Multi-task Joint Strategies of Self-supervised Representation Learning on Biomedical Networks for Drug Discovery. (arXiv:2201.04437v1 [cs.LG])

Self-supervised representation learning (SSL) on biomedical networks provides
new opportunities for drug discovery which is lack of available biological or
clinic phenotype. However, how to effectively combine multiple SSL models is
challenging and rarely explored. Therefore, we propose multi-task joint
strategies of self-supervised representation learning on biomedical networks
for drug discovery, named MSSL2drug. We design six basic SSL tasks that are
inspired by various modality features including structures, semantics, and
attributes in biomedical heterogeneous networks. In addition, fifteen
combinations of …

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