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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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Big Data Analytics for Early Detection and Prevention of Age-Related Diseases in Elderly Healthcare
Daril, M. A. M., Qayyum, S., Abbas, A. F., & Vân, N. T. (2023) · International Journal of Online and Biomedical Engineering (iJOE) · doi:10.3991/ijoe.v19i16.45035 · Cited by 9
Abhadionmhen, O. A., Asogwa, C. N., Ezema, M. E., Nzeh, R., Ezeora, N. J., Abhadiomhen, S. E., Echezona, S. C., & Udanor, C. N. (2024) · Molecular Biotechnology · doi:10.1007/s12033-024-01309-0 · Cited by 9
“Another systematic review [53] emphasized that innovations in predictive analytics, such as faster reporting and improved accuracy, significantly enhanced clinical decisions and research capabilities.”
Explainable artificial intelligence for stroke risk stratification in atrial fibrillation
Zimmerman, R., Hernandez, E., Tristani-Firouzi, M., Yandell, M., & Steinberg, B. A. (2025) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztaf019 · Cited by 8
“32 Personalization highlights a shift from a traditional ‘one size fits all’ approach seen in the CHAD2S2-VA and CHAD2S2-VASc scoring system to more precise risk calculations and subsequent treatment decisions for individual patients Equitability Equitability ensures that AI models deliver fair and…”
Paudel, P., Saud, R., Karna, S. K., & Bhandari, M. (2023) · International Conference on Electrical, Computer and Energy Technologies (ICECET) · doi:10.1109/icecet58911.2023.10389217 · Cited by 8
“The field of healthcare greatly values these technologies as they have a vital impact on the initial detection and forecasting of breast cancer and their importance cannot be overstated [10].”
BackgroundMathematical Modelling of Cancer Treatments, Resistance, Optimization
Azizi, T. (2025) · AppliedMath · doi:10.3390/appliedmath5020040 · Cited by 8
“Mathematical modeling provides a quantitative framework for understanding and optimizing cancer treatments, allowing researchers and clinicians to design more effective and personalized therapeutic strategies [32–34].”
AI-Driven Chemometrics for Multi-omics Data Integration: Advances, Challenges, and Future Directions
Polu, P. R. (2026) · Critical Reviews in Analytical Chemistry · doi:10.1080/10408347.2026.2657553 · Cited by 9
Prediction of Drug-Induced Nephrotoxicity Using Chemical Information and Transcriptomics Data
Chenga, H., Garg, A., Das, S. S., & Ramamurthi, N. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c00141 · Cited by 7
González, A. R., Acuña-Merchán, L. A., Alexander, J. A. A., Kaen, D., López-Correa, C., Martin, C. M., Attwill, A., Marinetti, T., Rocha, J. V., & Barrios, C. H. (2025) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph22081220 · Cited by 8
Giannakaki, A.-G., Giannakaki, M.-N., Bothou, Α., Nikolettos, K., Machairiotis, N., Pappa, K. I., & Tsikouras, P. N. (2025) · Journal of Personalized Medicine · doi:10.3390/jpm15090406 · Cited by 7
“Health equity should be embedded as a central metric in future clinical trials and observational registries [99].”
Nehra, R., Dhanda, S., Singh, K., Singh, H., & Bharti, P. K. (2026) · Archives of Microbiology · doi:10.1007/s00203-025-04617-4 · Cited by 7
“…affect mosquito fitness and competition and thus affect the ecological balance (Branda et al. 2024) and may alter the interaction between species, resulting in changes in food webs or more resistant vector species (Cordaux and Gilbert 2017; Yen et al. 2020; Hassan et al. 2022; Brand et al. 2024).”
Lee, K.-H., Lee, J., Hwang, S., Kim, Y., Lee, Y., Urtnasan, E., Koh, S. B., & Youk, H. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm12050803 · Cited by 7
“…clinical data in digital healthcare services is beneficial and powerful for precision medicine; therefore, lifelogs are becoming a new research trend that can improve the quality of daily life and expand insights based on big data analyses of individuals’ daily activities and health records [9–12].”
BackgroundYang, X., Sitharan, R., Sharji, E. A., & Feng, H. (2024) · Soft Computing · doi:10.1007/s00500-023-09570-2 · Cited by 7
“Hassan et al. (2022) have presented large cloud, a ‘‘secu-rity-by-design’’ framework for cloud BD.”
RetractedComparative Analysis of Machine Learning Techniques for Imbalanced Genetic Data
Kaur, A., & Sarmadi, M. (2024) · Annals of Data Science · doi:10.1007/s40745-024-00575-8 · Cited by 7
Nirojini, P. S., Kanaga, K., Devika, S. V., & Pradeep, P. (2024) · Scholars Academic Journal of Pharmacy · doi:10.36347/sajp.2024.v13i02.003 · Cited by 8
A Big Data Approach to Explore Medical Imaging Repositories Based on DICOM
Santos, M., & Rocha, N. P. (2023) · Procedia Computer Science · doi:10.1016/j.procs.2023.01.405 · Cited by 7
The Importance of Big Data Analysis: Developing Neuromarketing in Tourism Industry
Azman, N. B., Ahmad, A., & Valeri, M. (2023) · Emerald Group Publishing Limited eBooks · doi:10.1108/978-1-83797-166-420231001 · Cited by 7
Ordovás, J. M. (2023) · Elsevier eBooks · doi:10.1016/b978-0-443-15315-0.00015-8 · Cited by 8
AI in Healthcare Data Analytics Trends and Transformative Innovations
Badiger, M., Adiga, S., Naik, A., Shetty, S., B., S. A., Mehnaz, F., & Singh, C. (2024) · Advances in healthcare information systems and administration book series · doi:10.4018/979-8-3693-7277-7.ch001 · Cited by 6
Cutting-edge AI tools revolutionizing scientific research in life sciences
Lorenc-Kukuła, K. (2025) · Biotechnologia · doi:10.5114/bta/200803 · Cited by 9
“A study by Hassan et al. (2022) in Nature Medicine demonstrated that machine learning algorithms can accurately predict patient responses to cancer immunotherapy based on genetic profiles.”
Personalized Medicine in Treating Rare Genetic Disorders: A Review
Ansari, Z. J., Kareem, A. M., Aher, A. A., & Thitame, S. N. (2025) · Journal of Pharmacy And Bioallied Sciences · doi:10.4103/jpbs.jpbs_583_25 · Cited by 6
“[21] Future Directions and Innovations paragraph The development of personalized medicine for rare genetic disorders advances through research collaborations between institutions while artificial intelligence and gene editing technologies produce the most progress.”
Somara, S., Joshi, A. M., Mitra, K., Desai, S. S., Lundberg, M. S., Bhasin, S., & Hunsberger, J. G. (2025) · Current Stem Cell Reports · doi:10.1007/s40778-025-00249-y · Cited by 8
“Application to analyze biological data was enabled with significant advancement in machine learning and data mining in 1990s which enabled researchers to handle large datasets to uncover new insights about molecular interactions, genetic patterns and disease mechanisms [3].”
Emerging trends in computational approaches for drug discovery in molecular biology
Yuguda, Y. M., Unuebho, I. E., kolli, T., & Dhanushya, J. (2023) · GSC Biological and Pharmaceutical Sciences · doi:10.30574/gscbps.2023.24.3.0340 · Cited by 7
“With the exponential growth of biological data and the increasing complexity of molecular interactions, computational methods offer a systematic and scalable approach to tackle the immense challenges faced by researchers [3].”
BackgroundInfluentialA Comprehensive Review Of Real-Time Analytics Techniques And Applications In Streaming Big Data
Rozony, F. Z. (2024) · Innovatech Engineering Journal · doi:10.70937/itej.v1i01.2 · Cited by 8
Bhamidipaty, V., Bhamidipaty, D. L., Fayaz, S. M., Bhamidipaty, K. D. P., & Botchu, R. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-34226-4.00008-3 · Cited by 9
Biodegradable iron-based foams prepared by the space holder technique using urea
Čákyová, V., Gorejová, R., Macko, R., Petruš, O., Sopčák, T., Kupková, M., Kaľavský, F., & Oriňáková, R. (2023) · Journal of Applied Electrochemistry · doi:10.1007/s10800-023-01993-x · Cited by 5
“With the growing average length of a human's life, there is an increasing need for the use of orthopaedic implants [1, 2].”
Manik, K. H., Nath, A., Rimi, M. A. P., Ahmed, R., Rhine, A., Hasan, M. J., Mim, J. J., & Hossain, N. (2026) · Next Materials · doi:10.1016/j.nxmate.2026.102175 · Cited by 5
Big Data Analytics in Bioinformatics
Demirbaga, Ü., Aujla, G. S., Jindal, A., & Kalyon, O. (2024) · Springer eBooks · doi:10.1007/978-3-031-55639-5_12 · Cited by 5
Khan, M. U. K., & Fatima, I. (2024) · Quality & Quantity · doi:10.1007/s11135-024-01923-9 · Cited by 5
“The term “big data” encompasses various dimensions, including volume, velocity, variety, value, variability, visualization, vitality, and veracity, which shows massive amount of structured, semi-structured, and unstructured data (Hassan et al. 2022 ).”
Jamialahmadi, H., Asadnia, A., Khalili‐Tanha, G., Mohit, R., Azari, H., Khazaei, M., Maftooh, M., Nassiri, M., Hassanian, S. M., Ghayour‐Mobarhan, M., et al. (2024) · MicroRNA · doi:10.2174/0122115366320538240912080053 · Cited by 5
Multi-View Heterogeneous HyperGNN for Heterophilic Knowledge Combination Prediction
Liu, H., Ruan, S., Wu, H., Huang, Z., Lian, D., Liu, Q., & Chen, E. (2025) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2025.3527216 · Cited by 7
“For example, in academia, the intersection of genomics and Big Data analysis in personalized medicinehas thepotential torevolutionizehealthcare [5], [6].”
Rehman, S. U., Usenova, A. A., Abdullah, M., Osmonaliev, K., Habib, A., Shahzad, A., & Abdillaeva, N. (2025) · Brazilian Journal of Biology · doi:10.1590/1519-6984.292947 · Cited by 5
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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