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

一种有效的相似性整合多模态图神经网络方法，促进疾病基因优先排序（A04）

• 批准号: 524665467
• 金额: --
• 依托单位: Zentrum für Kinder- und Jugendmedizin
• 依托单位国家: 德国
• 项目类别: Collaborative Research Centres
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/524665467?language=en
• 关键词: effective; similarity; integration; multi; modal

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.

我们将开发一种机器学习方法，通过结合基因和疾病的相似性来改善疾病基因的发现。在一项原理验证研究中，我们将利用一个大型神经发育障碍患者队列，以及其他儿科遗传疾病队列。具体来说，我们将开发一个端到端的多模态图神经网络，用于疾病基因优先排序。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证实。