CROssBAR: Comprehensive Resource of Biomedical Relations with Network Representations and Deep Learning

Joshi, Vishal
Doğan, Tunca
Atalay, Rengül
Martin, Maria-Jesus
Saidi, Rabie
Zellner, Hermann
Volynkin, Vladimir
Sinoplu, Esra
Atas, Heval
Nightingale, Andrew
Rifaioğlu, Ahmet Süreyya
Atalay, Mehmet Volkan
Biomedical information is scattered across different biological data resources, which are biologically related but only loosely linked to each other in terms of data connections. This hinders the applications of integrative systems biology applications on data. We aim to develop a comprehensive resource, CROssBAR, to address these shortcomings by establishing relationships between relevant biological data sources to present a well-connected database, focusing on the fields of drug discovery and precision medicine. CROssBAR will contain 3 modules: (1) novel computational methods using graph theory and deep learning algorithms, to reveal unknown drug-target interactions and gene/protein-disease associations; (2) multi-partite biological networks where nodes will represent compounds/drugs, genes/proteins, pathways/systems and diseases, the edges will represent known and predicted pairwise relations in-between; and (3) an open access database and web-service to provide access to the resultant networks with its components. We have developed data pipelines for the heavy lifting of data from different data sources like UniProt, ChEMBL, PubChem, Drugbank and EFO persisting only specific data attributes for biomedical entity networks. The database is hosted in self-sufficient collections in MongoDB. The CROssBAR resource should help researchers in the interpretation of biomedical data by observing biological entities together with their relations.
Citation Formats
V. Joshi et al., “CROssBAR: Comprehensive Resource of Biomedical Relations with Network Representations and Deep Learning,” 2019, Accessed: 00, 2021. [Online]. Available: