A framework towards data analytics on host–pathogen protein–protein interactions

Chen, H; Shen, J; Wang, L; Song, J

A framework towards data analytics on host–pathogen protein–protein interactions

Chen, H Shen, J

Wang, L Song, J

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Publisher:: Springer Science and Business Media LLC
Publication Type:: Journal Article
Citation:: Journal of Ambient Intelligence and Humanized Computing, 2020, pp. 1-13
Issue Date:: 2020-01-01

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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	Song, J
dc.date.accessioned	2020-10-16T05:14:44Z
dc.date.available	2020-10-16T05:14:44Z
dc.date.issued	2020-01-01
dc.identifier.citation	Journal of Ambient Intelligence and Humanized Computing, 2020, pp. 1-13
dc.identifier.issn	1868-5137
dc.identifier.issn	1868-5145
dc.identifier.uri	http://hdl.handle.net/10453/143305
dc.description.abstract	© 2020, Springer-Verlag GmbH Germany, part of Springer Nature. With the rapid development of high-throughput technologies, systems biology is now embracing a great opportunity made possible by the increased accumulation of data available online. Biological data analytics is considered as a critical means to contribute to a better understanding on such data through extraction of the latent features, relationships and the associated mechanisms. Therefore, it is important to evaluate how to involve data analytics from both computational and biological perspectives in practice. This paper has investigated interaction relationships in the proteomics area, which provide insights of the critical molecular processes within infection mechanisms. Specifically, we focused on host–pathogen protein–protein interactions, which represented the primary challenges associated with infectious diseases and drug design. Accordingly, a novel framework based on data analytics and machine learning techniques is detailed for analyzing these areas and we will describe the analytical results from host–pathogen protein–protein interactions (HP-PPI). Based on this framework, which serves as a pipeline solution for extracting and learning from the raw proteomics data, we have firstly evaluated several models from literature using different analytic technologies and performance measurements. An unsupervised deep learning model based on stacked denoising autoencoders, is subsequently proposed to capture higher level feature regarding the sequence information in the framework. The achieved performance indicates a superior capability of the unsupervised deep learning model in dealing with the host–pathogen protein interactions scenario among all of these models. The results will further help to enrich a theoretical and technical foundation for analyzing HP-PPI networks.
dc.language	en
dc.publisher	Springer Science and Business Media LLC
dc.relation.ispartof	Journal of Ambient Intelligence and Humanized Computing
dc.relation.isbasedon	10.1007/s12652-020-01715-7
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0805 Distributed Computing
dc.title	A framework towards data analytics on host–pathogen protein–protein interactions
dc.type	Journal Article
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0805 Distributed Computing
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	open_access	*
utslib.copyright.embargo	2021-11-01T00:00:00+1000Z
dc.date.updated	2020-10-16T05:14:26Z
pubs.publication-status	Published

Abstract:

© 2020, Springer-Verlag GmbH Germany, part of Springer Nature. With the rapid development of high-throughput technologies, systems biology is now embracing a great opportunity made possible by the increased accumulation of data available online. Biological data analytics is considered as a critical means to contribute to a better understanding on such data through extraction of the latent features, relationships and the associated mechanisms. Therefore, it is important to evaluate how to involve data analytics from both computational and biological perspectives in practice. This paper has investigated interaction relationships in the proteomics area, which provide insights of the critical molecular processes within infection mechanisms. Specifically, we focused on host–pathogen protein–protein interactions, which represented the primary challenges associated with infectious diseases and drug design. Accordingly, a novel framework based on data analytics and machine learning techniques is detailed for analyzing these areas and we will describe the analytical results from host–pathogen protein–protein interactions (HP-PPI). Based on this framework, which serves as a pipeline solution for extracting and learning from the raw proteomics data, we have firstly evaluated several models from literature using different analytic technologies and performance measurements. An unsupervised deep learning model based on stacked denoising autoencoders, is subsequently proposed to capture higher level feature regarding the sequence information in the framework. The achieved performance indicates a superior capability of the unsupervised deep learning model in dealing with the host–pathogen protein interactions scenario among all of these models. The results will further help to enrich a theoretical and technical foundation for analyzing HP-PPI networks.

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http://hdl.handle.net/10453/143305