RT Journal Article
SR Electronic
T1 CROssBAR: Comprehensive Resource of Biomedical Relations with Deep Learning Applications and Knowledge Graph Representations
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.09.14.296889
DO 10.1101/2020.09.14.296889
A1 Tunca Doğan
A1 Heval Atas
A1 Vishal Joshi
A1 Ahmet Atakan
A1 Ahmet Sureyya Rifaioglu
A1 Esra Nalbat
A1 Andrew Nightingale
A1 Rabie Saidi
A1 Vladimir Volynkin
A1 Hermann Zellner
A1 Rengul Cetin-Atalay
A1 Maria Martin
A1 Volkan Atalay
YR 2020
UL http://biorxiv.org/content/early/2020/09/15/2020.09.14.296889.abstract
AB Systemic analysis of available large-scale biological and biomedical data is critical for developing novel and effective treatment approaches against both complex and infectious diseases. Owing to the fact that different sections of the biomedical data is produced by different organizations/institutions using various types of technologies, the data are scattered across individual computational resources, without any explicit relations/connections to each other, which greatly hinders the comprehensive multi-omics-based analysis of data. We aimed to address this issue by constructing a new biological and biomedical data resource, CROssBAR, a comprehensive system that integrates large-scale biomedical data from various resources and store them in a new NoSQL database, enrich these data with deep-learning-based prediction of relations between numerous biomedical entities, rigorously analyse the enriched data to obtain biologically meaningful modules and display them to users via easy-to-interpret, interactive and heterogenous knowledge graph (KG) representations within an open access, user-friendly and online web-service at https://crossbar.kansil.org. As a use-case study, we constructed CROssBAR COVID-19 KGs (available at: https://crossbar.kansil.org/covid_main.php) that incorporate relevant virus and host genes/proteins, interactions, pathways, phenotypes and other diseases, as well as known and completely new predicted drugs/compounds. Our COVID-19 graphs can be utilized for a systems-level evaluation of relevant virus-host protein interactions, mechanisms, phenotypic implications and potential interventions.Competing Interest StatementThe authors have declared no competing interest.