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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Figures
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
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
Items 201–300 of 317
Big Data Analytics for Big Medical Data—Tools Applications and Challenges
Banu, A., Ameen, A., & Begum, Z. (2024) · Advanced technologies and societal change · doi:10.1007/978-981-97-3312-5_1
Suchkov, S. V., Abe, H., Berezhnoy, A. I., Murphy, S. A., Polyakova, V. S., Vasiliev, M., Ovchinnikov, A. P., Smith, D., Zemskov, V. M., Revishvili, A. S., et al. (2024) · Biology Bulletin Reviews · doi:10.1134/s2079086424600073
“There are factual data that strongly support the idea that drugs with targets confirmed by genetic human studies have a much higher probability of successful implementation than those that do not have such evidence (Usova et al., 2022; Hassan et al., 2022; Drapkina et al., 2023).”
Optimizing Cancer Patient Classification Forecasting With Bayesian Pattern Recognition
Tansitpong, P. (2024) · International Journal of Healthcare Information Systems and Informatics · doi:10.4018/ijhisi.351244
Ultra-overt therapy: a novel medical approach centered on patient consciousness
Shirbache, K., Liaghat, A., Saeifar, S., Nezameslami, A., Shirbacheh, A., Nasri, H., & Namazi, H. (2024) · Frontiers in Integrative Neuroscience · doi:10.3389/fnint.2024.1457936
Gouthami, K., Maity, S., Santra, A., Venkatesh, G. P., Mallikarjunaswamy, A. M. M., Mubeena, A., Hebbani, A. V., & Reddy, V. D. (2024) · Current Cancer Therapy Reviews · doi:10.2174/0115733947314317240805204339
METABOLIC RESEARCH FROM THE STANDPOINT OF PERSONALIZED MEDICINE
Paltzev, M. A., & Zolnikova, O. Y. (2023) · Molekulyarnaya Meditsina (Molecular medicine) · doi:10.29296/24999490-2023-01-01
Suchkov, S. V., Abe, H., Berezhnoy, A. I., Murphy, S. N., Polyakova, V. S., Vasiliev, M., Ovchinnikov, A. P., Smith, D., Zemskov, V. M., Revishvili, A. S., et al. (2024) · Успехи современной биологии · doi:10.31857/s0042132424020021
Introduction to Bioinformatics: Past, Present and Future
Kushwah, S., Kumar, A., & Mani, A. (2024) · Springer eBooks · doi:10.1007/978-981-97-7123-3_1
The Power of Translational Biology
Nasr, F. (2024) · Springer eBooks · doi:10.1007/978-3-031-35529-5_148
Patel, D., & Borgstadt, J. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-23901-4.00010-6
Retracted: Using Deep Learning to Unlock Data Science Insights: An Exploration of Techniques
Samrat, B., Pandey, M., Babu, R. D. V., Elackiya, R., Kulkarni, O. S., & Srivastav, V. (2024) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt61001.2024.10724625
“Moreover, deep mastering may be extremely computationally steeply-priced and calls for monstrous quantities of energy[7-8].”
BackgroundRetractedPragya, S., Srivastava, V., & Prusty, D. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-22326-6.00008-0
Implementing population pharmacogenomics: Tailoring drug therapy for diverse populations
Karamperis, K., & Patrinos, G. P. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-18546-5.00008-5
Dietary Intake of Chromista Oil Alters Hepatic Metabolomic Profile of Mice With Excess Fat Mass
RUST, B. M., Nielsen, F. H., & Yan, L. (2024) · Nutrition and Metabolic Insights · doi:10.1177/11786388241297143
The role of pharmacogenomics in personalized medicine: A focus on drug metabolism
Alsuwaileh, A. N., Ibrahim, I. A., Aljaloud, . A., Aljadeedi, A. K., Alyami, S. H., & Alrashed, A. H. M. (2024) · International Journal of Health Sciences · doi:10.53730/ijhs.v8ns1.15423
Barakat, H. E., & Halim, I. T. A. (2024) · Advances in wireless technologies and telecommunication book series · doi:10.4018/979-8-3373-1132-6.ch007
Leveraging Drug-Gene Interaction Information for Personalized Hypertension Drug Molecule Discovery
Lehasa, O. M.-E., & Chude‐Okonkwo, U. A. K. (2024) · Current pharmacogenomics and personalized medicine (Online)/Current pharmacogenomics and personalized medicine · doi:10.2174/0118756921355914241203054023
Farmasi Cerdas: Era Baru Penemuan Obat dengan AI dan Big Data
Tjandrawinata, R. R. (2025) · MEDICINUS · doi:10.56951/rhvmjy22 · Cited by 1
Big Data Analytics in Bioinformatics and Healthcare
Lawal, Z. K., Zakari, R. Y., Thilakarathne, N. N., Gul, H., Abdulrazak, N., & Yassin, H. (2025) · Studies in big data · doi:10.1007/978-981-97-5734-3_4 · Cited by 1
Data Sovereignty and Genomic Data Across Borders: Taiwan in a Comparative Perspective
Ho, C. (2024) · Perspectives in law, business and innovation · doi:10.1007/978-981-97-9983-1_7
Multi-omics data for machine learning algorithms
Ahanger, A. N., Shah, S. N. A., Ahanger, A. B., Bhat, A. A., Assad, A., Bhat, M. R., & Macha, M. A. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-13595-8.00009-x
Medical Physics in Aesthetic Medicine: Innovations and Techniques for Cosmetic Procedures in 2025
Ali, E. A. A., Hameed, A. G. A. R., Mahmoud, M. M. A., Mahdi, H. A. R., & Rafas, R. Y. A. (2025) · Bright Sky Publications eBooks · doi:10.62906/bs.book.296 · Cited by 1
Taleb, Y. S., Memon, P., Jalbani, A. A., Alanazi, N., Al-Garni, A., AlTaweel, M., Khan, M. A., Sabar, M. F., & Iqbal, Z. (2025) · Saudi Journal for Health Sciences · doi:10.4103/sjhs.sjhs_95_24
Medical Physics: Imaging and Radiation Therapy
Swadi, H. K., Ragi, Z. A., Abbas, R. Q., Hassouni, H. F., & Kazim, H. H. (2025) · Bright Sky Publications eBooks · doi:10.62906/bs.book.311
Rikan, W. S. D., Wahid, S., thabit, S. H. K., Abboud, S. M. H., & Salman, A. R. M. (2025) · Bright Sky Publications eBooks · doi:10.62906/bs.book.308
Exploring the Role of Digital Biomarkers and Artificial Intelligence in Precision Therapeutics
Saeed, A., & Alajlan, S. A. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-6270-9.ch006
State of Florida Employer, O., & Ravi, R. K. (2025) · International Journal of Innovative Research in Science Engineering and Technology · doi:10.15680/ijirset.2025.1404008
A Survey on Innovation in Medical Science using Technology
Shabari Shedthi, B., Poojari, A., Bangera, L. U., Hrithika, Hithanksha, & Shetty, G. H. (2025) · International Conference on Artificial Intelligence and Data Engineering (AIDE) · doi:10.1109/aide64228.2025.10987451
“The explosion of biomedical and clinical data, particularly from various omics fields (e.g., genomics, proteomics, and metabolomics), presents both opportunities and challenges in healthcare innovation [42].”
Introduction to Federated Intelligent Systems in Healthcare
Ahmad, N. S. (2025) · Wiley eBooks · doi:10.1002/9781394271382.ch1
Data Quality and Big Data in the Health Industry: A Scoping Review Protocol
Santos, L. C. T., Bublitz, F. M., Bezerra, K. C. T., Moura, R. M., & Fonseca, B. B. (2025) · Lecture notes in mechanical engineering · doi:10.1007/978-3-031-94484-0_32
Bio-Genetics and Disease: Diagnostic and Analytical Approaches
Juma, M., Jasab, S. J. H., Mohammed, S. H. H., & Alai, L. H. S. (2025) · Bright Sky Publications eBooks · doi:10.62906/bs.book.388
Inibidores de checkpoint imunológico: eventos autoimunes e mineração de dados
Marmitt, N., Vargas, S. T., Innocente, A. P., DUCATTI, D. F., Fernandes, G. R., Dahmer, A., & Terres, M. D. S. (2025) · Journal of Health Informatics · doi:10.59681/2175-4411.v17.2025.1160
Ethical Boundary Between Network Security and Personal Privacy in the Era of Big Data
Xu, T. (2025) · International Journal of Information Security and Privacy · doi:10.4018/ijisp.384489
Advancing Healthcare: Data Mining and Digital Tech in Diagnostics and Care
Alzumaya, A., Alhazmi, K., & Emara, A.-H. M. (2025) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-031-91351-8_13
Meningoencephalitis Diagnosis Using Contemporary Diagnostic Advancements
Parveen, A., Karak, P., & Acharya, M. (2025) · Neurology India · doi:10.4103/neurol-india.neurol-india-d-24-00891
Predictive analytics and diagnostics in personalized medicine using machine learning models
Sharma, S., Singh, G., & Sai, V. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-32998-2.00003-2
Thakur, A., Chowdhury, K., Sharma, V. V., Singh, K., Gupta, J. K., Jain, D., & Sharma, M. C. (2025) · Combinatorial Chemistry & High Throughput Screening · doi:10.2174/0113862073397811250716154921
Feuer, O., Holmes, K. D., Kane, S. R., Curry, K. M., Ma, D., Chatwin, C. A., Chuldzhyan, M., Quinn, E. A., & Gorman, N. (2025) · Journal of Genetic Counseling · doi:10.1002/jgc4.70065
“These technologies drive precision in genetic counseling by offering data-driven insights and fostering the development of innovative tools (Hassan et al., 2022 ; Papadopoulou & Lytras, 2023).”
From Data to Diagnosis: Predicting Cancer Types with Machine Learning
Chatterjee, K., Mazumdar, P., & Mallik, S. (2025) · International Conference on Control, Communication and Computing (ICCC) · doi:10.1109/iccc64910.2025.11077209
“The swift progress in medical technology and data analysis has revolutionized oncology, allowing for more accurate diagnostics and tailored treatment solutions [1].”
Big Data Integration in Genomic Analysis
Segall, R. S., & Rajbhandari, P. (2025) · International Journal of Big Data Intelligence and Applications · doi:10.4018/ijbdia.387389
Population insights and big data in brain cancer research and patient care
Alaouna, M., Hull, R., Marutha, T., Makrogkikas, S., Lolas, G., & Dlamini, Z. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-45495-0.00003-6
Kumar, P., Pradhan, V., Panwar, V., & Bandyopadhyay, A. (2025) · De Gruyter eBooks · doi:10.1515/9783111504667-004
Quantum Neural Networks (QNNs) for Genomic Pattern Detection in Personalized MedicinePreprint
Bhuiyan, M. A. S., & Sharaboni, E. I. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5435076
MODERN APPROACHES TO MATHEMATICAL MODELING IN ANIMAL HUSBANDRY
Cherneha, O. S., Basovskiy, D. M., & Biriukova, О. D. (2025) · Animal Breeding and Genetics · doi:10.31073/abg.70.31
“Дослідники різних країн наголошують на наявності значної переваги використання су-часних ІТ-технологій у проведенні геномних досліджень для пришвидшення обробки і інте-рпретації значних обсягів інформації (Warr et al., 2020; Hassan et al., 2022; Vilhekar et al., 2024).”
Assessment of AI models in healthcare: performance metrics and applications across critical diseases
Goel, A., Singhal, A., & Kumar, S. (2025) · International Journal of Systems Assurance Engineering and Management · doi:10.1007/s13198-025-02968-1
Automation of Breast Cancer Diagnosis and Treatment Using Machine Learning
Swamy, C. M., Kumar, S. B., Kiran, B., & Kumar, M. S. V. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-5723-0_35
Barriers to Nursing Managers' Decision-Making
Taj, A., Mohammadi, S., Davoodvand, S., & Roshanzadeh, M. (2025) · Nurse Leader · doi:10.1016/j.mnl.2025.102544
From Molecule to Market: The Role of AI in Shaping Modern Pharmacoeconomics
Aziz, S., Jabal, G. A. A., Dayoob, M., Hameed, M. A., Aslam, A., & Alshehade, S. A. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-031-96631-6_23
Personalized Medicine Recommendation Through Genomics AI
Chaudhuri, U., Chanda, R., Sharma, V., Mishra, S., Alkhyyat, A., & Mishra, N. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-6053-7_3
Recent advances in biotechnology
Lerfall, J. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-33750-5.00014-7
Kazi, J. U. (2025) · Springer eBooks · doi:10.1007/978-3-031-85600-6_10
Role of Sustainability and Data Analytics in Healthcare Efficiency
Firdous, J. U., Sharma, A., -, S., Sharma, A. M., Singh, S., & Sharma, S. (2025) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9798898811563125010006 · Cited by 1
Sheela, T., Kadhim, H., Gopi, H., Ravichandran, C., Majid, A.-H. M., & Kavitha., V. (2025) · International Conference on Engineering Innovations and Technologies (ICoEIT) · doi:10.1109/icoeit63558.2025.11211586
“These gaps are overcome in Personalized medicine, which was developed by utilizing genetic, molecular and phenotypic data to make tailored clinical decisions that accord with an individual’s biology[2].”
Privacy-Preserving Genomic Data Sharing via Hybrid AES/ECC and Blockchain-Based Dynamic ConsentPreprint
Soundararajan, J., Zhuang, Y., & Xu, D. H. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.01.686038
“This exponential increase in genomic data generation presents immense opportunities for biomedical research and significant challenges in data management, security, and privacy [1].”
Healing with Technology: The Convergence of AI and Robotics in Healthcare
Pathak, A., & Islam, M. S. (2025) · Information systems engineering and management · doi:10.1007/978-3-031-91278-8_13
FDA export restrictions threaten collaboration
Zhang, K., Niu, J., & Li, Z. (2025) · Science · doi:10.1126/science.aeb1546
Innovations in Healthcare: Unveiling the Potential of Generative AI
Pandey, S., Bhushan, B., & Wajid, M. S. (2025) · Studies in computational intelligence · doi:10.1007/978-981-95-2129-6_7
FAIR and Square: Privacy Compliance Framework for Healthcare DatabasesPreprint
Zhu, Z., Louws, M. N., Bumbuc, R. V., Sakkers, J., Wilding, R., Brandt, B. W., Hoekstra, A. G., & Sheraton, V. M. (2025) · Research Square · doi:10.21203/rs.3.rs-8205252/v1 · Cited by 1
Bardhan, K. D., & Sarkar, C. (2025) · Functional & Integrative Genomics · doi:10.1007/s10142-025-01764-1
“…vast datasets remains a significant challenge due to the computational expertise required and the com - plexity of existing analytical pipelines (Hassan 2022). enhances the reliability of differential expression results, reducing false positives and improving biological inter - pretation at the…”
Ali, S. A. M., Begum, S., Naveed, M. A., & Mujahid, M. N. (2025) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9798898813512125010015
Cariaga, R. F. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5378101
Integration of artificial intelligence with traditional medicine for treating diabetes
Rustagi, S., Pandey, V. K., Singh, D., Jain, D., & Madan, A. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-31698-2.00010-2
Oso, O. V., Adebayo, U. O., Ajayi, D. T., Oluwasusi, E. A., Jegede, T. O., Onyeaghala, E. O., Agun, P. D., Oyetunji, S. O., & Wagwula, P. M. (2025) · SN Comprehensive Clinical Medicine · doi:10.1007/s42399-025-02208-4
“This data is obtained through electronic medical records, scanners, biosensors, social media, various omics technologies, including DNA sequences, transcriptomes, proteomes, metabolomes, epig - enomes, and microbiomes and bio-sensor devices such as smartphones and wearable devices [ 32 – 34 ].”
Data reduction for big data application in IOT
Selvaraj, I. B., Vadivelu, B., & Paulraj, C. S. J. R. (2025) · OPSEARCH · doi:10.1007/s12597-025-01071-0
Yamsani, N., Shyamala, G., Rao, A. R., & Manda, S. A. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-10783-1_6
Li, P.-Y., Tsao, Y.-P., Sun, Y., Tsai, H., Lee, C.-Y., Chan, C.-W., Chu, Y.-Y., Chen, Y., Lin, S., Wang, S.-L., et al. (2025) · Journal of Leukocyte Biology · doi:10.1093/jleuko/qiaf188
Leveraging AI and ITRS in Drug Discovery for Inflammatory and Cardiovascular Diseases
Swathi, N. L., Vineesh, A., Sadhankar, A., Muzzamil, M., Arun, A. K., Karpakavalli, M., Maity, D., Wilfy, A., Bosco, J. A. M., & Pilkhwal, G. S. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-5258-9.ch006
Singularities and Universals: Case Reports, Clinical Trials, and an Enduring Epistemic Tension
Galli, C., & Meleti, M. (2026) · Histories · doi:10.3390/histories6010011
“Meta‑ analyses pooling dozens of randomized trials, exhaustive systematic reviews, and popula tion‑scale genomic screens dominate leading journals, pushing statistics to center stage (Batko and Ślęzak 2022; Hassan et al. 2022).”
Xuan, H., Sun, H., Liu, X., Zhang, H., Zhang, J., & Zhong, C. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.28.702355
The Intersection of AI and genomics in health and disease: Advancements and applications
Kaushik, L., Vivek, A., Arora, S., Hamid, F., Mukherjee, K., Bisht, N., Chaudhary, S., Shukla, J., Nawani, S., & Kumar, S. (2026) · Progress in molecular biology and translational science · doi:10.1016/bs.pmbts.2026.01.013
Integrating genomic profiling and technological innovations in personalized surgery: a way forward?
Farooq, A., Htoo, P. M., Shwe, W. H., Win, K. Z., Aslam, Z., Chopra, H., Chopra, S., Emran, T. B., & Baig, A. A. (2025) · International Journal of Surgery Open · doi:10.1097/io9.0000000000000312
Quantum algorithms for genomic analysis in systems biology
Roosan, D., Khan, H. M., & Khan, R. I. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-34193-9.00002-8
Petchiappan, V., & Vadivel, V. (2025) · IEEE Pune Section International Conference (PuneCon) · doi:10.1109/punecon67554.2025.11378713
“But this progress also raises fresh concerns about data security [1,2].”
Noreen, S., Shahzad, A., Akhtar, S., Deeba, F., Mahmood, S., & Munir, I. (2026) · Network Modeling Analysis in Health Informatics and Bioinformatics · doi:10.1007/s13721-026-00736-0
“This accumulated knowledge serves as the catalyst for crafting precise therapeutic approaches and individualized treat-ment tactics, instilling a sense of optimism for enhanced outcomes in individuals affected by the RCC (Hassan et al. 2022).”
Li, Y. (2025) · International Conference on Intelligent Innovations in Engineering and Technology (ICIIET) · doi:10.1109/iciiet65921.2025.11379364
“As a result of a study of genomics and big data analytics progress, the GBA-PM (Genomics Big data Analytics for Precision Medicine) framework is offered by Hassan et al. [8].”
Adversarially-Robust Secure Scheduling Algorithm for Big Data Analytics in Healthcare IoT Networks
Samypen, Y., & Chakkaravarthy, M. (2025) · International Conference on Intelligent Innovations in Engineering and Technology (ICIIET) · doi:10.1109/iciiet65921.2025.11378465
“Adversarial attacks are also becoming a frequent method of attacking healthcare IoT networks, and are taking advantage of weaknesses in their scheduling systems [9].”
The role of artificial intelligence in post-stroke rehabilitation
Grinenko, T. B., Krasovskaya, Z. D., Salimgarieva, A. A., Filippov, A. A., Khakimov, R. R., Ильдусович, Л. И., Kagarmanova, A. I., Faizullina, A. R., Atlasova, A. E., Melokyan, L. S., et al. (2025) · Medical and Social Expert Evaluation and Rehabilitation · doi:10.17816/mser696141
Academic Research Uncovered With Methodologies, Challenges, and Innovations
Biswas, H. S., Biswas, S. S., Kundu, A. K., & Maiti, D. K. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-0897-5.ch016
Legal, Ethical, and Compliance Frameworks in Ethical Hacking Practices
Ghosh, O., & Kumar, B. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-3536-0.ch007
Thanawala, R. M., Jenkins, P. D., Bedrick, S., Mohan, V., Doberne, J., & Azarow, K. S. (2026) · The American Journal of Surgery · doi:10.1016/j.amjsurg.2026.116950
Chappell, K. (2023) · theses.fr (ABES) · doi:10.70675/5afa484cz21e5z493azae47ze62ca3553104
Siennicka, A. E., Matera-Witkiewicz, A., Pondel, M., & Chomiak-Orsa, I. (2026) · IFIP advances in information and communication technology · doi:10.1007/978-3-032-18920-2_45
Predictive modeling for disease prevention
Goel, R., Mishra, R., Puri, D., Raj, G., & Singh, N. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-44958-1.00018-5
Artificial gametes: A nanotechnology-driven future
Natarajan, V., Dalal, A., Tippani, R., Billah, A. A. M., Shaikh, S. S., Chinni, S. V., & Mohandoss, K. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-41561-6.00002-9
The Ecosystem of State Support for Science in Slovakia and Scientific Data
Zendulková, D., Gavurníková, G., & Turňa, J. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-1433-2.ch008
The Innovation Paradox: Navigating the Golden Cage of Product Personalization
Mu, J. (2026) · Future of business and finance · doi:10.1007/978-3-032-25134-3_5
Iourov, I. Y., Морозов, Д. А., Демидова, И. А., Куринная, О. С., Kolotii, A. D., Vasin, K. S., & Chaika, Y. A. (2026) · Russian Journal for Personalized Medicine · doi:10.18705/2782-3806-2026-6-1-19-31
Cao, S., Lan, D., Wei, Y., Zeng, H., & Zhang, H. (2026) · Journal of Nanobiotechnology · doi:10.1186/s12951-026-04500-4
Database Architecture Comparison for Large-Scale Genomic Variant Analysis
Dvorský, B., Komárková, M., Kollinová, K., Klempíř, O., & Krupička, R. (2026) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-032-24724-7_61
Kumar, M. M., Melam, Q. S., Hussain, M. A., Tummeeti, V. V., & Sudheer, A. (2026) · doi:10.1002/9781394367559.ch12
Adekilekun, H. A., Omodamiro, O. D., Achi, N. K., Oyewusi, H. A., & Omodamiro, R. M. (2026) · doi:10.1002/9781394367559.ch8
Navigating security hurdles in healthcare data governance
Kumar, B., & Ghosh, O. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-33700-0.00012-1
Improving clinical decision-making and operational efficiency through predictive analytics
Karthikeyan, V., & Visu, Y. P. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-41545-6.00017-8
Reicher, L., Zahavi, L., & Segal, E. (2026) · Springer eBooks · doi:10.1007/978-3-032-23479-7_20
Promoting sustainable healthcare practices through the integration of advanced quantum technologies
Ghosh, O., & Kumar, B. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-33700-0.00014-5
Biotechnological paradigms for augmenting secondary metabolite biosynthesis in medicinal flora
Tiwari, N., Sompura, Y., Singh, V. K., Soni, V., & Gangwar, M. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-34129-8.00019-5
Meral, G. (2026) · Psycho-Oncologie · doi:10.18282/po5678
Yuan, H., Wei, Y., Yang, C., Kuang, H., & Wang, Z. (2026) · Journal of Ethnopharmacology · doi:10.1016/j.jep.2026.122112
The Role of Intelligent Computing in Medical and Genomic Healthcare Innovations
Srivastava, A., Nanda, S., Goyal, D., Kulandhaivel, M., Premananthan, G., Vyshnavi, A., & Wong, L. S. (2025) · Asian Conference on Communication and Networks (ASIANComNet) · doi:10.1109/asiancomnet68615.2025.11579670
“It provides a cogno evaluative review of the literature that encompasses the present and future applications of cognitive platforms in precision medicine [19].”
Big Data in Cancer Genomics: Computational Foundations and Emerging Pathways for Precision Oncology
Vanu, N., Mohammad, N., Ahmed, F., Alam, T., Saha, K., Shahi, D., Hossain, A., Manik, M. M. T. G., & Parvin, M. R. (2026) · Computational and Systems Oncology · doi:10.1002/cso2.70020
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
