The DOI matches its Crossref record; 136 of the 136 references it cites were found in a registry or an index.
Innovations in Genomics and Big Data Analytics for Personalized Medicine and Health Care: A Review
Open accessAbstract
Big data in health care is a fast-growing field and a new paradigm that is transforming case-based studies to large-scale, data-driven research. As big data is dependent on the advancement of new data standards, technology, and relevant research, the future development of big data applications holds foreseeable promise in the modern day health care revolution. Enormously large, rapidly growing collections of biomedical omics-data (genomics, proteomics, transcriptomics, metabolomics, glycomics, etc.) and clinical data create major challenges and opportunities for their analysis and interpretation and open new computational gateways to address these issues. The design of new robust algorithms that are most suitable to properly analyze this big data by taking into account individual variability in genes has enabled the creation of precision (personalized) medicine. We reviewed and highlighted the significance of big data analytics for personalized medicine and health care by focusing mostly on machine learning perspectives on personalized medicine, genomic data models with respect to personalized medicine, the application of data mining algorithms for personalized medicine as well as the challenges we are facing right now in big data analytics.
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“These models use large datasets, including clinical data, genetic information, and lifestyle factors, to create highly personalized predictive models (46).”
From Generative artificial intelligence in predictive analysis of diabetes and its complications: a narrative review (2025) · cites this paper as [46]
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“However, the cloud infrastructure is not, in and of itself, an AI method (Hassan et al., 2022).”
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Metadata
- Authors
- Hassan, M., Awan, F. M., Naz, A., deAndrés-Galiana, E. J., Alvarez, O., Cernea, A., Fernández-Brillet, L., Fernández-Martínez, J. L., & Kloczkowski, A.
- Affiliations
- Nationwide Children's Hospital (US)
- University of Haripur (PK)
- University of Lahore (PK)
- Group of Inverse Problems, Optimization and Machine Learning, University of Oviedo, 33003 Oviedo, Spain
- DeepBioInsights, 38311 La Florida, Spain
- Corresponding author
- Hassan, Mubashir
- Kloczkowski, Andrzej
- ORCID
- Venue
- International Journal of Molecular Sciences
- Publisher
- MDPI AG
- Published
- 2022-04-22
- Volume
- 23
- Issue
- 9
- Pages
- 4645
- ISSN
- 1422-0067 (ISSN-L)
- PMC
- PMC9104788
- OpenAlex
- W4224276240
- Cited by
- 317
- References
- 136
- Citations by year
- 2026: 56 · 2025: 130 · 2024: 76 · 2023: 48 · 2022: 7
- FWCI
- 9.52
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- International Journal of Molecular Sciences (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institutes of Health (R01GM127701, R01HG012117)
- National Science Foundation (DBI-1661391)
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
- Keywords
- big data analytics, personalized medicine, precision medicine, health care, machine learning, data mining algorithms, omics data, clinical data, data standards
- MeSH terms
- Data Science, Big Data, Delivery of Health Care, Genomics, Precision Medicine
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