In this study, we present a deep learning framework with variational graph auto-encoder for miRNA-disease association prediction (VGAE-MDA). VGAE-MDA first gets the representations of miRNAs and diseases from the heterogeneous networks constructed by miRNA-miRNA similarity, disease-disease similarity,...
GraphMAE是最近提出的方法,旨在弥合这一差距,但其在无监督学习任务上的性能尚未探讨。 5.VGAE特点:VGAE是一种生成模型,仅在解码器中恢复链接。尽管存在一些自监督的VGAE模型,用于重构特征,但大多数只利用节点级别的嵌入,而忽略包含对节点特征有帮助的细粒度信息的特征级别嵌入。 本文重新考虑了生成图自监督学习,...
图生成模型:变分图自编码器(VGAE) 变分自编码器的训练过程 VAE的本质 VAE虽然也称是AE(AutoEncoder)的一种,但它的做法(或者说它对网络的诠释)是别具一格的。在VAE中,它的Encoder有两个,一个用来计算均值,一个用来计算方差,这已经让人意外了:Encoder不是用来Encode的,是用来算均值和方差的,这真是大新闻了,还...
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We use a variational graph autoencoder (VGAE) to generate synthetic samples from real-world electronic health records. Our approach generates health records not seen in the training data. We show that these artificial patient trajectories are realistic and preserve patient privacy and can therefore ...
Initially, we use a variational graph autoencoder (VGAE) to compress the augmented gene expression data into a low-dimensional latent representation Z. A clustering layer, denoted as {μj}j=1J, is subsequently introduced within the encoder's latent space, where J represents the total number ...
Variational autoencoders (VAEs) play an important role in high-dimensional data generation based on their ability to fuse the stochastic data representatio
while graph autoencoders propagate labels via known lncRNA-disease associations. These two kinds of autoencoders are trained alternately by adopting variational expectation maximization algorithm. The integration of both the VGAE for graph representation learning, and the alternate training via variational...
A Variational Autoencoder is a type of likelihood-based generative model. It consists of an encoder, that takes in data $x$ as input and transforms this into a latent representation $z$, and a decoder, that takes a latent representation $z$ and returns a reconstruction $\hat{x}$. Infere...
Variational Graph Normalized AutoEncoder 本文提出了两种变体的图自编码器,分别称为图归一化自编码器(GNAE)和变分图归一化自编码器(VGNAE)。对于每个节点,GNAE对其邻域的局部结构信息和节点特征信息进行编码,从而推导出潜在变量 我们还提出了一个变分图归一化自动编码器(VGNAE)。由于VGAEs中的平均向量也具有孤立节...