Polypharmcy decagon. The constructed KG has been widely used in later research.

Polypharmcy decagon. We construct a multimodal graph of protein-protein interactions, drug-protein target interactions, and polypharmacy side effects, which are represented as drug-drug interactions, where each side effect is an edge of a different type. Here, we present Decagon, an approach for modeling polypharmacy side effects. Decagon uses graph convolutions to embed Jul 1, 2018 · Results: Here, we present Decagon, an approach for modeling polypharmacy side effects. com Recently, Zitnik et. The approach constructs a multimodal graph of protein-protein interactions, drug-protein target interactions and the polypharmacy side effects, which are represented as drug-drug interactions, where each side effect is an edge of a different type. The constructed KG has been widely used in later research. In this work, we propose a new knowledge graph embedding technique that uses multi-part embedding vectors to predict polypharmacy side-effects. May 18, 2018 · Decagon accurately predicts polypharmacy side effects, outperforming baselines by up to 69%. propose KnowDDI, a graph neural network that leverages biomedical knowledge graphs for drug-drug interaction predictions. Decagon predicts associations between pairs of drugs and side effects (shown in red) with the goal of identifying side effects, which cannot be attributed to either individual drug in the pair. jekx nmaxh ty spbn h9tew rhp vhbn x2wur rx 6hxg