Towards Biological Sequence Data Service with Insights

Chen, H; Shen, J; Wang, L; Chi, CH

Towards Biological Sequence Data Service with Insights

Chen, H Shen, J

Wang, L Chi, CH

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018, 2019, pp. 2847-2854
Issue Date:: 2019-01-22

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Field	Value	Language
dc.contributor.author	Chen, H
dc.contributor.author	Shen, J https://orcid.org/0000-0002-9403-7140
dc.contributor.author	Wang, L
dc.contributor.author	Chi, CH
dc.contributor.editor	Abe, N
dc.contributor.editor	Liu, H
dc.contributor.editor	Pu, C
dc.contributor.editor	Hu, X
dc.contributor.editor	Ahmed, N
dc.contributor.editor	Qiao, M
dc.contributor.editor	Song, Y
dc.contributor.editor	Kossmann, D
dc.contributor.editor	Liu, B
dc.contributor.editor	Lee, K
dc.contributor.editor	Tang, J
dc.contributor.editor	He, J
dc.contributor.editor	Saltz, J
dc.date	2013-12-10
dc.date.accessioned	2020-05-22T05:43:30Z
dc.date.available	2020-05-22T05:43:30Z
dc.date.issued	2019-01-22
dc.identifier.citation	Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018, 2019, pp. 2847-2854
dc.identifier.isbn	9781538650356
dc.identifier.issn	2639-1589
dc.identifier.uri	http://hdl.handle.net/10453/140888
dc.description.abstract	© 2018 IEEE. Testable prediction outcomes generated by computational models based on available databases are the primary sources helping to design biological experiments. Although numerous databases have been designed by collecting data either only from literature manually or together with prediction outcomes from computational models, there is currently not a comprehensive data service framework delivering better insights for these results. In this paper, we introduce a biological sequence data service towards delivering deeper insights and helping better biological experiments design. The service includes following major components: a comprehensive database for storing biological data, data analytics tools for analysing biological data, and computational models for delivering testable prediction outcomes. Specifically, we present this service in a framework for studies on host-pathogen interactions. The design of this framework aims to improve the understanding of host-pathogen interactions. The relationships of hierarchical databases and their working mechanism, specifically between PPIs and DDIs, are also presented in this framework. Finally, the preliminary and practical experiences of building computational model for prediction is discussed.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018
dc.relation.ispartof	IEEE International Conference on Big Data (Big Data)
dc.relation.isbasedon	10.1109/BigData.2018.8622194
dc.rights	© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Towards Biological Sequence Data Service with Insights
dc.type	Conference Proceeding
utslib.location.activity	USA
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2020-05-22T05:43:27Z
pubs.finish-date	2013-12-13
pubs.publication-status	Published
pubs.start-date	2013-12-10
utslib.start-page	2847

Abstract:

© 2018 IEEE. Testable prediction outcomes generated by computational models based on available databases are the primary sources helping to design biological experiments. Although numerous databases have been designed by collecting data either only from literature manually or together with prediction outcomes from computational models, there is currently not a comprehensive data service framework delivering better insights for these results. In this paper, we introduce a biological sequence data service towards delivering deeper insights and helping better biological experiments design. The service includes following major components: a comprehensive database for storing biological data, data analytics tools for analysing biological data, and computational models for delivering testable prediction outcomes. Specifically, we present this service in a framework for studies on host-pathogen interactions. The design of this framework aims to improve the understanding of host-pathogen interactions. The relationships of hierarchical databases and their working mechanism, specifically between PPIs and DDIs, are also presented in this framework. Finally, the preliminary and practical experiences of building computational model for prediction is discussed.

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http://hdl.handle.net/10453/140888