ArticleNature communications2023
Simulation of undiagnosed patients with novel genetic conditions.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02450851 (Clinical and Genetic Evaluation of Patients With Undiagnosed Disorders Through the Undiagnosed Diseases Network), which is not on this map. Cited by 8 papers.
What it found
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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.
Clinical and Genetic Evaluation of Patients With Undiagnosed Disorders Through the Undiagnosed Diseases Network
Who cites it
8 citing papers in PubMed, 8 citations in OpenAlex.
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- Generating Synthetic Multi-national Longitudinal Cohorts for Clinically Grounded HIV Research.medRxiv : the preprint server for health sciences · 2025Article
- Few shot learning for phenotype-driven diagnosis of patients with rare genetic diseases.NPJ digital medicine · 2025Article
- Automated Shared Phenotype Discovery in Undiagnosed Cohorts for Rare Disease Research.Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications · 2024Article
- Analysis of the Use of Korean Medicine Treatments among Children and Adolescents in South Korea: Analysis of Nationally Representative Sample.Healthcare (Basel, Switzerland) · 2024Article
- Simulation of undiagnosed patients with novel genetic conditions.Nature communications · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 2 countries.
Funding
Abstract
Rare Mendelian disorders pose a major diagnostic challenge and collectively affect 300-400 million patients worldwide. Many automated tools aim to uncover causal genes in patients with suspected genetic disorders, but evaluation of these tools is limited due to the lack of comprehensive benchmark datasets that include previously unpublished conditions. Here, we present a computational pipeline that simulates realistic clinical datasets to address this deficit. Our framework jointly simulates complex phenotypes and challenging candidate genes and produces patients with novel genetic conditions. We demonstrate the similarity of our simulated patients to real patients from the Undiagnosed Diseases Network and evaluate common gene prioritization methods on the simulated cohort. These prioritization methods recover known gene-disease associations but perform poorly on diagnosing patients with novel genetic disorders. Our publicly-available dataset and codebase can be utilized by medical genetics researchers to evaluate, compare, and improve tools that aid in the diagnostic process.
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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.