ArticleBioinformatics (Oxford, England)2018
PhenoRank: reducing study bias in gene prioritization through simulation.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Genome-scale perturbation signatures from primary human CD4bioRxiv : the preprint server for biology · 2026Article
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- Advancing edge-based clustering and graph embedding for biological network analysis: a case study in RASopathies.Briefings in bioinformatics · 2025Article
- Network-Based Approaches for Disease-Gene Association Prediction Using Protein-Protein Interaction Networks.International journal of molecular sciences · 2022Article
- Contribution of model organism phenotypes to the computational identification of human disease genes.Disease models & mechanisms · 2022Article
- Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases.Briefings in bioinformatics · 2022Article
- DeepSVP: integration of genotype and phenotype for structural variant prioritization using deep learning.Bioinformatics (Oxford, England) · 2022Article
- Diagnosis of Genetic White Matter Disorders by Singleton Whole-Exome and Genome Sequencing Using Interactome-Driven Prioritization.Neurology · 2022Article
- WINNER: A network biology tool for biomolecular characterization and prioritization.Frontiers in big data · 2022Article
- Computational approaches for predicting variant impact: An overview from resources, principles to applications.Frontiers in genetics · 2022Review
- Semantic similarity and machine learning with ontologies.Briefings in bioinformatics · 2021Review
- Predicting candidate genes from phenotypes, functions and anatomical site of expression.Bioinformatics (Oxford, England) · 2021Article
- PRYNT: a tool for prioritization of disease candidates from proteomics data using a combination of shortest-path and random walk algorithms.Scientific reports · 2021Article
- DeepPheno: Predicting single gene loss-of-function phenotypes using an ontology-aware hierarchical classifier.PLoS computational biology · 2020Article
- Finding Diagnostically Useful Patterns in Quantitative Phenotypic Data.American journal of human genetics · 2019Article
- A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases.Journal of integrative bioinformatics · 2019Review
- DeepPVP: phenotype-based prioritization of causative variants using deep learning.BMC bioinformatics · 2019Article
- Expansion of the Human Phenotype Ontology (HPO) knowledge base and resources.Nucleic acids research · 2019Article
- Article
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Authors and funding
3 authors.
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Abstract
Motivation: Genome-wide association studies have identified thousands of loci associated with human disease, but identifying the causal genes at these loci is often difficult. Several methods prioritize genes most likely to be disease causing through the integration of biological data, including protein-protein interaction and phenotypic data. Data availability is not the same for all genes however, potentially influencing the performance of these methods. Results: We demonstrate that whilst disease genes tend to be associated with greater numbers of data, this may be at least partially a result of them being better studied. With this observation we develop PhenoRank, which prioritizes disease genes whilst avoiding being biased towards genes with more available data. Bias is avoided by comparing gene scores generated for the query disease against gene scores generated using simulated sets of phenotype terms, which ensures that differences in data availability do not affect the ranking of genes. We demonstrate that whilst existing prioritization methods are biased by data availability, PhenoRank is not similarly biased. Avoiding this bias allows PhenoRank to effectively prioritize genes with fewer available data and improves its overall performance. PhenoRank outperforms three available prioritization methods in cross-validation (PhenoRank area under receiver operating characteristic curve [AUC]=0.89, DADA AUC = 0.87, EXOMISER AUC = 0.71, PRINCE AUC = 0.83, P < 2.2 × 10-16). Availability and implementation: PhenoRank is freely available for download at https://github.com/alexjcornish/PhenoRank. Supplementary information: Supplementary data are available at Bioinformatics online.
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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.