| 引用本文: | 刘立伟,刘晓兰,谭者斌.基于图自编码器和协同训练预测miRNA与疾病的关联[J].生物信息学,2024,22(2):116-123. |
| LIU Liwei,LIU Xiaolan,TAN Zhebin.Predicting miRNA-disease associations based on graph autoencoders and collaborative training[J].Chinese Journal of Bioinformatics,2024,22(2):116-123. |
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| 摘要: |
| 近年来,越来越多的生物学实验研究表明,microRNA (miRNA)在人类复杂疾病的发展中发挥着重要作用。因此,预测miRNA与疾病之间的关联有助于疾病的准确诊断和有效治疗。由于传统的生物学实验是一种昂贵且耗时的方式,于是许多基于生物学数据的计算模型被提出来预测miRNA与疾病的关联。本研究提出了一种端到端的深度学习模型来预测miRNA-疾病关联关系,称为MDAGAC。首先,通过整合疾病语义相似性,miRNA功能相似性和高斯相互作用谱核相似性,构建miRNA和疾病的相似性图。然后,通过图自编码器和协同训练来改善标签传播的效果。该模型分别在miRNA图和疾病图上建立了两个图自编码器,并对这两个图自编码器进行了协同训练。miRNA图和疾病图上的图自编码器能够通过初始关联矩阵重构得分矩阵,这相当于在图上传播标签。miRNA-疾病关联的预测概率可以从得分矩阵得到。基于五折交叉验证的实验结果表明,MDAGAC方法可靠有效,优于现有的几种预测miRNA-疾病关联的方法。 |
| 关键词: microRNA 疾病 关联预测 协同训练 图自编码器 端到端 |
| DOI：10.12113/202302009 |
| 分类号:Q522+.2 |
| 文献标识码:A |
| 基金项目:海南省计算科学与应用重点实验室开放课题(No. JSKX202102). |
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| Predicting miRNA-disease associations based on graph autoencoders and collaborative training |
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LIU Liwei1, LIU Xiaolan1, TAN Zhebin2
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(1.School of Science, Dalian Jiaotong University, Dalian 116028, Liaoning, China; 2.School of Software,Dalian Jiaotong University, Dalian 116028, Liaoning, China)
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| Abstract: |
| In recent years, increasing biological experiments have shown that microRNA (miRNA) plays an important role in the development of human complex diseases. Therefore, predicting miRNA-disease associations can contribute to accurate diagnosis and effective treatment of diseases. Since traditional biological experiments are expensive and time-consuming, plenty of computational models based on biological data have been proposed to predict MiRNA-disease associations. In this study, we propose an end-to-end deep learning model to predict miRNA-disease associations (MDAGAC). Specifically, we firstly construct the similarity network of miRNA and disease by integrating disease semantic similarity, miRNA functional similarity and Gaussian interaction profile kernel similarity. Then, the effect of label propagation is improved through Graph Autoencoders and Collaborative training. This model implements two graph autoencoders on miRNA graph and disease graph respectively, and trains these two graph autoencoders collaboratively. Graph autoencoders on miRNA graph and disease graph are able to reconstruct score matrix through initial association matrix, which is equivalent to propagate labels on graphs. The prediction probability of MiRNA-disease association can be obtained from the score matrix. The results of the experiment based on 5-fold cross validation show that MDAGAC is reliable and effective and outperforms current MiRNA-disease associations prediction methods. |
| Key words: microRNA Disease Association prediction Collaborative training Graph autoencoder End-to-end |
