Figure 1.

Overview of DOTA. (A) Drug networks (drug–drug, drug–gene, drug–side-effects, drug–disease, and five other drug–drug similarities) are first converted into high-quality vector representation with a random walk-based procedure. (B) Next, the associations of the factorized co-occurrence matrixes are represented as PPMI matrixes. (C) The PPMI matrixes are then fused together into a low-dimensional feature representation using an unsupervised multimodal auto-encoder. (D) The optimal transport problem used in the second autoencoder part of DOTA. A Wasserstein loss function is used to minimize the optimal transport cost $W_C\left(P_X, P_G\right)$ between the input and output. (E) The drug information from the embedding layer of MAE is extracted and used as drug features to predict new drug-disease associations. A WVAE is used to encode and decode the drug-associations.