advanced genetics (Hoboken, N.J.) · 2020Article
- Interpretable Clinical Genomics with a Likelihood Ratio Paradigm.American journal of human genetics · 2020Article
- Exploratory Analysis of Circulating miRNA Signatures in Atrial Fibrillation Patients Determining Potential Biomarkers to Support Decision-Making in Anticoagulation and Catheter Ablation.International journal of molecular sciences · 2020Observational
- A network embedding model for pathogenic genes prediction by multi-path random walking on heterogeneous network.BMC medical genomics · 2019Article
- Familial chronic megacolon presenting in childhood or adulthood: Seeking the presumed gene association.Neurogastroenterology and motility · 2019Article
- A tailored approach to fusion transcript identification increases diagnosis of rare inherited disease.PloS one · 2019Article
- Phenotype-driven gene prioritization for rare diseases using graph convolution on heterogeneous networks.BMC medical genomics · 2018Article
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2 authors.
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Abstract
backgroundBridging genotype and phenotype is a fundamental biomedical challenge that underlies more effective target discovery and patient-tailored therapy. Approaches that can flexibly and intuitively, integrate known gene-phenotype associations in the context of molecular signaling networks are vital to effectively prioritize and biologically interpret genes underlying disease traits of interest.
resultsWe describe Phenotype Consensus Analysis (PCAN); a method to assess the consensus semantic similarity of phenotypes in a candidate gene's signaling neighborhood. We demonstrate that significant phenotype consensus (p < 0.05) is observable for ~67% of 4,549 OMIM disease-gene associations, using a combination of high quality String interactions + Metabase pathways and use Joubert Syndrome to demonstrate the ease with which a significant result can be interrogated to highlight discriminatory traits linked to mechanistically related genes.
conclusionsWe advocate phenotype consensus as an intuitive and versatile method to aid disease-gene association, which naturally lends itself to the mechanistic deconvolution of diverse phenotypes. We provide PCAN to the community as an R package ( http://bioconductor.org/packages/PCAN/ ) to allow flexible configuration, extension and standalone use or integration to supplement existing gene prioritization workflows.
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