DEEP LEARNING-CANCER GENETICS AND APPLICATION OF DEEP LEARNING TO CANCER ONCOLOGY

Hoang, DB; Hoang, S

DEEP LEARNING-CANCER GENETICS AND APPLICATION OF DEEP LEARNING TO CANCER ONCOLOGY

Hoang, DB Hoang, S

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Publisher:: Publishing House for Science and Technology, Vietnam Academy of Science and Technology (Publications)
Publication Type:: Journal Article
Citation:: Vietnam Journal of Science and Technology, 2022, 60, (6), pp. 885-928
Issue Date:: 2022-12-30

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Field	Value	Language
dc.contributor.author	Hoang, DB
dc.contributor.author	Hoang, S
dc.date.accessioned	2023-03-21T22:24:15Z
dc.date.available	2023-03-21T22:24:15Z
dc.date.issued	2022-12-30
dc.identifier.citation	Vietnam Journal of Science and Technology, 2022, 60, (6), pp. 885-928
dc.identifier.issn	2525-2518
dc.identifier.issn	2815-5874
dc.identifier.uri	http://hdl.handle.net/10453/167984
dc.description.abstract	Arguably the human body has been one of the most sophisticated systems we encounter but until now we are still far from understanding its complexity. We have been trying to replicate human intelligence by way of artificial intelligence but with limited success. We have discovered the molecular structure in terms of genetics, performed gene editing to change an organism’s DNA and much more, but their translatability into the field of oncology has remained limited. Conventional machine learning methods achieved some degree of success in solving problems for which we do not have an explicit algorithm. However, they are basically shallow learning methods, not rich enough to discover and extract intricate features that represent patterns in the real environment. Deep learning has exceeded human performance in pattern recognition as well as strategic games and are powerful for dealing with many complex problems. High-throughput sequencing and microarray techniques have generated vast amounts of data and allowed the comprehensive study of gene expression in tumor cells. The application of deep learning with molecular data enables applications in oncology with information not available from clinical diagnosis. This paper provides an overview of the fundamental concepts of deep learning, some essential knowledge of cancer genetics, and a review of applications of deep learning to cancer oncology. Importantly, it provides an insightful knowledge of deep learning and an extensive discussion on its challenges. The ultimate purpose is to stimulate ideas and facilitate collaborations between cancer biologists and deep learning researchers to address challenging oncological problems using advanced deep learning technologies.
dc.language	en
dc.publisher	Publishing House for Science and Technology, Vietnam Academy of Science and Technology (Publications)
dc.relation.ispartof	Vietnam Journal of Science and Technology
dc.relation.isbasedon	10.15625/2525-2518/17256
dc.rights	info:eu-repo/semantics/openAccess
dc.title	DEEP LEARNING-CANCER GENETICS AND APPLICATION OF DEEP LEARNING TO CANCER ONCOLOGY
dc.type	Journal Article
utslib.citation.volume	60
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 Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Centre for Health Technologies (CHT)
utslib.copyright.status	open_access	*
dc.date.updated	2023-03-21T22:24:14Z
pubs.issue	6
pubs.publication-status	Published
pubs.volume	60
utslib.citation.issue	6

Abstract:

Arguably the human body has been one of the most sophisticated systems we encounter but until now we are still far from understanding its complexity. We have been trying to replicate human intelligence by way of artificial intelligence but with limited success. We have discovered the molecular structure in terms of genetics, performed gene editing to change an organism’s DNA and much more, but their translatability into the field of oncology has remained limited. Conventional machine learning methods achieved some degree of success in solving problems for which we do not have an explicit algorithm. However, they are basically shallow learning methods, not rich enough to discover and extract intricate features that represent patterns in the real environment. Deep learning has exceeded human performance in pattern recognition as well as strategic games and are powerful for dealing with many complex problems. High-throughput sequencing and microarray techniques have generated vast amounts of data and allowed the comprehensive study of gene expression in tumor cells. The application of deep learning with molecular data enables applications in oncology with information not available from clinical diagnosis. This paper provides an overview of the fundamental concepts of deep learning, some essential knowledge of cancer genetics, and a review of applications of deep learning to cancer oncology. Importantly, it provides an insightful knowledge of deep learning and an extensive discussion on its challenges. The ultimate purpose is to stimulate ideas and facilitate collaborations between cancer biologists and deep learning researchers to address challenging oncological problems using advanced deep learning technologies.

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http://hdl.handle.net/10453/167984