Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Graph2MDA: a multi-modal variational graph embedding model for
  predicting microbe-drug associations   Accumulated clinical studies show that microbes living in humans interact
closely with human hosts, and get involved in modulating drug efficacy and drug
toxicity. Microbes have become novel targets for the development of
antibacterial agents. Therefore, screening of microbe-drug associations can
benefit greatly drug research and development. With the increase of microbial
genomic and pharmacological datasets, we are greatly motivated to develop an
effective computational method to identify new microbe-drug associations. In
this paper, we proposed a novel method, Graph2MDA, to predict microbe-drug
associations by using variational graph autoencoder (VGAE). We constructed
multi-modal attributed graphs based on multiple features of microbes and drugs,
such as molecular structures, microbe genetic sequences, and function
annotations. Taking as input the multi-modal attribute graphs, VGAE was trained
to learn the informative and interpretable latent representations of each node
and the whole graph, and then a deep neural network classifier was used to
predict microbe-drug associations. The hyperparameter analysis and model
ablation studies showed the sensitivity and robustness of our model. We
evaluated our method on three independent datasets and the experimental results
showed that our proposed method outperformed six existing state-of-the-art
methods. We also explored the meaningness of the learned latent representations
of drugs and found that the drugs show obvious clustering patterns that are
significantly consistent with drug ATC classification. Moreover, we conducted
case studies on two microbes and two drugs and found 75\%-95\% predicted
associations have been reported in PubMed literature. Our extensive performance
evaluations validated the effectiveness of our proposed method.\

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The categories of this paper are: nlin
3
The categories of this paper are: physics
14
The categories of this paper are: math-ph
26
The categories of this paper are: bayes-an
16
The categories of this paper are: stat
36
The categories of this paper are: cmp-lg
34
The categories of this paper are: chem-ph
8
The categories of this paper are: hep-ex
6
The categories of this paper are: astro-ph
13
The categories of this paper are: quant-ph
20
The categories of this paper are: acc-phys
33
The categories of this paper are: plasm-ph
35
The categories of this paper are: mtrl-th
18
The categories of this paper are: econ
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The categories of this paper are: eess
29
The categories of this paper are: funct-an