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CROssBAR: comprehensive resource of biomedical relations with knowledge graph representations.
Dogan, Tunca; Atas, Heval; Joshi, Vishal; Atakan, Ahmet; Rifaioglu, Ahmet Sureyya; Nalbat, Esra; Nightingale, Andrew; Saidi, Rabie; Volynkin, Vladimir; Zellner, Hermann; Cetin-Atalay, Rengul; Martin, Maria; Atalay, Volkan.
Afiliación
  • Dogan T; Department of Computer Engineering, Hacettepe University, Ankara 06800, Turkey.
  • Atas H; Institute of Informatics, Hacettepe University, Ankara 06800, Turkey.
  • Joshi V; Cancer Systems Biology Laboratory, Graduate School of Informatics, METU, Ankara 06800, Turkey.
  • Atakan A; European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire CB10 1SD, UK.
  • Rifaioglu AS; Cancer Systems Biology Laboratory, Graduate School of Informatics, METU, Ankara 06800, Turkey.
  • Nalbat E; European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire CB10 1SD, UK.
  • Nightingale A; Department of Computer Engineering, METU, Ankara 06800, Turkey.
  • Saidi R; Department of Computer Engineering, EBYU, Erzincan 24002, Turkey.
  • Volynkin V; Department of Computer Engineering, METU, Ankara 06800, Turkey.
  • Zellner H; Department of Computer Engineering, Iskenderun Technical University, Hatay 31200, Turkey.
  • Cetin-Atalay R; Cancer Systems Biology Laboratory, Graduate School of Informatics, METU, Ankara 06800, Turkey.
  • Martin M; European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire CB10 1SD, UK.
  • Atalay V; European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire CB10 1SD, UK.
Nucleic Acids Res ; 49(16): e96, 2021 09 20.
Article en En | MEDLINE | ID: mdl-34181736
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Programas Informáticos / Biología Computacional Límite: Humans Idioma: En Revista: Nucleic Acids Res Año: 2021 Tipo del documento: Article País de afiliación: Turquía Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
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XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Programas Informáticos / Biología Computacional Límite: Humans Idioma: En Revista: Nucleic Acids Res Año: 2021 Tipo del documento: Article País de afiliación: Turquía Pais de publicación: Reino Unido