Towards A More Effective Bidirectional LSTM-Based Learning Model for Human-Bacterium Protein-Protein Interactions

Chen, H; Shen, J; Wang, L; Jin, Y

Towards A More Effective Bidirectional LSTM-Based Learning Model for Human-Bacterium Protein-Protein Interactions

Chen, H Shen, J Wang, L Jin, Y

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Publisher:: Springer International Publishing
Publication Type:: Conference Proceeding
Citation:: Advances in Intelligent Systems and Computing, 2021, 1240 AISC, pp. 91-101
Issue Date:: 2021-01-01

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Field	Value	Language
dc.contributor.author	Chen, H
dc.contributor.author	Shen, J
dc.contributor.author	Wang, L
dc.contributor.author	Jin, Y
dc.date.accessioned	2022-06-28T01:24:19Z
dc.date.available	2022-06-28T01:24:19Z
dc.date.issued	2021-01-01
dc.identifier.citation	Advances in Intelligent Systems and Computing, 2021, 1240 AISC, pp. 91-101
dc.identifier.isbn	9783030545673
dc.identifier.issn	2194-5357
dc.identifier.issn	2194-5365
dc.identifier.uri	http://hdl.handle.net/10453/158420
dc.description.abstract	The identification of protein-protein interaction (PPI) is one of the most important tasks to understand the biological functions and disease mechanisms. Although numerous databases of biological interactions have been published in debt to advanced high-throughput technology, the study of inter-species protein-protein interactions, especially between human and bacterium pathogens, remains an active yet challenging topic to harness computational models tackling the complex analysis and prediction tasks. In this paper, we comprehensively revisit the prediction task of human-bacterium protein-protein interactions (HB-PPI), which is a first ever endeavour to report an empirical evaluation in learning and predicting HB-PPI based on machine learning models. Firstly, we summarise the literature review of human-bacterium interaction (HBI) study, meanwhile a vast number of databases published in the last decades are carefully examined. Secondly, a broader and deeper experimental framework is designed for HB-PPI prediction task, which explores a variety of feature representation algorithms and different computational models to learn from the curated HB-PPI dataset and perform predictions. Furthermore, a bidirectional LSTM-based model is proposed for the prediction task, which demonstrates a more effective performance in comparison with the others. Finally, opportunities for improving the performance and robustness of machine learning models for HP-PPI prediction are also discussed, laying a foundation for future work.
dc.language	en
dc.publisher	Springer International Publishing
dc.relation.ispartof	Advances in Intelligent Systems and Computing
dc.relation.isbasedon	10.1007/978-3-030-54568-0_10
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Towards A More Effective Bidirectional LSTM-Based Learning Model for Human-Bacterium Protein-Protein Interactions
dc.type	Conference Proceeding
utslib.citation.volume	1240 AISC
pubs.organisational-group	/University of Technology Sydney
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
utslib.copyright.status	closed_access	*
dc.date.updated	2022-06-28T01:24:17Z
pubs.publication-status	Published
pubs.volume	1240 AISC

Abstract:

The identification of protein-protein interaction (PPI) is one of the most important tasks to understand the biological functions and disease mechanisms. Although numerous databases of biological interactions have been published in debt to advanced high-throughput technology, the study of inter-species protein-protein interactions, especially between human and bacterium pathogens, remains an active yet challenging topic to harness computational models tackling the complex analysis and prediction tasks. In this paper, we comprehensively revisit the prediction task of human-bacterium protein-protein interactions (HB-PPI), which is a first ever endeavour to report an empirical evaluation in learning and predicting HB-PPI based on machine learning models. Firstly, we summarise the literature review of human-bacterium interaction (HBI) study, meanwhile a vast number of databases published in the last decades are carefully examined. Secondly, a broader and deeper experimental framework is designed for HB-PPI prediction task, which explores a variety of feature representation algorithms and different computational models to learn from the curated HB-PPI dataset and perform predictions. Furthermore, a bidirectional LSTM-based model is proposed for the prediction task, which demonstrates a more effective performance in comparison with the others. Finally, opportunities for improving the performance and robustness of machine learning models for HP-PPI prediction are also discussed, laying a foundation for future work.

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http://hdl.handle.net/10453/158420