Web: http://arxiv.org/abs/2209.09941

Sept. 22, 2022, 1:11 a.m. | Nhat Khang Ngo, Truong Son Hy, Risi Kondor

cs.LG updates on arXiv.org arxiv.org

Latent representations of drugs and their targets produced by contemporary
graph autoencoder-based models have proved useful in predicting many types of
node-pair interactions on large networks, including drug-drug, drug-target, and
target-target interactions. However, most existing approaches model the node's
latent spaces in which node distributions are rigid and disjoint; these
limitations hinder the methods from generating new links among pairs of nodes.
In this paper, we present the effectiveness of variational graph autoencoders
(VGAE) in modeling latent node representations on …

arxiv bio deep generative models generative models graphs interactions

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