ArticleNAR genomics and bioinformatics2021
CADA: phenotype-driven gene prioritization based on a case-enriched knowledge graph.
Article in NAR genomics and bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed, 37 citations in OpenAlex.
- PhenoSS: phenotype semantic similarity-based approach for rare disease prediction and patient clustering.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Leveraging Next-Generation Phenotyping in Dysmorphology to Support Variant Interpretation in Mowat-Wilson Syndrome.Neurology. Genetics · 2026Article
- PhenoSS: Phenotype semantic similarity-based approach for rare disease prediction and patient clustering.medRxiv : the preprint server for health sciences · 2026Article
- Few shot learning for phenotype-driven diagnosis of patients with rare genetic diseases.NPJ digital medicine · 2025Article
- AI-powered precision medicine: utilizing genetic risk factor optimization to revolutionize healthcare.NAR genomics and bioinformatics · 2025Review
- 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
- Assessing the utility of large language models for phenotype-driven gene prioritization in the diagnosis of rare genetic disease.American journal of human genetics · 2024Article
- Next-generation phenotyping integrated in a national framework for patients with ultrarare disorders improves genetic diagnostics and yields new molecular findings.Nature genetics · 2024Article
- REEV: review, evaluate and explain variants.Nucleic acids research · 2024Article
- A corpus of GA4GH Phenopackets: case-level phenotyping for genomic diagnostics and discovery.medRxiv : the preprint server for health sciences · 2024Article
- Enhancing Variant Prioritization in VarFish through On-Premise Computational Facial Analysis.Genes · 2024Article
- STIGMA: Single-cell tissue-specific gene prioritization using machine learning.American journal of human genetics · 2024Article
- Enhancing phenotype recognition in clinical notes using large language models: PhenoBCBERT and PhenoGPT.Patterns (New York, N.Y.) · 2024Article
- A new model construction based on the knowledge graph for mining elite polyphenotype genes in crops.Frontiers in plant science · 2024Article
- PhenoSV: interpretable phenotype-aware model for the prioritization of genes affected by structural variants.Nature communications · 2023Article
- Enhancing Phenotype Recognition in Clinical Notes Using Large Language Models: PhenoBCBERT and PhenoGPT.ArXiv · 2023Article
- KG-Hub-building and exchanging biological knowledge graphs.Bioinformatics (Oxford, England) · 2023Article
- The future role of facial image analysis in ACMG classification guidelines.Medizinische Genetik : Mitteilungsblatt des Berufsverbandes Medizinische Genetik e.V · 2023Article
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Corrections and comments
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Authors and funding
20 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Many rare syndromes can be well described and delineated from other disorders by a combination of characteristic symptoms. These phenotypic features are best documented with terms of the Human Phenotype Ontology (HPO), which are increasingly used in electronic health records (EHRs), too. Many algorithms that perform HPO-based gene prioritization have also been developed; however, the performance of many such tools suffers from an over-representation of atypical cases in the medical literature. This is certainly the case if the algorithm cannot handle features that occur with reduced frequency in a disorder. With Cada, we built a knowledge graph based on both case annotations and disorder annotations. Using network representation learning, we achieve gene prioritization by link prediction. Our results suggest that Cada exhibits superior performance particularly for patients that present with the pathognomonic findings of a disease. Additionally, information about the frequency of occurrence of a feature can readily be incorporated, when available. Crucial in the design of our approach is the use of the growing amount of phenotype-genotype information that diagnostic labs deposit in databases such as ClinVar. By this means, Cada is an ideal reference tool for differential diagnostics in rare disorders that can also be updated regularly.
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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.