Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Drug Similarity Integration Through Attentive Multi-view Graph
  Auto-Encoders   Drug similarity has been studied to support downstream clinical tasks such as
inferring novel properties of drugs (e.g. side effects, indications,
interactions) from known properties. The growing availability of new types of
drug features brings the opportunity of learning a more comprehensive and
accurate drug similarity that represents the full spectrum of underlying drug
relations. However, it is challenging to integrate these heterogeneous, noisy,
nonlinear-related information to learn accurate similarity measures especially
when labels are scarce. Moreover, there is a trade-off between accuracy and
interpretability. In this paper, we propose to learn accurate and interpretable
similarity measures from multiple types of drug features. In particular, we
model the integration using multi-view graph auto-encoders, and add attentive
mechanism to determine the weights for each view with respect to corresponding
tasks and features for better interpretability. Our model has flexible design
for both semi-supervised and unsupervised settings. Experimental results
demonstrated significant predictive accuracy improvement. Case studies also
showed better model capacity (e.g. embed node features) and interpretability.

31
The categories of this paper are: ao-sci
36
The categories of this paper are: cmp-lg
11
The categories of this paper are: hep-lat
20
The categories of this paper are: acc-phys
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The categories of this paper are: plasm-ph
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The categories of this paper are: supr-con
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The categories of this paper are: mtrl-th
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The categories of this paper are: alg-geom
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The categories of this paper are: chao-dyn
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The categories of this paper are: q-bio
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The categories of this paper are: gr-qc
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The categories of this paper are: math-ph
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The categories of this paper are: astro-ph
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The categories of this paper are: hep-ph
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The categories of this paper are: eess
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The categories of this paper are: physics