ArticleScientific data2021
A resource to explore the discovery of rare diseases and their causative genes.
Article in Scientific data, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Quantifying hope: an EU perspective of rare disease therapeutic space and market dynamics.Frontiers in public health · 2025Pooled it
- A text mining and ontology-based approach using phenotypes to obtain relevant literature for rare diseases.iScience · 2026Article
- Review
- GenRiskPro: A Comprehensive Whole-Genome Sequencing Analysis Platform for Clinical and Wellness Applications.Computational and structural biotechnology journal · 2026Article
- Domain-specific embeddings uncover latent genetics knowledge.bioRxiv : the preprint server for biology · 2025Article
- PSAP-Genomic-Regions: A Method Leveraging Population Data to Prioritize Coding and Non-Coding Variants in Whole Genome Sequencing for Rare Disease Diagnosis.Genetic epidemiology · 2025Article
- Leaving no patient behind! Expert recommendation in the use of innovative technologies for diagnosing rare diseases.Orphanet journal of rare diseases · 2024Review
- Potential of Artificial Intelligence to Accelerate Drug Development for Rare Diseases.Pharmaceutical medicine · 2024Article
- Heterozygous loss-of-function variants in DOCK4 cause neurodevelopmental delay and microcephaly.Human genetics · 2024Article
- GPAD: a natural language processing-based application to extract the gene-disease association discovery information from OMIM.BMC bioinformatics · 2024Article
- The Dangers of Acetaminophen for Neurodevelopment Outweigh Scant Evidence for Long-Term Benefits.Children (Basel, Switzerland) · 2023Review
- Methylation analysis by targeted bisulfite sequencing in large for gestational age (LGA) newborns: the LARGAN cohort.Clinical epigenetics · 2023Article
- Rare disease research resources at the Rat Genome Database.Genetics · 2023Article
- Activated PI3Kδ syndrome - reviewing challenges in diagnosis and treatment.Frontiers in immunology · 2023Review
- Knowledge-based approaches to drug discovery for rare diseases.Drug discovery today · 2022Review
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.
Funding
Abstract
Here, we describe a dataset with information about monogenic, rare diseases with a known genetic background, supplemented with manually extracted provenance for the disease itself and the discovery of the underlying genetic cause. We assembled a collection of 4166 rare monogenic diseases and linked them to 3163 causative genes, annotated with OMIM and Ensembl identifiers and HGNC symbols. The PubMed identifiers of the scientific publications, which for the first time described the rare diseases, and the publications, which found the genes causing the diseases were added using information from OMIM, PubMed, Wikipedia, whonamedit.com, and Google Scholar. The data are available under CC0 license as spreadsheet and as RDF in a semantic model modified from DisGeNET, and was added to Wikidata. This dataset relies on publicly available data and publications with a PubMed identifier, but by our effort to make the data interoperable and linked, we can now analyse this data. Our analysis revealed the timeline of rare disease and causative gene discovery and links them to developments in methods.
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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.