BiblioEngine: An AI-Empowered Platform for Disease Genetic Knowledge Mining

Wu, M; Zhang, Y; Lin, H; Grosser, M; Zhang, G; Lu, J

BiblioEngine: An AI-Empowered Platform for Disease Genetic Knowledge Mining

Wu, M

Zhang, Y

Lin, H Grosser, M Zhang, G

Lu, J

Permalink

Publisher:: Springer Nature
Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2023, 14305 LNCS, pp. 187-198
Issue Date:: 2023-01-01

Closed Access

	Filename	Description	Size
	978-981-99-7108-4_16.pdf		3.48 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Wu, M https://orcid.org/0000-0003-3956-7808
dc.contributor.author	Zhang, Y https://orcid.org/0000-0002-7731-0301
dc.contributor.author	Lin, H
dc.contributor.author	Grosser, M
dc.contributor.author	Zhang, G https://orcid.org/0000-0003-3960-0583
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732
dc.date.accessioned	2023-11-23T02:58:24Z
dc.date.available	2023-11-23T02:58:24Z
dc.date.issued	2023-01-01
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2023, 14305 LNCS, pp. 187-198
dc.identifier.isbn	9789819971077
dc.identifier.issn	0302-9743
dc.identifier.issn	1611-3349
dc.identifier.uri	http://hdl.handle.net/10453/173519
dc.description.abstract	Recent decades have seen significant advancements in contemporary genetic research with the aid of artificial intelligence (AI) techniques. However, researchers lack a comprehensive platform for fully exploiting these AI tools and conducting customized analyses. This paper introduces BiblioEngine, a literature analysis platform that helps researchers profile the research landscape and gain genetic insights into diseases. BiblioEngine integrates multiple AI-empowered data sources and employs heterogeneous network analysis to identify and emphasize genes and other biomedical entities for further investigation. Its effectiveness is demonstrated through a case study on stroke-related genetic research. Analysis with BiblioEngine uncovers valuable research intelligence and genetic insights. It provides a profile of leading research institutions and the knowledge landscape in the field. The gene co-occurrence map reveals frequent research of NOTCH3, prothrombotic factors, inflammatory cytokines, and other potential risk factors. The heterogeneous biomedical entity network analysis highlights infrequently studied genes and biomedical entities with potential significance for future stroke studies. In conclusion, BiblioEngine is a valuable tool enabling efficient navigation and comprehension of expanding biomedical knowledge from scientific literature, empowering researchers in their pursuit of disease-specific genetic knowledge.
dc.language	en
dc.publisher	Springer Nature
dc.relation	http://purl.org/au-research/grants/arc/LP210100414
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.relation.ispartofseries	Lecture Notes in Computer Science
dc.relation.isbasedon	10.1007/978-981-99-7108-4_16
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.classification	46 Information and computing sciences
dc.title	BiblioEngine: An AI-Empowered Platform for Disease Genetic Knowledge Mining
dc.type	Conference Proceeding
utslib.citation.volume	14305 LNCS
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/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2023-11-23T02:58:22Z
pubs.publication-status	Published
pubs.volume	14305 LNCS

Abstract:

Recent decades have seen significant advancements in contemporary genetic research with the aid of artificial intelligence (AI) techniques. However, researchers lack a comprehensive platform for fully exploiting these AI tools and conducting customized analyses. This paper introduces BiblioEngine, a literature analysis platform that helps researchers profile the research landscape and gain genetic insights into diseases. BiblioEngine integrates multiple AI-empowered data sources and employs heterogeneous network analysis to identify and emphasize genes and other biomedical entities for further investigation. Its effectiveness is demonstrated through a case study on stroke-related genetic research. Analysis with BiblioEngine uncovers valuable research intelligence and genetic insights. It provides a profile of leading research institutions and the knowledge landscape in the field. The gene co-occurrence map reveals frequent research of NOTCH3, prothrombotic factors, inflammatory cytokines, and other potential risk factors. The heterogeneous biomedical entity network analysis highlights infrequently studied genes and biomedical entities with potential significance for future stroke studies. In conclusion, BiblioEngine is a valuable tool enabling efficient navigation and comprehension of expanding biomedical knowledge from scientific literature, empowering researchers in their pursuit of disease-specific genetic knowledge.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/173519