  Most phenomena related to biomedical tasks are inherently complex, and in
many cases, are expressed as signals on biomedical Knowledge Graphs (KGs). In
this work, we introduce the use of a new representation framework, the Prime
Adjacency Matrix (PAM) for biomedical KGs, which allows for very efficient
network analysis. PAM utilizes prime numbers to enable representing the whole
KG with a single adjacency matrix and the fast computation of multiple
properties of the network. We illustrate the applicability of the framework in
the biomedical domain by working on different biomedical knowledge graphs and
by providing two case studies: one on drug-repurposing for COVID-19 and one on
important metapath extraction. We show that we achieve better results than the
original proposed workflows, using very simple methods that require no
training, in considerably less time.
