Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/132127
Citations
Scopus Web of Science® Altmetric
?
?
Full metadata record
DC FieldValueLanguage
dc.contributor.authorChen, H.-
dc.contributor.authorLi, F.-
dc.contributor.authorSun, G.-
dc.contributor.authorZhang, X.-
dc.contributor.authorDong, X.-
dc.contributor.authorWang, L.-
dc.contributor.authorLiao, K.-
dc.contributor.authorShen, H.-
dc.contributor.authorShen, J.-
dc.date.issued2020-
dc.identifier.citationProceedxings of the 13th IEEE International Conference on Services Computing (SCC 2020), 2020, pp.463-465-
dc.identifier.isbn9781728187891-
dc.identifier.issn2474-8137-
dc.identifier.issn2474-2473-
dc.identifier.urihttps://hdl.handle.net/2440/132127-
dc.description.abstractThe booming of proteomics data has positioned multiple disciplines and research areas in a more complicated and challenging place. Moreover, the proteomics data of any defined research interests, such as for pathogenic mechanism studies of infectious diseases, have presented unstructured and heterogeneous characteristics. Thus, a service computing framework for proteomics analysis is desired to bring biologists and computer scientists into this area seamlessly and efficiently. With this regard, this work is dedicated to detail the proteomics analysis and collaboration process of pathogenic mechanism studies. We articulate this framework to serve the requirements and ease the task design by broadly reviewing the state-of-theart research and development efforts and collectively designing different informative stages. Thus, the framework has a focus of distilling different aspects, including data curation, resources distribution, standard construction and computational tasks identification, into the proteomics analysis. The framework is designed as Proteomics Analysis as a Service to deepen the understanding of the interdisciplinary research.-
dc.description.statementofresponsibilityHuaming Chen, Fucun Li, Geng Sun, Xuyun Zhang, Xianjun Dong, Lei Wang, Kewen Liao, Haifeng Shen, Jun Shen-
dc.language.isoen-
dc.publisherIEEE-
dc.relation.ispartofseriesIEEE International Conference on Services Computing SCC Proceedings-
dc.rights©2020 IEEE-
dc.source.urihttps://ieeexplore.ieee.org/xpl/conhome/9284357/proceeding-
dc.subjectservice computing; pathogenic mechanism; proteomics-
dc.titleA service computing framework for proteomics analysis and collaboration of pathogenic mechanism studies-
dc.typeConference paper-
dc.contributor.conferenceIEEE International Conference on Services Computing (SCC) (18 Oct 2020 - 24 Oct 2020 : virtual online)-
dc.identifier.doi10.1109/scc49832.2020.00069-
dc.publisher.placeonline-
pubs.publication-statusPublished-
dc.identifier.orcidChen, H. [0000-0001-5678-472X]-
Appears in Collections:Computer Science publications

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.