Zhongguo Dang Dai Er Ke Za Zhi. 2026 Aug 15;28(8):991-997. doi: 10.7499/j.issn.1008-8830.2511119.
ABSTRACT
OBJECTIVES: To establish a genotype-phenotype database and machine learning models for pseudohypertrophic muscular dystrophy (PMD), and to explore genotype-phenotype correlations of the disease.
METHODS: Clinical data of children with PMD admitted to Xiangya Hospital, Central South University from January 2010 to December 2024, together with cases retrieved from the PubMed database between January 1987 and December 2024, were retrospectively collected to construct a genotype-phenotype database, with an online query function via a WeChat mini-program. Based on this database, Random Forest, Extreme Gradient Boosting, and Light Gradient Boosting Machine algorithms were integrated using a soft voting ensemble strategy to build a machine learning model predicting clinical phenotypes associated with small variants. The predictive performance of the model was compared with that of the reading-frame rule. The model was deployed online via the Streamlit platform.
RESULTS: The database included 17 053 PMD cases, comprising 472 patients in the local cohort and 16 581 literature-derived cases. Modeling and validation were performed on a filtered dataset comprising small variants. In the internal test set, the machine learning model achieved an area under the receiver operating characteristic curve (AUC) of 0.924 (95%CI: 0.881-0.963), significantly higher than the reading-frame rule AUC of 0.652 (95%CI: 0.591-0.717) (P<0.001). In the external test set, the machine learning model achieved an AUC of 0.854 (95%CI: 0.736-1.000), compared to 0.667 (95%CI: 0.500-1.000) for the reading-frame rule, with no statistically significant difference (P>0.05).
CONCLUSIONS: The constructed PMD genotype-phenotype database and machine learning prediction model provide an efficient and reliable novel tool for phenotype prediction in PMD.
PMID:42608308 | DOI:10.7499/j.issn.1008-8830.2511119
