MedGraphNet: Leveraging Multi-Relational Graph Neural Networks and Text Knowledge for Biomedical Predictions.

Oladimeji Macaulay, Michael Servilla, David Arredondo, Kushal Virupakshappa, Yue Hu, Luis Tafoya, Yanfu Zhang, Avinash Sahu
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Abstract

Genetic, molecular, and environmental factors influence diseases through complex interactions with genes, phenotypes, and drugs. Current methods often fail to integrate diverse multi-relational biological data meaningfully, limiting the discovery of novel risk genes and drugs. To address this, we present MedGraphNet, a multi-relational Graph Neural Network (GNN) model designed to infer relationships among drugs, genes, diseases, and phenotypes. MedGraphNet initializes nodes using informative embeddings from existing text knowledge, allowing for robust integration of various data types and improved generalizability. Our results demonstrate that MedGraphNet matches and often outperforms traditional single-relation approaches, particularly in scenarios with isolated or sparsely connected nodes. The model shows generalizability to external datasets, achieving high accuracy in identifying disease-gene associations and drug-phenotype relationships. Notably, MedGraphNet accurately inferred drug side effects without direct training on such data. Using Alzheimer's disease as a case study, MedGraphNet successfully identified relevant phenotypes, genes, and drugs, corroborated by existing literature. These findings demonstrate the potential of integrating multi-relational data with text knowledge to enhance biomedical predictions and drug repurposing for diseases. MedGraphNet code is available at https://github.com/vinash85/MedGraphNet.

MedGraphNet:利用多关系图神经网络和文本知识进行生物医学预测。
遗传、分子和环境因素通过与基因、表型和药物的复杂相互作用影响疾病。目前的方法往往不能有效地整合各种多关系的生物学数据,限制了新的风险基因和药物的发现。为了解决这个问题,我们提出了MedGraphNet,一个多关系图神经网络(GNN)模型,旨在推断药物、基因、疾病和表型之间的关系。MedGraphNet使用来自现有文本知识的信息嵌入来初始化节点,从而允许各种数据类型的健壮集成和改进的泛化性。我们的研究结果表明,MedGraphNet匹配并经常优于传统的单关系方法,特别是在具有孤立或稀疏连接节点的场景中。该模型显示了对外部数据集的可泛化性,在识别疾病基因关联和药物表型关系方面达到了很高的准确性。值得注意的是,MedGraphNet在没有对这些数据进行直接训练的情况下准确地推断了药物的副作用。MedGraphNet以阿尔茨海默病为例,成功地鉴定出相关的表型、基因和药物,并得到了现有文献的证实。这些发现证明了将多关系数据与文本知识集成在一起以增强生物医学预测和疾病药物再利用的潜力。MedGraphNet的代码可从https://github.com/vinash85/MedGraphNet获得。
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