CROssBAR: comprehensive resource of biomedical relations with knowledge graph representations

Autor: Tunca Doğan, Heval Atas, Ahmet Sureyya Rifaioglu, Maria Jesus Martin, Rabie Saidi, Rengul Cetin-Atalay, Hermann Zellner, Volkan Atalay, Vishal Joshi, Andrew Nightingale, Esra Nalbat, Ahmet Atakan, Vladimir Volynkin
Přispěvatelé: Mühendislik ve Doğa Bilimleri Fakültesi -- Elektrik-Elektronik Mühendisliği Bölümü, Rifaioğlu, Ahmet Süreyya
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Data base
Enzyme mechanism
AcademicSubjects/SCI00010
Signal transduction
NoSQL
computer.software_genre
SPARQL
0302 clinical medicine
Resource (project management)
Databases
Genetic
Computational methods
Protein analysis
Narese/8
Narese/7
0303 health sciences
Biological data
Molecular interaction
Genomics
Genetic parameters
Narese/24
030220 oncology & carcinogenesis
Methods Online
Heterogeneous network
Data integration
Human
Biochemistry & Molecular Biology
Relation (database)
Biology
Semantic Similarity
Miscellaneous/other
03 medical and health sciences
Medical research
CROssBAR database
Genetics
Humans
Access to information
Representation (mathematics)
030304 developmental biology
Computational Biology
Deep learning
Nonhuman
Data science
Gene Ontology
NoSQL database
Crossbar switch
Prediction
computer
Gene function
Databases
Chemical
Software
Zdroj: Nucleic Acids Research
Popis: Systemic analysis of available large-scale biological/biomedical data is critical for studying biological mechanisms, and developing novel and effective treatment approaches against diseases. However, different layers of the available data are produced using different technologies and scattered across individual computational resources without any explicit connections to each other, which hinders extensive and integrative multi-omics-based analysis. We aimed to address this issue by developing a new data integration/representation methodology and its application by constructing a biological data resource. CROssBAR is a comprehensive system that integrates large-scale biological/biomedical data from various resources and stores them in a NoSQL database. CROssBAR is enriched with the deep-learning-based prediction of relationships between numerous data entries, which is followed by the rigorous analysis of the enriched data to obtain biologically meaningful modules. These complex sets of entities and relationships are displayed to users via easy-to-interpret, interactive knowledge graphs within an open-access service. CROssBAR knowledge graphs incorporate relevant genes-proteins, molecular interactions, pathways, phenotypes, diseases, as well as known/predicted drugs and bioactive compounds, and they are constructed on-the-fly based on simple non-programmatic user queries. These intensely processed heterogeneous networks are expected to aid systems-level research, especially to infer biological mechanisms in relation to genes, proteins, their ligands, and diseases.
Databáze: OpenAIRE
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