An effective similarity integration multi-modal graph neural network method to facilitate disease gene prioritization (A04)

我们将开发一种机器学习方法，通过分别纳入基因和疾病的相似性来改进疾病基因发现。在一项原则验证研究中，我们将利用一个大型神经发育障碍患者队列，以及其他儿科遗传病队列。具体地说，我们将开发一个端到端的多模图神经网络，用于疾病基因的优先排序。该模型将在疾病队列中进行评估，新的候选基因将通过细胞和动物模型进行实验确认。

We will develop a machine learning approach that improves disease gene discovery by incorporating the similarity of genes and diseases respectively. In a proof-of-principle study, we will make use of a large neurodevelopmental disorder patient cohort, as well as other pediatric genetic disease cohorts. Specifically, we will develop an end-to-end multi-modal graph neural network for disease gene prioritization. This model will be evaluated in the disease cohorts and novel candidate genes will be experimentally confirmed by cell and animal models.