Challenges for big data in oncology

Bairagee, D; Jain, NK; Dwivedi, S; Dua, K

Challenges for big data in oncology

Bairagee, D Jain, NK Dwivedi, S Dua, K

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Publication Type:: Chapter
Citation:: Big Data in Oncology: Impact, Challenges, and Risk Assessment, 2023, pp. 411-444
Issue Date:: 2023-08-12

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Field	Value	Language
dc.contributor.author	Bairagee, D
dc.contributor.author	Jain, NK
dc.contributor.author	Dwivedi, S
dc.contributor.author	Dua, K https://orcid.org/0000-0002-7507-1159
dc.date.accessioned	2023-11-16T00:05:43Z
dc.date.available	2023-11-16T00:05:43Z
dc.date.issued	2023-08-12
dc.identifier.citation	Big Data in Oncology: Impact, Challenges, and Risk Assessment, 2023, pp. 411-444
dc.identifier.isbn	9788770228138
dc.identifier.uri	http://hdl.handle.net/10453/173433
dc.description.abstract	During this era of data innovation, big data research is making its way into the biological sciences. Big data's capacity to identify patterns and translate vast amounts of data into usable information for correct medical decision-making is determined by its capability to notice patterns and translate large capacities of data into valuable information for accurate medicine and decision-making. In a few cases, the usage of big data in medical services is now supplying answers for increasing patient consideration and the age of significant value in medical care associations. The fundamentals of big data are presented, with a focus on cancer. There is an enormous number of informative collections in oncology that collect data on malignant development genome, transcriptome, clinical information, neurotic, personal happiness information, and so on. When it comes to physical and biological data exchanges, radiotherapy data is unique. The authors examined developments and discussed current obstacles in using top-bottom and bottom-top approaches to cross-examine large data in radiotherapy in this chapter. They discussed the specifics of big data in radiotherapy and the challenges that bioinformatics tools for data aggregation, sharing, and privacy provided. The problem of big data in cancer is incorporating all of this disparate data into a unified platform that can be examined and translated into comprehensible files. The capability to extract info from all of the obtained data resulted in advancements in cancer patient treatment and resolution.
dc.language	en
dc.relation.ispartof	Big Data in Oncology: Impact, Challenges, and Risk Assessment
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Challenges for big data in oncology
dc.type	Chapter
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Health
utslib.copyright.status	closed_access	*
dc.date.updated	2023-11-16T00:05:42Z
pubs.publication-status	Published

Abstract:

During this era of data innovation, big data research is making its way into the biological sciences. Big data's capacity to identify patterns and translate vast amounts of data into usable information for correct medical decision-making is determined by its capability to notice patterns and translate large capacities of data into valuable information for accurate medicine and decision-making. In a few cases, the usage of big data in medical services is now supplying answers for increasing patient consideration and the age of significant value in medical care associations. The fundamentals of big data are presented, with a focus on cancer. There is an enormous number of informative collections in oncology that collect data on malignant development genome, transcriptome, clinical information, neurotic, personal happiness information, and so on. When it comes to physical and biological data exchanges, radiotherapy data is unique. The authors examined developments and discussed current obstacles in using top-bottom and bottom-top approaches to cross-examine large data in radiotherapy in this chapter. They discussed the specifics of big data in radiotherapy and the challenges that bioinformatics tools for data aggregation, sharing, and privacy provided. The problem of big data in cancer is incorporating all of this disparate data into a unified platform that can be examined and translated into comprehensible files. The capability to extract info from all of the obtained data resulted in advancements in cancer patient treatment and resolution.

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http://hdl.handle.net/10453/173433