Work

An explainable dataset linking facial phenotypes and genes to rare genetic diseases

Year: 2025

Type: data-paper

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.

Source: Scientific Data

DOI: 10.1038/s41597-025-04922-z

Authors: Jie Song, Mengqiao He, Shumin Ren, Bairong Shen

Institutions: Sichuan University, West China Medical Center of Sichuan University, West China Hospital of Sichuan University

Language: English

Topic: Genomics and Rare Diseases

Subfield: Genetics

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,490