A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf
.
Implications of Topological Imbalance for Representation Learning on Biomedical Knowledge Graphs
[article]
2022
arXiv
pre-print
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
arXiv:2112.06567v2
fatcat:cfdwzkoy4rglflz2zpwvxjaoum