Akademik Veri Yönetim Sistemi
27th Conference on Intelligent Systems for Molecular Biology and 18th European Conference on Computational Biology, Basel, İsviçre, 21 - 25 Temmuz 2019
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.