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Implications of Topological Imbalance for Representation Learning on Biomedical Knowledge Graphs. (arXiv:2112.06567v2 [cs.LG] UPDATED)
March 21, 2022, 1:12 a.m. | Stephen Bonner, Ufuk Kirik, Ola Engkvist, Jian Tang, Ian P Barrett
cs.LG updates on arXiv.org arxiv.org
Adoption of recently developed methods from machine learning has given rise
to creation of drug-discovery knowledge graphs (KG) that utilize the
interconnected nature of the domain. Graph-based modelling of the data,
combined with KG embedding (KGE) methods, are promising as they provide a more
intuitive representation and are suitable for inference tasks such as
predicting missing links. One common application is to produce ranked lists of
genes for a given disease, where the rank is based on the perceived likelihood …
arxiv graphs knowledge knowledge graphs learning representation representation learning
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