仅凭症状数据，用知识图谱+AI精准找致病基因。
PhenoKG: Knowledge Graph-Driven Gene Discovery and Patient Insights from Phenotypes Alone
- 构建疾病知识图谱，融合图神经网络与Transformer预测致病基因。
- 在真实数据集上MRR达24.64%，优于现有方法19.02%。
- 无需基因列表，适合基因信息缺失的临床场景。
从患者表型识别致病基因仍是精准医疗中的重大挑战，对遗传病的诊断与治疗具有重要意义。本文提出一种基于图的新方法，仅需患者表型数据（或含候选基因列表），即可预测致病基因。该方法整合罕见病知识图谱（PhenoKG），结合图神经网络与Transformer模型，在真实世界数据集MyGene2上取得显著性能提升：平均倒数排名（MRR）达24.64%，nDCG@100为33.64%，超越最佳基线模型SHEPHERD（MRR 19.02%，nDCG@100 30.54%）。通过大量消融实验验证了各组件贡献。该方法可泛化至仅有表型数据的情形，解决了基因信息不全时临床决策支持的关键难题。
原文摘要 · Abstract (English)
Identifying causative genes from patient phenotypes remains a significant challenge in precision medicine, with important implications for the diagnosis and treatment of genetic disorders. We propose a novel graph-based approach for predicting causative genes from patient phenotypes, with or without an available list of candidate genes, by integrating a rare disease knowledge graph (KG). Our model, combining graph neural networks and transformers, achieves substantial improvements over the current state-of-the-art. On the real-world MyGene2 dataset, it attains a mean reciprocal rank (MRR) of 24.64\% and nDCG@100 of 33.64\%, surpassing the best baseline (SHEPHERD) at 19.02\% MRR and 30.54\% nDCG@100. We perform extensive ablation studies to validate the contribution of each model component. Notably, the approach generalizes to cases where only phenotypic data are available, addressing key challenges in clinical decision support when genomic information is incomplete.
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