Biomedical Big Data Technologies, Applications, and Challenges for Precision Medicine: A Review
Abstract: The explosive growth of biomedical Big Data presents both significant opportunities and challenges in the realm of knowledge discovery and translational applications within precision medicine. Efficient management, analysis, and interpretation of big data can pave the way for groundbreaking advancements in precision medicine. However, the unprecedented strides in the automated collection of large‐scale molecular and clinical data have also introduced formidable challenges in terms of data analysis and interpretation, necessitating the development of novel computational approaches. Some potential challenges include the curse of dimensionality, data heterogeneity, missing data, class imbalance, and scalability issues. This overview article focuses on the recent progress and breakthroughs in the application of big data within precision medicine. Key aspects are summarized, including content, data sources, technologies, tools, challenges, and existing gaps. Nine fields—Datawarehouse and data management, electronic medical record, biomedical imaging informatics, Artificial intelligence‐aided surgical design and surgery optimization, omics data, health monitoring data, knowledge graph, public health informatics, and security and privacy—are discussed.
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Erschienen in
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Biomedical Big Data Technologies, Applications, and Challenges for Precision Medicine: A Review ; day:20 ; month:11 ; year:2023 ; extent:21
Global challenges ; (20.11.2023) (gesamt 21)
- Urheber
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Yang, Xue
Huang, Kexin
Yang, Dewei
Zhao, Weiling
Zhou, Xiaobo
- DOI
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10.1002/gch2.202300163
- URN
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urn:nbn:de:101:1-2023112114142891428408
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
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15.08.2025, 07:34 MESZ
Datenpartner
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Yang, Xue
- Huang, Kexin
- Yang, Dewei
- Zhao, Weiling
- Zhou, Xiaobo
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