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024 7 _ |a 10.15252/msb.202110387
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024 7 _ |a pmc:PMC8524328
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037 _ _ |a DZNE-2021-01564
041 _ _ |a English
082 _ _ |a 570
100 1 _ |a Ostaszewski, Marek
|0 0000-0003-1473-370X
|b 0
245 _ _ |a COVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms.
260 _ _ |a Heidelberg
|c 2021
|b EMBO Press
336 7 _ |a article
|2 DRIVER
336 7 _ |a Output Types/Journal article
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336 7 _ |a Journal Article
|b journal
|m journal
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336 7 _ |a ARTICLE
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336 7 _ |a JOURNAL_ARTICLE
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336 7 _ |a Journal Article
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500 _ _ |a (CC BY)
520 _ _ |a We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective.
536 _ _ |a 352 - Disease Mechanisms (POF4-352)
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650 _ 7 |a computable knowledge repository
|2 Other
650 _ 7 |a large-scale biocuration
|2 Other
650 _ 7 |a omics data analysis
|2 Other
650 _ 7 |a open access community effort
|2 Other
650 _ 7 |a systems biomedicine
|2 Other
650 _ 7 |a Antiviral Agents
|2 NLM Chemicals
650 _ 7 |a Cytokines
|2 NLM Chemicals
650 _ 7 |a Transcription Factors
|2 NLM Chemicals
650 _ 7 |a Viral Proteins
|2 NLM Chemicals
650 _ 2 |a COVID-19 Drug Treatment
|2 MeSH
650 _ 2 |a Antiviral Agents: therapeutic use
|2 MeSH
650 _ 2 |a COVID-19: drug therapy
|2 MeSH
650 _ 2 |a COVID-19: genetics
|2 MeSH
650 _ 2 |a COVID-19: immunology
|2 MeSH
650 _ 2 |a COVID-19: virology
|2 MeSH
650 _ 2 |a Computational Biology: methods
|2 MeSH
650 _ 2 |a Computer Graphics
|2 MeSH
650 _ 2 |a Cytokines: genetics
|2 MeSH
650 _ 2 |a Cytokines: immunology
|2 MeSH
650 _ 2 |a Data Mining: statistics & numerical data
|2 MeSH
650 _ 2 |a Databases, Factual
|2 MeSH
650 _ 2 |a Gene Expression Regulation
|2 MeSH
650 _ 2 |a Host Microbial Interactions: genetics
|2 MeSH
650 _ 2 |a Host Microbial Interactions: immunology
|2 MeSH
650 _ 2 |a Humans
|2 MeSH
650 _ 2 |a Immunity, Cellular: drug effects
|2 MeSH
650 _ 2 |a Immunity, Humoral: drug effects
|2 MeSH
650 _ 2 |a Immunity, Innate: drug effects
|2 MeSH
650 _ 2 |a Lymphocytes: drug effects
|2 MeSH
650 _ 2 |a Lymphocytes: immunology
|2 MeSH
650 _ 2 |a Lymphocytes: virology
|2 MeSH
650 _ 2 |a Metabolic Networks and Pathways: genetics
|2 MeSH
650 _ 2 |a Metabolic Networks and Pathways: immunology
|2 MeSH
650 _ 2 |a Myeloid Cells: drug effects
|2 MeSH
650 _ 2 |a Myeloid Cells: immunology
|2 MeSH
650 _ 2 |a Myeloid Cells: virology
|2 MeSH
650 _ 2 |a Protein Interaction Mapping
|2 MeSH
650 _ 2 |a SARS-CoV-2: drug effects
|2 MeSH
650 _ 2 |a SARS-CoV-2: genetics
|2 MeSH
650 _ 2 |a SARS-CoV-2: immunology
|2 MeSH
650 _ 2 |a SARS-CoV-2: pathogenicity
|2 MeSH
650 _ 2 |a Signal Transduction
|2 MeSH
650 _ 2 |a Software
|2 MeSH
650 _ 2 |a Transcription Factors: genetics
|2 MeSH
650 _ 2 |a Transcription Factors: immunology
|2 MeSH
650 _ 2 |a Viral Proteins: genetics
|2 MeSH
650 _ 2 |a Viral Proteins: immunology
|2 MeSH
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773 _ _ |a 10.15252/msb.202110387
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Marc 21