Existing computational tools, resources, and services to assist experimental biomed-
ical research lack data diversity and data connectivity and therefore, they are limited
in helping to solve real world problems. Within the framework of CROssBAR project
aiming for drug discovery, CROssBAR database (CROssBAR-DB) integrated diverse
biomedical resources with the predictions of a comprehensive computing resource de-
veloped using machine learning and deep learning-based methods. It is crucial for the
users to perform easily search on CROssBAR-DB, and then to visualize the result of
the search. For this reason, CrossBAR web service (CROssBAR-WS) is designed, de-
veloped and implemented within the scope of this thesis. In CROssBAR-WS, knowl-
edge graphs can be generated by using the open-source graph library Cytoscape.js.
CROssBAR-WS is available at crossbar.kansil.org.