Figure 1
The general flowchart of MCMDA, which is mainly composed of two steps: (Step1) The feature representation learning modular learns miRNA and disease latent representations with similarity information and the known associations as input. (Step2) Prediction modular yields multicategory association scores. Totally, MCMDA is formulated as a tensor completion problem with side information.

The general flowchart of MCMDA, which is mainly composed of two steps: (Step1) The feature representation learning modular learns miRNA and disease latent representations with similarity information and the known associations as input. (Step2) Prediction modular yields multicategory association scores. Totally, MCMDA is formulated as a tensor completion problem with side information.

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