仅用症状数据就能精准定位罕见病致病基因，助力临床诊断
Knowledge Graph Sparsification for GNN-based Rare Disease Diagnosis
- 基于患者症状构建子图，用GNN模型识别最可能的致病基因
- 在两个生物医学数据集上表现优异，集成其他方法后性能显著提升
- 只需症状数据即可运行，适合资源有限地区的医疗场景
罕见遗传病诊断面临严峻挑战：患者数据不足、全基因组测序难获取，且可能致病基因数量庞大。这些限制导致诊断周期长、治疗不当和延误，尤其影响资源匮乏地区患者。我们提出RareNet，一种基于子图的图神经网络，仅需患者表型数据即可识别最可能的致病基因，并提取聚焦的患者子图用于靶向临床研究。RareNet可独立使用，也可作为其他候选基因优先排序方法的预处理或后处理过滤器，持续提升其性能并提供可解释性洞察。在两个生物医学数据集上的全面评估表明，RareNet在因果基因预测上表现竞争且稳健，与其它框架集成后实现显著性能提升。由于仅需易获取的表型数据，RareNet使复杂基因分析普惠化，对缺乏先进基因组基础设施的群体具有重要价值。
原文摘要 · Abstract (English)
Rare genetic disease diagnosis faces critical challenges: insufficient patient data, inaccessible full genome sequencing, and the immense number of possible causative genes. These limitations cause prolonged diagnostic journeys, inappropriate treatments, and critical delays, disproportionately affecting patients in resource-limited settings where diagnostic tools are scarce. We propose RareNet, a subgraph-based Graph Neural Network that requires only patient phenotypes to identify the most likely causal gene and retrieve focused patient subgraphs for targeted clinical investigation. RareNet can function as a standalone method or serve as a pre-processing or post-processing filter for other candidate gene prioritization methods, consistently enhancing their performance while potentially enabling explainable insights. Through comprehensive evaluation on two biomedical datasets, we demonstrate competitive and robust causal gene prediction and significant performance gains when integrated with other frameworks. By requiring only phenotypic data, which is readily available in any clinical setting, RareNet democratizes access to sophisticated genetic analysis, offering particular value for underserved populations lacking advanced genomic infrastructure.
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