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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Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., et al. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.12.012 · Cited by 10,423
Machine learning: Trends, perspectives, and prospects
Jordan, M. I., & Mitchell, T. M. (2015) · Science · doi:10.1126/science.aaa8415 · Cited by 9,955 · cited 1×
“Machine learning imposes a major societal impact in many computational biology applications [53,54].”
BackgroundAnalysis of protein-coding genetic variation in 60,706 humans
Exome Aggregation Consortium, Lek, M., Karczewski, K. J., Minikel, E. V., Samocha, K. E., Banks, E., Fennell, T., O’Donnell-Luria, A. H., Ware, J. S., Hill, A. J., et al. (2016) · Nature · doi:10.1038/nature19057 · Cited by 9,587 · cited 2×
“ExAC, GNOMAD, and the Beacon Network databases provide support in the understanding of genetic variations and identifying variants that are unique within a specific ethnic group [22].”
BackgroundMethodGene expression profiling predicts clinical outcome of breast cancer
van 't Veer, L. J., Dai, H., van de Vijver, M. J., He, Y. D., Hart, A. A. M., Mao, M., Peterse, H. L., van der Kooy, K., Marton, M. J., Witteveen, A. T., et al. (2002) · Nature · doi:10.1038/415530a · Cited by 9,536
Lamb, J., Crawford, E. D., Peck, D., Modell, J. W., Blat, I. C., Wrobel, M. J., Lerner, J., Brunet, J.-P., Subramanian, A., Ross, K. N., et al. (2006) · Science · doi:10.1126/science.1132939 · Cited by 5,257 · cited 1×
“Based on these findings, it is possible to perform drug repositioning using connectivity map (CMAP) technologies provided by the Broad Institute [74].”
MethodBeyond the hype: Big data concepts, methods, and analytics
Gandomi, A., & Haider, M. (2015) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2014.10.007 · Cited by 4,226
A pathology atlas of the human cancer transcriptome
Uhlen, M., Zhang, C., Lee, S., Sjöstedt, E., Fagerberg, L., Bidkhori, G., Benfeitas, R., Arif, M., Liu, Z., Edfors, F., et al. (2017) · Science · doi:10.1126/science.aan2507 · Cited by 3,371 · cited 1×
“Most importantly, the GEM modeling approach is being used in multiple medical domains such as cancer [42] obesity [43], and in Alzheimer’s disease [44].”
MethodEhteshami Bejnordi, B., Veta, M., Johannes van Diest, P., van Ginneken, B., Karssemeijer, N., Litjens, G., van der Laak, J. A. W. M., and the CAMELYON16 Consortium, Hermsen, M., Manson, Q. F., et al. (2017) · JAMA · doi:10.1001/jama.2017.14585 · Cited by 3,293
Big data analytics in healthcare: promise and potential
Raghupathi, W., & Raghupathi, V. (2014) · Health Information Science and Systems · doi:10.1186/2047-2501-2-3 · Cited by 3,034 · cited 3×
“Through digital medicine, big data analysis will prove to be an invaluable tool for health care organizations as it will provide more opportunities for proactive intervention and a more holistic view of the patients’ conditions with consolidated real-time information [109].”
BackgroundMachine learning applications in genetics and genomics
Libbrecht, M. W., & Noble, W. S. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3920 · Cited by 2,250 · cited 2×
“Translational research is usually focused on collecting data abundantly, for example, clinical data, imaging data, genetic and genomic data, and analysts are usually found to be lacking in skills to interpret such types of data [10,62,92].”
Background
Research works printed but not linked: 2
- Zikopoulos, P., and Eaton, C. (2011). Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data, McGraw-Hill Osborne Media.
- Brown, 2008, HIPAA Beyond HIPAA: ONCHIT, ONC, AHIC, HITSP, and CCHIT, J. Health Care Compliance, 10, 41
Cited by
Sorted by citations
Emerging issues in probiotic safety: 2023 perspectives
Merenstein, D., Pot, B., Leyer, G., Ouwehand, A. C., Preidis, G. A., Elkins, C. A., Hill, C., Lewis, Z. T., Shane, A. L., Zmora, N., et al. (2023) · Gut Microbes · doi:10.1080/19490976.2023.2185034 · Cited by 481
Chakraborty, C., Bhattacharya, M., Pal, S., & Lee, S. (2023) · Current Research in Biotechnology · doi:10.1016/j.crbiot.2023.100164 · Cited by 311
Assessing and mitigating batch effects in large-scale omics studies
Yu, Y., Mai, Y., Zheng, Y., & Shi, L. M. (2024) · Genome biology · doi:10.1186/s13059-024-03401-9 · Cited by 254
Transforming Clinical Research: The Power of High-Throughput Omics Integration
Vitorino, R. M. P. (2024) · Proteomes · doi:10.3390/proteomes12030025 · Cited by 203
“These advances not only improve diagnostic accuracy, but also open new avenues for targeted drug development and precision medicine [45].”
BackgroundTransforming Drug Therapy with Deep Learning: The Future of Personalized Medicine
Mulani, A. O., Deshmukh, M. S., Jadhav, V., Chaudhari, K., Mathew, A. A., & Salunkhe, S. S. (2025) · Drug Research · doi:10.1055/a-2682-5167 · Cited by 141
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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