用于微生物-疾病关联预测的对抗正则化自动编码器图神经网络。

IF 6.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS Briefings in bioinformatics Pub Date : 2024-09-23 DOI:10.1093/bib/bbae584
Limuxuan He, Quan Zou, Qi Dai, Shuang Cheng, Yansu Wang
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引用次数: 0

摘要

背景:微生物栖息在人体的各个部位,是导致多种疾病的重要因素。预测微生物与疾病之间的关联对于了解致病机制以及制定预防和治疗策略至关重要。确定这些关联的生物实验既耗时又昂贵。因此,将深度学习与生物网络相结合,可以有效地大规模识别潜在的微生物与疾病的关联:我们提出了一种对抗正则化自动编码器图神经网络算法,名为 "堆叠对抗正则化微生物-疾病关联预测(SARMDA)",用于预测微生物与疾病之间的关联。首先,我们整合了微生物和疾病的拓扑结构相似性和功能相似性指标,构建了一个异构网络。然后,我们利用基于 GraphSAGE 的自动编码器,学习所构建网络中节点的拓扑和属性表示。最后,我们引入了对抗正则化自动编码器图神经网络嵌入模型,以解决传统 GraphSAGE 自动编码器在捕捉全局信息方面的固有局限性:在微生物-疾病对的五倍交叉验证下,利用人类微生物-疾病关联数据库(HMDAD)和Disbiome数据库将SARMDA与八种先进方法进行了比较。SARMDA在HMDAD上获得的最佳ROC曲线下面积(AUC)为0.9891\pm$0.0057,最佳精度-召回曲线下面积(AUPR)为0.9902\pm$0.0128。在 Disbiome 数据集上,AUC 为 0.9328$/pm$0.0072,最佳 AUPR 为 0.9233$/pm$0.0089,优于其他八种 MDAs 预测方法。此外,通过对哮喘和炎症性肠病病例的详细分析,证明了我们模型的有效性。
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Adversarial regularized autoencoder graph neural network for microbe-disease associations prediction.

Background: Microorganisms inhabit various regions of the human body and significantly contribute to numerous diseases. Predicting the associations between microbes and diseases is crucial for understanding pathogenic mechanisms and informing prevention and treatment strategies. Biological experiments to determine these associations are time-consuming and costly. Therefore, integrating deep learning with biological networks can efficiently identify potential microbe-disease associations on a large scale.

Methods: We propose an adversarial regularized autoencoder graph neural network algorithm, named Stacked Adversarial Regularization for Microbe-Disease Associations Prediction (SARMDA), for predicting associations between microbes and diseases. First, we integrate topological structural similarity and functional similarity metrics of microbes and diseases to construct a heterogeneous network. Then, utilizing an autoencoder based on GraphSAGE, we learn both the topological and attribute representations of nodes within the constructed network. Finally, we introduce an adversarial regularized autoencoder graph neural network embedding model to address the inherent limitations of traditional GraphSAGE autoencoders in capturing global information.

Results: Under the five-fold cross-validation on microbe-disease pairs, SARMDA was compared with eight advanced methods using the Human Microbe-Disease Association Database (HMDAD) and Disbiome databases. The best area under the ROC curve (AUC) achieved by SARMDA on HMDAD was 0.9891$\pm$0.0057, and the best area under the precision-recall curve (AUPR) was 0.9902$\pm$0.0128. On the Disbiome dataset, the AUC was 0.9328$\pm$0.0072, and the best AUPR was 0.9233$\pm$0.0089, outperforming the other eight MDAs prediction methods. Furthermore, the effectiveness of our model was demonstrated through a detailed analysis of asthma and inflammatory bowel disease cases.

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来源期刊
Briefings in bioinformatics
Briefings in bioinformatics 生物-生化研究方法
CiteScore
13.20
自引率
13.70%
发文量
549
审稿时长
6 months
期刊介绍: Briefings in Bioinformatics is an international journal serving as a platform for researchers and educators in the life sciences. It also appeals to mathematicians, statisticians, and computer scientists applying their expertise to biological challenges. The journal focuses on reviews tailored for users of databases and analytical tools in contemporary genetics, molecular and systems biology. It stands out by offering practical assistance and guidance to non-specialists in computerized methodologies. Covering a wide range from introductory concepts to specific protocols and analyses, the papers address bacterial, plant, fungal, animal, and human data. The journal's detailed subject areas include genetic studies of phenotypes and genotypes, mapping, DNA sequencing, expression profiling, gene expression studies, microarrays, alignment methods, protein profiles and HMMs, lipids, metabolic and signaling pathways, structure determination and function prediction, phylogenetic studies, and education and training.
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