ArticleBriefings in bioinformatics2022
Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases.
Article in Briefings in bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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39 citing papers in PubMed, 50 citations in OpenAlex.
- INDIGENA: inductive prediction of disease-gene associations using phenotype ontologies.Bioinformatics (Oxford, England) · 2026Article
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- Systematic benchmarking demonstrates large language models have not reached the diagnostic accuracy of traditional rare-disease decision support tools.European journal of human genetics : EJHG · 2026Article
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- Comprehensive evaluation of ACMG/AMP-based variant classification tools.Bioinformatics (Oxford, England) · 2026Article
- Association Between Catenin Beta-1 (CTNNB1) Gene Polymorphisms and Non-Traumatic Osteonecrosis of the Femoral Head (ONFH).Current issues in molecular biology · 2026Article
- Benchmarking knowledge graph embedding models for the prediction of oligogenic combinations.Briefings in bioinformatics · 2026Article
- Accelerate the discovery of genetic variants in mitochondrial diseases with Variant prIOritization using Latent spAce.Briefings in bioinformatics · 2025Article
- geneEX: An Integrated Phenotype-Driven Algorithm for Rapid Identification of Causative Variants in Monogenic Disorders.Molecular genetics & genomic medicine · 2025Article
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- Clinical and data-driven optimization of Genomiser for rare disease patients: experience from the Hong Kong Genome Project.Briefings in bioinformatics · 2025Article
- A Hypergraph powered approach to Phenotype-driven Gene Prioritization and Rare Disease Prediction.Scientific reports · 2025Article
- PhenoDP: leveraging deep learning for phenotype-based case reporting, disease ranking, and symptom recommendation.Genome medicine · 2025Article
- Digenic variant interpretation with hypothesis-driven explainable AI.NAR genomics and bioinformatics · 2025Article
- The application of Large Language Models to the phenotype-based prioritization of causative genes in rare disease patients.Scientific reports · 2025Article
- Towards a standard benchmark for phenotype-driven variant and gene prioritisation algorithms: PhEval - Phenotypic inference Evaluation framework.BMC bioinformatics · 2025Article
- A clinical knowledge graph-based framework to prioritize candidate genes for facilitating diagnosis of Mendelian diseases and rare genetic conditions.BMC bioinformatics · 2025Article
- Evaluating seven bioinformatics platforms for tertiary analysis of genomic data from whole exome sequencing in a pilot group of patients.Advances in laboratory medicine · 2025Article
- An AI-based approach driven by genotypes and phenotypes to uplift the diagnostic yield of genetic diseases.Human genetics · 2025Article
- Phenotype-driven genomics enhance diagnosis in children with unresolved neuromuscular diseases.European journal of human genetics : EJHG · 2025Article
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Authors and funding
17 authors at 3 institutions in 1 country.
Funding
Abstract
It's challenging work to identify disease-causing genes from the next-generation sequencing (NGS) data of patients with Mendelian disorders. To improve this situation, researchers have developed many phenotype-driven gene prioritization methods using a patient's genotype and phenotype information, or phenotype information only as input to rank the candidate's pathogenic genes. Evaluations of these ranking methods provide practitioners with convenience for choosing an appropriate tool for their workflows, but retrospective benchmarks are underpowered to provide statistically significant results in their attempt to differentiate. In this research, the performance of ten recognized causal-gene prioritization methods was benchmarked using 305 cases from the Deciphering Developmental Disorders (DDD) project and 209 in-house cases via a relatively unbiased methodology. The evaluation results show that methods using Human Phenotype Ontology (HPO) terms and Variant Call Format (VCF) files as input achieved better overall performance than those using phenotypic data alone. Besides, LIRICAL and AMELIE, two of the best methods in our benchmark experiments, complement each other in cases with the causal genes ranked highly, suggesting a possible integrative approach to further enhance the diagnostic efficiency. Our benchmarking provides valuable reference information to the computer-assisted rapid diagnosis in Mendelian diseases and sheds some light on the potential direction of future improvement on disease-causing gene prioritization methods.
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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.