LncRNA-disease association prediction based on neighborhood information aggregation in neural network

Chen, H; Wang, X; Zhang, X; Zeng, X; Song, T; Rodriguez-Paton, A

LncRNA-disease association prediction based on neighborhood information aggregation in neural network

Chen, H Wang, X Zhang, X

Zeng, X Song, T Rodriguez-Paton, A

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018, 2019, pp. 175-178
Issue Date:: 2019-01-21

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Field	Value	Language
dc.contributor.author	Chen, H
dc.contributor.author	Wang, X
dc.contributor.author	Zhang, X https://orcid.org/0000-0002-3089-9809
dc.contributor.author	Zeng, X
dc.contributor.author	Song, T
dc.contributor.author	Rodriguez-Paton, A
dc.date	2018-12-03
dc.date.accessioned	2020-06-17T19:47:29Z
dc.date.available	2020-06-17T19:47:29Z
dc.date.issued	2019-01-21
dc.identifier.citation	Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018, 2019, pp. 175-178
dc.identifier.isbn	9781538654880
dc.identifier.uri	http://hdl.handle.net/10453/141496
dc.description.abstract	© 2018 IEEE. Recent studies have demonstrated that IncRNAs play pivotal roles in various biological processes. Some computational methods have been developed to infer IncRNA-disease associations. However, the experimental identification is time-consuming. In this paper, we introduce a method named NNHLDA, which is based on neighborhood information aggregation in neural network. NNHLDA outperforms previous methods at IncRNA-disease association prediction in heterogeneous network. To evaluate our method, we conduct several experiments. In leave-one-out cross-validation (LOOCV) experiments, our NNHLDA method performed better than current state-of-the-art approach. Furthermore, we extracted top 100 IncRNA-disease associations identified by our method and conducted case studies on gastric cancer. The predictions have been confirmed by verified experimental results. Therefore, it is anticipated that NNHLDA could be a useful tool for biomedical researches.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
dc.relation.ispartof	IEEE International Conference on Bioinformatics and Biomedicine
dc.relation.isbasedon	10.1109/BIBM.2018.8621441
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/restrictedAccess
dc.title	LncRNA-disease association prediction based on neighborhood information aggregation in neural network
dc.type	Conference Proceeding
utslib.location.activity	Madrid, Spain
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2020-06-17T19:47:21Z
pubs.finish-date	2018-12-06
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2018-12-03
dc.location	Piscataway, USA

Abstract:

© 2018 IEEE. Recent studies have demonstrated that IncRNAs play pivotal roles in various biological processes. Some computational methods have been developed to infer IncRNA-disease associations. However, the experimental identification is time-consuming. In this paper, we introduce a method named NNHLDA, which is based on neighborhood information aggregation in neural network. NNHLDA outperforms previous methods at IncRNA-disease association prediction in heterogeneous network. To evaluate our method, we conduct several experiments. In leave-one-out cross-validation (LOOCV) experiments, our NNHLDA method performed better than current state-of-the-art approach. Furthermore, we extracted top 100 IncRNA-disease associations identified by our method and conducted case studies on gastric cancer. The predictions have been confirmed by verified experimental results. Therefore, it is anticipated that NNHLDA could be a useful tool for biomedical researches.

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http://hdl.handle.net/10453/141496