An explainable dataset linking facial phenotypes and genes to rare genetic diseases - Jie Song; Mengqiao He; Shumin Ren; Bairong Shen (Scientific Data)
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.