The Clinical Knowledge Graph (CKG) is a graph database with millions of nodes and relationships representing experimental data, databases, and scientific literature. Further, CKG is an open-source platform that allows easy expansion with new databases and experimental data types and it includes statistical and machine learning tools for faster proteomics analysis.
We are currently working on a new version that will be more flexible and generalizable. Stay tuned!
Understanding host-parasite interactions is vital for tackling infectious diseases. Due to the challenges in experimental identification, we propose a computational method to predict protein interactions between human and 18 eukaryotic parasites. Our approach leverages an orthology-based transfer of interactions, focusing on parasite secretomes and human membrane proteins. We also filter the host proteome based on the parasites’ specific tissue tropisms. In this version, we added cell-type specific expression annotations to provide further resolution of the host-parasite predicted interactions and we support interactions with structural information.
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