Fig. 1: The schematic workflow of KGE_NFM. | Nature Communications

Fig. 1: The schematic workflow of KGE_NFM.

From: A unified drug–target interaction prediction framework based on knowledge graph and recommendation system

Fig. 1

The pipeline mainly consists of two parts. (1) The construction of KG and embeddings extraction. The original input contains the DTI data and related omics data, and the embeddings of entities and relations are extracted by DistMult. (2) The integration of multimodal information by NFM. The extracted KGEs represent the heterogeneous information, and the molecular fingerprints and protein descriptors represent the structural information. The two types of information are combined and optimized via the Bi-Interaction layer and a feed-forward neural network (FFNN) is used to capture the inherent correlations between DTI.

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