enveda/kgem-ensembles-in-drug-discovery
Source code and data repository for "Ensembles of knowledge graph embedding models improve predictions for drug discovery"
This project helps drug discovery scientists improve their predictions for drug candidates by combining the outputs of multiple knowledge graph embedding models. It takes biomedical knowledge graphs as input and produces more precise rankings of potential drug-disease interactions. A computational biologist or pharmacologist would use this to get better insights from their drug discovery data.
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Use this if you need to identify promising drug candidates from complex biomedical knowledge graphs with higher precision than individual models.
Not ideal if your primary goal is to train a single, novel knowledge graph embedding model, rather than leveraging ensembles.
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Last pushed
Jan 17, 2024
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