scholar.google.com › citations
A graph called drug-drug interaction (DDI) is used to represent the drugs and effects of consuming one drug with other drugs. Additionally, information about ...
In this paper, we propose a graph encoding-enhanced transformer (GEET) to recommend drugs. The DDI and DCR graphs are encoded by using Graph Attention Network ...
In this paper, we propose a graph encoding-enhanced transformer (GEET) for drug recommendation. It uses a model called Graph Attention Network (GAT) as graph ...
It transforms drug linkage prediction research into a link prediction problem and views drug relationships with known interactions as edges in the interaction ...
Aug 29, 2024 · DrugFormer, a novel graph-augmented large language model designed to predict drug resistance at single-cell level is proposed.
The Transformer architecture has recently gained considerable attention in the field of graph rep- resentation learning, as it naturally overcomes.
Graph reasoning method enhanced by relational transformers and ...
www.cell.com › iscience › fulltext
We proposed a graph reasoning method, RKDSP, to fuse the semantics of multiple connection relationships, the local knowledge within each meta-path, the global ...
Nov 25, 2023 · In this study, we combined a molecular graph structure and sequential representations using a generative pretrained transformer (GPT) architecture for ...
Mar 28, 2024 · We propose a Transformer graph-based early fusion research approach for drug-target affinity prediction (GEFormerDTA).
Dec 1, 2021 · This paper proposes a deep learning model, GraTransDRP, to better drug representation and reduce information redundancy.
Missing: Recommendation. | Show results with:Recommendation.