Webb15 jan. 2024 · We propose a specific knowledge graph embedding model, TriModel, to learn vector representations (i.e. embeddings) for all drugs and targets in the created … Webb19 maj 2024 · In this work, we have represented each protein as a graph and employed different graph neural networks (GCN and GAT), which operate on these graph …
Drug target discovery using knowledge graph embeddings
Webb13 juli 2024 · Here, we explore whether protein knowledge graphs can be used to identify genes that are targeted by disease-associated non-coding SNPs by testing and … Webb10 apr. 2024 · The Saccharomyces cerevisiae Agp2 is a plasma membrane protein initially reported to be an uptake transporter for L-carnitine. Agp2 was later rediscovered, together with three additional proteins, Sky1, Ptk2, and Brp1, to be involved in the uptake of the polyamine analogue bleomycin-A5, an anticancer drug. Mutants lacking either Agp2, … burning rome st paul
Toward better drug discovery with knowledge graph - ScienceDirect
Webb19 okt. 2024 · It was developed to enable benchmarking of ML algorithms. Drug discovery BioKG [253] A KG that integrates information about genes, proteins, diseases, drugs, and other biological entities. It aims ... Webb4 okt. 2024 · We then combined the HGCN with a one-dimensional convolutional network to construct a complete model for predicting compound-protein interactions. Furthermore we apply an explanation technique, Grad-CAM, to visualize the contribution of each amino acid into the prediction. Results Experiments using different datasets show the … Webb1 jan. 2024 · In recent years, several knowledge graph-based semantic similarity measures have been developed, but building a gold standard data set to support their evaluation is non-trivial. We present a collection of 21 benchmark data sets that aim at circumventing the difficulties in building benchmarks for large biomedical knowledge graphs by … burning room