通过图形神经网络识别候选基因-疾病关联
Pietro Cinaglia1, Mario Cannataro2
1Department of Health Sciences, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy.
Entropy (Basel, Switzerland)
|June 28, 2023
概括
本研究引入了一个图形神经网络 (GNN) 模型,以识别潜在的基因疾病关联 (GDA). 全国基因组实现了95%的AUC,成功预测了93%的顶级候选GDAs.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 网络科学 网络科学
背景情况:
- 现实世界的系统通常被表示为网络,生物关系,如基因疾病协会 (GDAs) 是主要的例子.
- 使用节点和边缘的图形结构为建模这些复杂的生物相互连接提供了自然框架.
研究的目的:
- 开发和评估图形神经网络 (GNN) 模型,以准确识别候选基因疾病关联.
- 利用网络属性和图形卷积来预测新的GDA.
主要方法:
- 设计了一个图形神经网络 (GNN) 模型,利用多层和非线性图形卷积.
- 节点嵌入被计算为在多维向量空间中表示基因和疾病.
- 该模型经过训练和验证,使用来自DisGeNET数据集的精选基因疾病关系数据进行了训练和验证.
主要成果:
- 在培训,验证和测试数据集中,GNN模型实现了95%的高曲线下面面积 (AUC).
- 该模型表现出强大的预测性能,正确识别了排名最高的93%的候选GDA.
- 在疾病基因协会矿工 (DG-AssocMiner) 数据集上进一步评估了性能.
结论:
- 图形神经网络为发现基因疾病关联提供了一种强大的方法.
- 开发的GNN模型显示了促进生物网络分析和疾病基因发现的巨大潜力.
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