Artificial intelligence-driven biomedical genomics

Guo, K; Wu, M; Soo, Z; Yang, Y; Zhang, Y; Zhang, Q; Lin, H; Grosser, M; Venter, D; Zhang, G; Lu, J

Artificial intelligence-driven biomedical genomics

Guo, K

Wu, M

Soo, Z Yang, Y Zhang, Y

Zhang, Q Lin, H Grosser, M Venter, D Zhang, G

Lu, J

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Publisher:: ELSEVIER
Publication Type:: Journal Article
Citation:: Knowledge-Based Systems, 2023, 279
Issue Date:: 2023-11-04

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Field	Value	Language
dc.contributor.author	Guo, K https://orcid.org/0000-0003-1570-314X
dc.contributor.author	Wu, M https://orcid.org/0000-0003-3956-7808
dc.contributor.author	Soo, Z
dc.contributor.author	Yang, Y
dc.contributor.author	Zhang, Y https://orcid.org/0000-0002-7731-0301
dc.contributor.author	Zhang, Q
dc.contributor.author	Lin, H
dc.contributor.author	Grosser, M
dc.contributor.author	Venter, D
dc.contributor.author	Zhang, G https://orcid.org/0000-0003-3960-0583
dc.contributor.author	Lu, J
dc.date.accessioned	2024-01-16T03:08:38Z
dc.date.available	2024-01-16T03:08:38Z
dc.date.issued	2023-11-04
dc.identifier.citation	Knowledge-Based Systems, 2023, 279
dc.identifier.issn	0950-7051
dc.identifier.issn	1872-7409
dc.identifier.uri	http://hdl.handle.net/10453/174558
dc.description.abstract	As genomic research becomes more complex and data-rich, artificial intelligence (AI) has emerged as a crucial tool for processing and analyzing high-dimensional genomic data, accelerating biomarker discovery, and enhancing genomic sequence annotations. Despite the increasing application of AI in genomic research, challenges persist, particularly regarding the integration of biomedical knowledge into algorithm development. We reviewed high-quality, AI-driven biomedical genomic studies from the past five years, covering applications in disease prediction, detection, diagnosis, and treatment. Each category highlights how different AI techniques are applied in biomedical contexts. Furthermore, we identify current challenges and potential solutions in AI-assisted biomedical genomics. This comprehensive review is designed to encourage collaboration among computer scientists, healthcare professionals, and interested communities, propelling the development of AI applications that can be smoothly integrated into routine medical services.
dc.language	English
dc.publisher	ELSEVIER
dc.relation	http://purl.org/au-research/grants/arc/LP210100414
dc.relation.ispartof	Knowledge-Based Systems
dc.relation.isbasedon	10.1016/j.knosys.2023.110937
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences, 15 Commerce, Management, Tourism and Services, 17 Psychology and Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.classification	4602 Artificial intelligence
dc.subject.classification	4605 Data management and data science
dc.subject.classification	4611 Machine learning
dc.title	Artificial intelligence-driven biomedical genomics
dc.type	Journal Article
utslib.citation.volume	279
utslib.for	08 Information and Computing Sciences
utslib.for	15 Commerce, Management, Tourism and Services
utslib.for	17 Psychology and Cognitive Sciences
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	2024-01-16T03:08:37Z
pubs.publication-status	Published
pubs.volume	279

Abstract:

As genomic research becomes more complex and data-rich, artificial intelligence (AI) has emerged as a crucial tool for processing and analyzing high-dimensional genomic data, accelerating biomarker discovery, and enhancing genomic sequence annotations. Despite the increasing application of AI in genomic research, challenges persist, particularly regarding the integration of biomedical knowledge into algorithm development. We reviewed high-quality, AI-driven biomedical genomic studies from the past five years, covering applications in disease prediction, detection, diagnosis, and treatment. Each category highlights how different AI techniques are applied in biomedical contexts. Furthermore, we identify current challenges and potential solutions in AI-assisted biomedical genomics. This comprehensive review is designed to encourage collaboration among computer scientists, healthcare professionals, and interested communities, propelling the development of AI applications that can be smoothly integrated into routine medical services.

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http://hdl.handle.net/10453/174558