ArticleScientific data2025
An explainable dataset linking facial phenotypes and genes to rare genetic diseases.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Machine Learning, Large Language Models, and Multimodal AI for Diagnosing Pediatric Rare Diseases: Scoping Review.Journal of medical Internet research · 2026Article
- Multi-modal AI-enabled steatotic liver disease diagnostics using facial images and metabolomics.Cell reports. Medicine · 2026Article
- Initial-Visit Specialty Triage in Rare Diseases Using Large Language Models: Retrospective Benchmarking Study.Journal of medical Internet research · 2026Article
- Artificial intelligence for pediatric rare disease diagnosis: a multimethod study integrating published evidence and clinician interviews.BMC medical informatics and decision making · 2026Article
- WDR26-related Skraban-Deardorff syndrome: clinical, genetic and pathomechanistic insights.European journal of medical research · 2025Article
- An explainable dataset linking facial phenotypes and genes to rare genetic diseases.Scientific data · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Distinctive facial phenotypes serve as crucial diagnostic markers for many rare genetic diseases. Although AI-driven image recognition achieves high diagnostic accuracy, it often fails to explain its predictions. In this study, we present the Facial phenotype-Gene-Disease Dataset (FGDD), an explainable dataset collected from 509 research publications. It contains 1,147 data records encompassing 197 disease-causing genes, 437 facial phenotypes, and 211 disease entities, with 689 records having disease labels. Each data record represents a patient group and includes demographic information, variation information, and phenotype information. Baseline and explainability validations conducted on FGDD confirmed the dataset's effectiveness. FGDD supports the training of diagnostic models for rare genetic diseases while delivering explainable results, and provides a foundation for exploring intricate connections between genes, diseases, and facial phenotypes.
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Registered trials
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