Machine Learning, COVID-19 (2019-nCoV), and multi-OMICS.
Identifieur interne : 000E04 ( Ncbi/Merge ); précédent : 000E03; suivant : 000E05Machine Learning, COVID-19 (2019-nCoV), and multi-OMICS.
Auteurs : Attila Tárnok [Allemagne]Source :
- Cytometry. Part A : the journal of the International Society for Analytical Cytology [ 1552-4930 ] ; 2020.
Descripteurs français
- KwdFr :
- MESH :
English descriptors
- KwdEn :
- MESH :
- genetics : Betacoronavirus.
- metabolism : Betacoronavirus.
- prevention & control : Coronavirus Infections, Pneumonia, Viral.
- transmission : Coronavirus Infections, Pneumonia, Viral.
- Genomics, Humans, Machine Learning.
DOI: 10.1002/cyto.a.23990
PubMed: 32142596
Links toward previous steps (curation, corpus...)
- to stream PubMed, to step Corpus: 000777
- to stream PubMed, to step Curation: 000777
- to stream PubMed, to step Checkpoint: 000552
Links to Exploration step
pubmed:32142596Le document en format XML
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Part A : the journal of the International Society for Analytical Cytology</Title><ISOAbbreviation>Cytometry A</ISOAbbreviation></Journal><ArticleTitle>Machine Learning, COVID-19 (2019-nCoV), and multi-OMICS.</ArticleTitle><Pagination><MedlinePgn>215-216</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1002/cyto.a.23990</ELocationID><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Tárnok</LastName><ForeName>Attila</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>Institute for Medical Informatics, Statistics and Epidemiology (IMISE), University of Leipzig, Leipzig, Germany.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Dept. Therapy Validation, Fraunhofer Institute for Cell Therapy and Immunology IZI, Leipzig, Germany.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Dept. for Precision Instrument, Tsinghua University, Beijing, China.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016421">Editorial</PublicationType></PublicationTypeList></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>Cytometry A</MedlineTA><NlmUniqueID>101235694</NlmUniqueID><ISSNLinking>1552-4922</ISSNLinking></MedlineJournalInfo><SupplMeshList><SupplMeshName Type="Disease" UI="C000657245">COVID-19</SupplMeshName><SupplMeshName Type="Organism" UI="C000656484">severe acute respiratory syndrome coronavirus 2</SupplMeshName></SupplMeshList><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000073640" MajorTopicYN="Y">Betacoronavirus</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="N">genetics</QualifierName><QualifierName UI="Q000378" MajorTopicYN="N">metabolism</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D018352" MajorTopicYN="Y">Coronavirus Infections</DescriptorName><QualifierName UI="Q000517" MajorTopicYN="N">prevention & control</QualifierName><QualifierName UI="Q000635" MajorTopicYN="N">transmission</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D023281" MajorTopicYN="N">Genomics</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000069550" MajorTopicYN="Y">Machine Learning</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D011024" MajorTopicYN="Y">Pneumonia, Viral</DescriptorName><QualifierName UI="Q000517" MajorTopicYN="N">prevention & control</QualifierName><QualifierName UI="Q000635" MajorTopicYN="N">transmission</QualifierName></MeshHeading></MeshHeadingList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>3</Month><Day>7</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>3</Month><Day>7</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>3</Month><Day>19</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">32142596</ArticleId><ArticleId IdType="doi">10.1002/cyto.a.23990</ArticleId></ArticleIdList><ReferenceList><Title>References</Title><Reference><Citation>Editorial: Method of the Year 2019: Single-cell multimodal omics. Nat Methods 2020;17(1):1. [No authors listed] Nat Methods 2020. PMID 31907477</Citation></Reference><Reference><Citation>Yang Yang, Qingbin Lu, Mingjin Liu, Yixing Wang, Anran Zhang, Neda Jalali, Natalie Dean, Ira Longini, M. Elizabeth Halloran, Bo Xu, Xiaoai Zhang, Liping Wang, Wei Liu, Liqun Fang. Epidemiological and clinical features of the 2019 novel coronavirus outbreak in China. doi: https://doi.org/10.1101/2020.02.10.20021675</Citation></Reference><Reference><Citation>Rosen O, Chan LL, Abiona OM, et al. A high-throughput inhibition assay to study MERS-CoV antibody interactions using image cytometry. J Virol Methods 2019;265:77-83.</Citation></Reference><Reference><Citation>Lee WM, Grindle K, Pappas T, Marshall DJ, Moser MJ, Beaty EL, Shult PA, Prudent JR, Gern JE. High-throughput, sensitive, and accurate multiplex PCR-microsphere flow cytometry system for large-scale comprehensive detection of respiratory viruses. 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Clin Infect Dis 2019;68(6):984-992.</Citation></Reference><Reference><Citation>He Z, Zhao C, Dong Q, Zhuang H, Song S, Peng G, Dwyer DE. Effects of severe acute respiratory syndrome (SARS) coronavirus infection on peripheral blood lymphocytes and their subsets. Int J Infect Dis 2005;9(6):323-330.</Citation></Reference></ReferenceList></PubmedData></pubmed><affiliations><list><country><li>Allemagne</li></country><region><li>District de Leipzig</li><li>Saxe (Land)</li></region><settlement><li>Leipzig</li></settlement></list><tree><country name="Allemagne"><region name="Saxe (Land)"><name sortKey="Tarnok, Attila" sort="Tarnok, Attila" uniqKey="Tarnok A" first="Attila" last="Tárnok">Attila Tárnok</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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