Innovations in Genomics and Big Data Analytics for Personalized Medicine and Health Care: A Review

Hassan, Mubashir; Awan, Faryal Mehwish; Naz, Anam; deAndrés-Galiana, Enrique J.; Alvarez, Oscar; Cernea, Ana; Fernández-Brillet, Lucas; Fernández-Martínez, Juan Luis; Kloczkowski, Andrzej

doi:10.3390/ijms23094645

Cited by 317Artificial Intelligence in HealthcareArtificial Intelligence in Healthcare and EducationBig Data Technologies and Applications

Innovations in Genomics and Big Data Analytics for Personalized Medicine and Health Care: A Review

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Hassan, Mubashir, Awan, Faryal Mehwish, Naz, Anam, deAndrés-Galiana, Enrique J., Alvarez, Oscar, Cernea, Ana, Fernández-Brillet, Lucas, Fernández-Martínez, Juan Luis +1 more·2022·International Journal of Molecular Sciences

DOI 10.3390/ijms23094645PMC PMC9104788

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Abstract

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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Generative AI models use large datasets including clinical data, genetic information, and lifestyle factors to create highly personalized predictive models.
“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]
Section 4 says machine-learning and AI approaches combine genetic data and electronic health records to build predictive classifiers, and Section 1 describes personalized medicine as considering behavioral and environmental information. The paper does not establish that generative AI models specifically use lifestyle factors to create these predictive models; its discussion concerns AI and machine learning more broadly. Page numbers were not available in our copy. (LLM, conf=0.88)
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Developing Structure−Activity Relationships for the Prediction of Hepatotoxicity
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“The computational problem consists of ﬁnding a new compound that provides the desirable structure–activity relationships (SAR data) [82,83].”
Background
Genetic and epigenetic regulation of gene expression in fetal and adult human livers
Bonder, M. J., Kasela, S., Kals, M., Tamm, R., Lokk, K., Barragán, I., Buurman, W. A., Deelen, P., Greve, J.-W. M., Ivanov, M., et al. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-860 · Cited by 164 · see citing works
Heterogeneous Data and Big Data Analytics
Wang, L. (2017) · Automatic control and information sciences · doi:10.12691/acis-3-1-3 · Cited by 163 · cited 3× · see citing works
“In the current situation, large amount of genomics data could be used to address personalized health care issues [104] and can help to propose new drugs for the treatment of gene related disorders.”
Background
De Novo Drug Design
Hartenfeller, M., & Schneider, G. (2010) · Methods in molecular biology · doi:10.1007/978-1-60761-839-3_12 · Cited by 160 · cited 1× · see citing works
“Two other ﬁelds of active work are de novo drug design [77] and the optimization of gene therapies [78].”
Background
Putting the data before the algorithm in big data addressing personalized healthcare
Cahan, E. M., Hernandez-Boussard, T., Thadaney-Israni, S., & Rubin, D. L. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0157-2 · Cited by 149 · cited 1× · see citing works
“Algorithms represent the terminal node in the ﬁnal predictions from big data [85].”
Background
Atlas of Cancer Signalling Network: a systems biology resource for integrative analysis of cancer data with Google Maps
Kuperstein, I., Bonnet, É., Nguyen, H.-A., Cohen, D., Viara, E., Grieco, L., Fourquet, S., Calzone, L., Russo, C., Kondratova, M., et al. (2015) · Oncogenesis · doi:10.1038/oncsis.2015.19 · Cited by 143 · cited 1× · see citing works
“MIMs explore the information about different mechanistic pathways and regulatory modules involved in a disease such as Parkinson’s [39] or signaling in cancer [40], respectively.”
Background
Using artificial intelligence methods to speed up drug discovery
Álvarez-Machancoses, Ó., & Fernández‐Martínez, J. L. (2019) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2019.1621284 · Cited by 127 · cited 1× · see citing works
“Two other ﬁelds of active work are de novo drug design [77] and the optimization of gene therapies [78].”
BackgroundSelf-citation
Drug discovery for malaria: a very challenging and timely endeavor
Gelb, M. H. (2007) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2007.05.038 · Cited by 123 · cited 1× · see citing works
“Drug discovery is a very challenging problem due to the high attrition rates in de novo design due to the lack of the efﬁcacity of the new compounds and due to possible development of undesirable side effects [79–81].”
Background
Computational Models for Clinical Applications in Personalized Medicine—Guidelines and Recommendations for Data Integration and Model Validation
Collin, C. B., Gebhardt, T., Golebiewski, M., Karaderi, T., Hillemanns, M., Khan, F. M., Salehzadeh‐Yazdi, A., Kirschner, M. W., Krobitsch, S., & Kuepfer, L. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm12020166 · Cited by 122 · cited 3× · see citing works
“Furthermore, upon overlying expression data, such maps serve as visualization tools for the activity level of regulators and their targets of established disease markers, which provide the simplest mechanistic visualization of data [31].”
Background
Predictive data mining in clinical medicine: a focus on selected methods and applications
Bellazzi, R., Ferrazzi, F., & Sacchi, L. (2011) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.23 · Cited by 106 · cited 1× · see citing works
“Temporal data are more often related to clinical studies and mainly depend on time series, with or without a sequence of events (i.e., the time-based quantitative measurements or sequence of temporal events related to particular clinical study) [88].”
Background
Machine learning in infection management using routine electronic health records: Tools, techniques, and reporting of future technologies
Luz, C. F., Vollmer, M., Decruyenaere, J., Nijsten, M. W., Glasner, C., & Sinha, B. (2020) · Clinical Microbiology and Infection · doi:10.1016/j.cmi.2020.02.003 · Cited by 106 · cited 1× · see citing works
“…of data including genetic, genomic [62], epigenomic [63,64], transcriptomic [65], metabolomic data [66], medical images, biobanks data [67], electronic health records (EHR) [68], scientiﬁc literature data, etc., and are able to combine all of this information to design optimum classiﬁers [69].”
Background
A Personal Perspective on the Origin(s) and Development of 'Big Data': The Phenomenon, the Term, and the Discipline, Second VersionPreprint
Diebold, F. X. (2013) · SSRN Electronic Journal · doi:10.2139/ssrn.2202843 · Cited by 93 · see citing works
From Bayes to Tarantola: New insights to understand uncertainty in inverse problems
Fernández‐Martínez, J. L., Fernández‐Muñiz, Z., Pallero, J. L. G., & González, L. M. P. (2013) · Journal of Applied Geophysics · doi:10.1016/j.jappgeo.2013.07.005 · Cited by 91 · cited 1× · see citing works
“These models are located in ﬂat curvilinear valleys of the cost function landscape [17,18].”
BackgroundSelf-citation
Medicina de precisão/medicina personalizada: análise crítica dos movimentos de transformação da biomedicina no início do século XXI
Iriart, J. A. B. (2019) · Cadernos de Saúde Pública · doi:10.1590/0102-311x00153118 · Cited by 87 · see citing works
Novel Hybrid Phenotype Revealed in Small Cell Lung Cancer by a Transcription Factor Network Model That Can Explain Tumor Heterogeneity
Udyavar, A. R., Wooten, D. J., Hoeksema, M., Bansal, M., Califano, A., Estrada, L., Schnell, S. D., Irish, J. M., Massion, P. P., & Quaranta, V. (2016) · Cancer Research · doi:10.1158/0008-5472.can-16-1467 · Cited by 83 · cited 1× · see citing works
“In the context of systems medicine, this approach is often applied for cancer research [46,47].”
Background
Whither systems medicine?
Apweiler, R., Beißbarth, T., Berthold, M. R., Blüthgen, N., Burmeister, Y., Dammann, O., Deutsch, A., Feuerhake, F., Franke, A., Hasenauer, J., et al. (2018) · Experimental & Molecular Medicine · doi:10.1038/emm.2017.290 · Cited by 83 · cited 2× · see citing works
“The added value of such computational odels, sometimes called digital evidence, in medicine is also acceptable by the scientific community [32,33] and the U.”
Background
Pharmacokinetic-Pharmacodynamic Modeling
Pérez‐Urizar, J., Granados‐Soto, V., Flores‐Murrieta, F. J., & Castañeda‐Hernández, G. (2000) · Archives of Medical Research · doi:10.1016/s0188-4409(00)00242-3 · Cited by 81 · cited 1× · see citing works
“Therefore, pharmacokinetic models can be described by compartmental pharmacokinetic (PK) modeling [51] or by physiologically based PK (PBPK) modeling [52].”
Background
Discovery of food identity markers by metabolomics and machine learning technology
Erban, A., Fehrle, I., Martínez-Seidel, F., Brigante, F. I., Más, A. L., Baroni, M. V., Wunderlin, D. A., & Kopka, J. (2019) · Scientific Reports · doi:10.1038/s41598-019-46113-y · Cited by 79 · cited 1× · see citing works
“…and AI approaches work with different types of data including genetic, genomic [62], epigenomic [63,64], transcriptomic [65], metabolomic data [66], medical images, biobanks data [67], electronic health records (EHR) [68], scientiﬁc literature data, etc., and are able to combine all of this…”
Background
Patient‐specific logic models of signaling pathways from screenings on cancer biopsies to prioritize personalized combination therapies
Eduati, F., Jaaks, P., Wappler, J., Cramer, T., Merten, C. A., Garnett, M. J., & Sáez-Rodríguez, J. (2020) · Molecular Systems Biology · doi:10.15252/msb.20188664 · Cited by 74 · cited 1× · see citing works
“In the context of systems medicine, this approach is often applied for cancer research [46,47].”
Background
Big Data solutions in Healthcare: Problems and perspectives
Mathew, P. S., & Pillai, A. S. (2015) · International Conference on Innovations in Information, Embedded and Communication Systems · doi:10.1109/iciiecs.2015.7193211 · Cited by 69 · cited 1× · see citing works
“One of the major problems in the use of big data in medicine is that medical data have been collected across different states, hospitals, and administrative departments using different protocols [127–129].”
Background
Special issue on Security, Privacy and Trust in network-based Big Data
Wang, H., Jiang, X., & Kambourakis, G. (2015) · Information Sciences · doi:10.1016/j.ins.2015.05.040 · Cited by 69 · cited 1× · see citing works
“It is known that during online data collection and transmission, health care devices are functional with the help of their MAC addresses and Internet protocol (IP) addresses [97,131].”
Background
Using Big Data to Discover Diagnostics and Therapeutics for Gastrointestinal and Liver Diseases
Wooden, B., Goossens, N., Hoshida, Y., & Friedman, S. L. (2016) · Gastroenterology · doi:10.1053/j.gastro.2016.09.065 · Cited by 69 · see citing works
Temporal Trends in Critical Care Outcomes in U.S. Minority-Serving Hospitals
Danziger, J., de la Hoz, M. Á. A., Li, W., Komorowski, M., Deliberato, R. O., Rush, B. N. M., Mukamal, K. J., Celi, L. A., & Badawi, O. (2020) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.201903-0623oc · Cited by 69 · see citing works
Integrating Transcriptomics with Metabolic Modeling Predicts Biomarkers and Drug Targets for Alzheimer's Disease
Stempler, S., Yizhak, K., & Ruppin, E. (2014) · PLoS ONE · doi:10.1371/journal.pone.0105383 · Cited by 65 · cited 1× · see citing works
“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].”
Method
Immunohistochemical versus molecular (BluePrint and MammaPrint) subtyping of breast carcinoma. Outcome results from the EORTC 10041/BIG 3-04 MINDACT trial
for the MINDACT investigators, Viale, G., de Snoo, F. A., Slaets, L., Bogaerts, J., Veer, L. V. ‘., Rutgers, E. J. T., Piccart, M. J., Stork‐Sloots, L., Glas, A. M., et al. (2017) · Breast Cancer Research and Treatment · doi:10.1007/s10549-017-4509-9 · Cited by 64 · cited 2× · see citing works
“Moreover, the BluePrint test has also demonstrated the expression data, which could be supportive for personalized medicine in MINDACT and IMPACt trials [59].”
BackgroundMethod
Big Data Analytics in Heart Attack Prediction
Alexander, C. A., & Wang, L. (2017) · Journal of Nursing & Care · doi:10.4172/2167-1168.1000393 · Cited by 63 · cited 1× · see citing works
“Moreover, heterogeneity might be terminological, conceptual, syntactic, or semiotic Moreover, heterogeneity might be terminological, conceptual, syntactic, or semiotic in nature [104,107]. in nature [104,107].”
Background
The exploration of novel Alzheimer’s therapeutic agents from the pool of FDA approved medicines using drug repositioning, enzyme inhibition and kinetic mechanism approaches
Hassan, M., Raza, H., Abbasi, M. A., Moustafa, A. A., & Seo, S. (2018) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2018.11.115 · Cited by 61 · cited 1× · see citing works
“Drug repurposing, also called drug repositioning, of the already known FDA approved compounds for new therapeutic uses is a very effective methodology to ﬁnd a cure in rare diseases where the economic constraints for new drug development are very important [75].”
BackgroundSelf-citation
The effect of noise and Tikhonov's regularization in inverse problems. Part I: The linear case
Fernández‐Martínez, J. L., Pallero, J. L. G., Fernández‐Muñiz, Z., & González, L. M. P. (2014) · Journal of Applied Geophysics · doi:10.1016/j.jappgeo.2014.05.006 · Cited by 57 · see citing works
Self-citation
MetaFast: fast reference-free graph-based comparison of shotgun metagenomic data
Игоревич, У. В., Kazakov, S. V., Dubinkina, V. B., Tyakht, A., & Alexeev, D. G. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw312 · Cited by 50 · cited 1× · see citing works
“As a result, specialists would be able to detect previously unidentiﬁed pathogens and their strains, which can aid in the development of personalized medicine [87].”
Background
The effect of noise and Tikhonov’s regularization in inverse problems. Part II: The nonlinear case
Fernández‐Martínez, J. L., Pallero, J. L. G., Fernández‐Muñiz, Z., & González, L. M. P. (2014) · Journal of Applied Geophysics · doi:10.1016/j.jappgeo.2014.05.005 · Cited by 49 · cited 1× · see citing works
“Therefore, the best way to deal with such problems is by reducing the dimension to perform a robust uncertainty analysis of the corresponding medical decision problem [19,20].”
BackgroundSelf-citation
Machine Learning for Data Mining in Medicine
Nada Lavrač, A. (1999) · Lecture notes in computer science · doi:10.1007/3-540-48720-4_4 · Cited by 48 · cited 1× · see citing works
“…of data including genetic, genomic [62], epigenomic [63,64], transcriptomic [65], metabolomic data [66], medical images, biobanks data [67], electronic health records (EHR) [68], scientiﬁc literature data, etc., and are able to combine all of this information to design optimum classiﬁers [69].”
Background
Big Data for Precision Medicine
Leff, D. R., & Yang, G. (2015) · Engineering · doi:10.15302/j-eng-2015075 · Cited by 48 · see citing works
Implementation of the Big Data concept in organizations - possibilities, impediments and challenges
Wielki, J. (2013) · Cited by 47 · see citing works
Machine learning predicts putative hematopoietic stem cells within large single-cell transcriptomics data sets
Hamey, F., & Göttgens, B. (2019) · Experimental Hematology · doi:10.1016/j.exphem.2019.08.009 · Cited by 46 · cited 1× · see citing works
“Machine learning and AI approaches work with different types of data including genetic, genomic [62], epigenomic [63,64], transcriptomic [65], metabolomic data [66], medical images, biobanks data [67], electronic health records (EHR) [68], scientiﬁc literature data, etc., and are able to combine…”
Background
Improving the Use of Big Data Analytics within Electronic Health Records: A Case Study based OpenEHR
Khennou, F., Khamlichi, Y. I., & Chaoui, N. E. H. (2018) · Procedia Computer Science · doi:10.1016/j.procs.2018.01.098 · Cited by 42 · cited 1× · see citing works
“Every record is comprised of one modiﬁable ﬁle, which means that doctors can implement changes over time with no paperwork and no danger of data replication [117].”
Background
Overview of Sequence Data Formats
Zhang, H. (2016) · Methods in molecular biology · doi:10.1007/978-1-4939-3578-9_1 · Cited by 39 · cited 1× · see citing works
“However, the last decade has seen remarkable progress in the development of standard genomic data formats such as FASTQ, BAM/CRAM, and VCF ﬁles [21].”
Precision Medicine and Big Data
Schaefer, G. O., Tai, E. S., & Sun, S. H.-L. (2019) · Asian Bioethics Review · doi:10.1007/s41649-019-00094-2 · Cited by 38 · cited 2× · see citing works
“By linking together diverse datasets to reveal hitherto-unknown casual pathways and correlations, big data allows for far more precision and tailoring than was ever before possible [4].”
Background
Hidden big data analytics issues in the healthcare industry
Strang, K. D., & Sun, Z. (2019) · Health Informatics Journal · doi:10.1177/1460458219854603 · Cited by 37 · cited 1× · see citing works
“We will move from standard regression-based methods, which are a subset of supervised machine learning methods, because the standard regression models can be used in the machine learning framework to learn from the data and provide outcome predictions based on the inputs [130].”
Method
Personalized medicine
Cutter, G., & Liu, Y. (2012) · Neurology Clinical Practice · doi:10.1212/cpj.0b013e318278c328 · Cited by 36 · cited 1× · see citing works
“Other successful personalized medicine examples of “treatment–biomarker” combinations are in colon cancer (Erbitux–EFGR) and lung cancer (Xalkori–ALK) [76].”
Background
REVIEW ARTICLE ON GENE THERAPY
PM, P., PD, C., Sahu, M., & NJ, D. (2012) · International Journal of Genetics · doi:10.9735/0975-2862.4.1.74-79 · Cited by 34 · cited 1× · see citing works
“Gene therapy is an experimental technique that uses genes to treat or prevent disease [84].”
Background
Data protection legislation: A very hungry caterpillar
van Loenen, B., Kulk, S., & Ploeger, H. D. (2016) · Government Information Quarterly · doi:10.1016/j.giq.2016.04.002 · Cited by 33 · cited 1× · see citing works
“Based on such problems, most countries have created legislative principles to secure the personal health care privacy and conﬁdentiality of medical records such as the HIPAA under the Privacy Rule of 2003 in the United States [134,135].”
Background
Genomic risk prediction of aromatase inhibitor‐related arthralgia in patients with breast cancer using a novel machine‐learning algorithm
Reinbolt, R. E., Sonis, S. T., Timmers, C. D., Fernández‐Martínez, J. L., Cernea, A., deAndrés‐Galiana, E. J., Hashemi, S., Miller, K. P., Pilarski, R., & Lustberg, M. B. (2017) · Cancer Medicine · doi:10.1002/cam4.1256 · Cited by 33 · cited 1× · see citing works
“There are multiple applications of this methodology in the ﬁeld of proteomics [95], genomics [96], clinical prognosis [97], cancer treatment [98], aging [99], analysis of defective pathways [100,101], and drug repositioning [102].”
MethodSelf-citation
The Impact of Pharmacogenomics Research on Drug Development
Liou, S., Stringer, F., & Hirayama, M. (2012) · Drug Metabolism and Pharmacokinetics · doi:10.2133/dmpk.dmpk-11-rv-093 · Cited by 32 · cited 1× · see citing works
“Additionally, the epigenomic regulation of the transcriptome can be achieved via epigenomics through chemical compounds that bind to the DNA and alter gene expression [73].”
Background
Importance of Big Data in Precision and Personalized Medicine
Naqvi, M. R., Jaffar, M. A., Aslam, M., Shahzad, S. K., Iqbal, M. W., & Farooq, A. (2020) · International Congress on Human-Computer Interaction, Optimization and Robotic Applications (ICHORA) · doi:10.1109/hora49412.2020.9152842 · Cited by 31 · cited 1× · see citing works
“Personalized or precision medicine is an emerging medical practice based on a data-driven approach that considers relevant medical, genetic, behavioral, and environmental information about an individual to determine patient-specific therapy [2,4,5].”
Background
Transmission protocol for secure big data in two-hop wireless networks with cooperative jamming
Shen, Y., & Zhang, Y. (2014) · Information Sciences · doi:10.1016/j.ins.2014.05.037 · Cited by 29 · cited 1× · see citing works
“Moreover, there are various open software applications that can track cell phone locations and the names of social media users through MAC and GPS big data, which might be used for malicious reasons [132,133].”
Background
Artificial intelligence powered statistical genetics in biobanks
Narita, A., Ueki, M., & Tamiya, G. (2020) · Journal of Human Genetics · doi:10.1038/s10038-020-0822-y · Cited by 28 · cited 1× · see citing works
“…types of data including genetic, genomic [62], epigenomic [63,64], transcriptomic [65], metabolomic data [66], medical images, biobanks data [67], electronic health records (EHR) [68], scientiﬁc literature data, etc., and are able to combine all of this information to design optimum…”
Background
Design of Biomedical Robots for Phenotype Prediction Problems
deAndrés‐Galiana, E. J., Fernández‐Martínez, J. L., & Sonis, S. T. (2016) · Journal of Computational Biology · doi:10.1089/cmb.2016.0008 · Cited by 27 · cited 1× · see citing works
“There is no unique model capable of solving this problem perfectly, so we need to explore different techniques, obtaining a solution with its uncertainty assessment, using a consensus strategy [94].”
MethodSelf-citation
Analysis of defective pathways and drug repositioning in Multiple Sclerosis via machine learning approaches
deAndrés‐Galiana, E. J., Bea, G., Fernández‐Martínez, J. L., & Saligan, L. N. (2019) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2019.103492 · Cited by 24 · cited 1× · see citing works
“There are multiple applications of this methodology in the ﬁeld of proteomics [95], genomics [96], clinical prognosis [97], cancer treatment [98], aging [99], analysis of defective pathways [100,101], and drug repositioning [102].”
MethodSelf-citation
On the prediction of Hodgkin lymphoma treatment response
deAndrés‐Galiana, E. J., Fernández‐Martínez, J. L., Luaces, O., del Coz, J. J., Fernández, R., Solano, J., Nogues, E., Zanabili, Y., Alonso, J. M. N., Páyer, Á. R., et al. (2015) · Clinical & Translational Oncology · doi:10.1007/s12094-015-1285-z · Cited by 24 · cited 2× · see citing works
“There are multiple applications of this methodology in the ﬁeld of proteomics [95], genomics [96], clinical prognosis [97], cancer treatment [98], aging [99], analysis of defective pathways [100,101], and drug repositioning [102].”
BackgroundMethodSelf-citation
Nutritionists’ Health Study cohort: a web-based approach of life events, habits and health outcomes
Folchetti, L. G. D., da Silva, I. T., de Almeida-Pititto, B., & Ferreira, S. R. G. (2016) · BMJ Open · doi:10.1136/bmjopen-2016-012081 · Cited by 23 · cited 2× · see citing works
“…data analyses will eventually be beneﬁcial for the patients themselves, especially for those who can utilize telemedicine and remote patient monitoring to their advantage, in order to enjoy more ﬂexible and convenient access to care, which in turn will help them to live healthier lives [110–112].”
Background
Predictive Precision Medicine: Towards the Computational Challenge
Tuena, C., Semonella, M., Fernández‐Álvarez, J., Colombo, D., & Cipresso, P. (2019) · Springer eBooks · doi:10.1007/978-3-030-27994-3_5 · Cited by 23 · cited 1× · see citing works
“These advancements have led many research groups to investigate different aspects of personalized medicine such as diagnosis, prognosis, and pharmacogenomics through computational approaches [30].”
Background
Big Data: transforming drug development and health policy decision making
Alemayehu, D., & Berger, M. L. (2016) · Health Services and Outcomes Research Methodology · doi:10.1007/s10742-016-0144-x · Cited by 21 · cited 1× · see citing works
“Data analytics in health care can streamline, innovate, provide security, save lives, and give conﬁdence and clarity [126].”
Background
Metformin pharmacogenomics: a genome-wide association study to identify genetic and epigenetic biomarkers involved in metformin anticancer response using human lymphoblastoid cell lines
Niu, N., Liu, T., Cairns, J., Ly, R. C., Tan, X., Deng, M., Fridley, B. L., Kalari, K. R., Abo, R., Jenkins, G. D., et al. (2016) · Human Molecular Genetics · doi:10.1093/hmg/ddw301 · Cited by 19 · see citing works
Cancer modeling and network biology: Accelerating toward personalized medicine
Masoudi‐Nejad, A., & Wang, E. (2014) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2014.06.005 · Cited by 19 · cited 2× · see citing works
“The patient’s individual characteristics are used to modify treatment in a way that might be more intricate compared to the standard course [27].”
Background
Predictive Diagnostic Pathology in the Target Therapy Era in Breast Cancer
Marchiò, C., Balmativola, D., Castiglione, R., Annaratone, L., & Sapino, A. (2016) · Current Drug Targets · doi:10.2174/1389450116666150203121218 · Cited by 18 · cited 1× · see citing works
“MammaPrint is a microarray-based signature method using formalin-ﬁxed-parafﬁn-embedded (FFPE) or fresh tissue for microarray analysis [57,58].”
Method
A computational algorithm for personalized medicine in schizophrenia
Lee, B. S., McIntyre, R. S., Gentle, J. E., Park, N. S., Chiriboga, D. A., Lee, Y. L., Singh, S., & McPherson, M. (2017) · Schizophrenia Research · doi:10.1016/j.schres.2017.05.001 · Cited by 18 · cited 1× · see citing works
“The proposed algorithm can be easily adopted in many other clinical practices for personalized medicine [86].”
Background
Swift action needed to close the skills gap in bioinformatics
MacLean, M., & Miles, C. G. (1999) · Nature · doi:10.1038/43269 · Cited by 17 · cited 1× · see citing works
“Data volumes are increasing continuously and beyond comprehension, while there is a shortage of bioinformaticians in the current scenario [91].”
Background
Big Data and Strategy: Theoretical Foundations and New Opportunities
Mazzei, M. J., & Noble, D. W. (2019) · IntechOpen eBooks · doi:10.5772/intechopen.84819 · Cited by 17 · cited 1× · see citing works
“Therefore, better understating of these data and better strategic plans can cure more patients in the most diverse areas [118].”
Background
Personalized Computational Models as Biomarkers
Kölch, W., & Fey, D. (2017) · Journal of Personalized Medicine · doi:10.3390/jpm7030009 · Cited by 17 · cited 1× · see citing works
“Most importantly, in personalized medicine, ODE models are applied for individual biomarker discovery [49], drug response, and tailored treatments [50].”
Background
A Deep Learning Model for Epigenomic Studies
Bosco, G. L., Rizzo, R., Fiannaca, A., Rosa, M. L., & Urso, A. M. (2016) · International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) · doi:10.1109/sitis.2016.115 · Cited by 16 · cited 1× · see citing works
“Machine learning and AI approaches work with different types of data including genetic, genomic [62], epigenomic [63,64], transcriptomic [65], metabolomic data [66], medical images, biobanks data [67], electronic health records (EHR) [68], scientific literature data, etc.”
Background
Applications of personalised signalling network models in precision oncology
Hastings, J. F., O’Donnell, Y. E. I., Fey, D., & Croucher, D. R. (2020) · Pharmacology & Therapeutics · doi:10.1016/j.pharmthera.2020.107555 · Cited by 16 · cited 1× · see citing works
“Most importantly, in personalized medicine, ODE models are applied for individual biomarker discovery [49], drug response, and tailored treatments [50].”
Background
Prognostic networks for unraveling the biological mechanisms of Sarcopenia
Cernea, A., Fernández‐Martínez, J. L., deAndrés‐Galiana, E. J., Fernández‐Muñiz, Z., Bermejo-Millo, J. C., González‐Blanco, L., Solano, J. J., Abizanda, P., Coto‐Montes, A. M., & Caballero, B. (2019) · Mechanisms of Ageing and Development · doi:10.1016/j.mad.2019.111129 · Cited by 14 · cited 1× · see citing works
“There are multiple applications of this methodology in the ﬁeld of proteomics [95], genomics [96], clinical prognosis [97], cancer treatment [98], aging [99], analysis of defective pathways [100,101], and drug repositioning [102].”
MethodSelf-citation
Computational Approaches in Precision Medicine
Enríquez, J. E., Mejía-Pedroza, R. A., & Lemus, E. H. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-809411-2.00013-1 · Cited by 12 · cited 1× · see citing works
“…there is a need for new computational approaches such as faster, more integrated processors, larger computer memories, improved sensors, new much sophisticated algorithms, methodologies and cloud computing, which may guide future clinical practice by providing clinically useful information [6,7].”
Background
Genomics innovation: transforming healthcare, business, and the global economy
Jiménez‐Sánchez, G. (2015) · Genome · doi:10.1139/gen-2015-0121 · Cited by 12 · see citing works
Systems and Network-Based Approaches for Personalized Medicine
Janga, S. C. (2014) · Current Synthetic and Systems Biology · doi:10.4172/2332-0737.1000e109 · Cited by 11 · cited 4× · see citing works
“…on genomic, epigenomic proﬁles, and drug and treatment responses, computational methods can classify the patients into different subtypes that can be helpful in disease prediction, the diagnosis of various cancers, generating disease decision rules, and personalized recommendation systems [29].”
BackgroundInfluential
Design and Implementation of a Telepediatric Primary-Level and Low-Cost System to Reduce Unnecessary Patient Transfers
la Portilla, C. C.-D., Romero, E., & Godoy, J. A. (2016) · Telemedicine Journal and e-Health · doi:10.1089/tmj.2016.0180 · Cited by 10 · cited 2× · see citing works
“…data analyses will eventually be beneﬁcial for the patients themselves, especially for those who can utilize telemedicine and remote patient monitoring to their advantage, in order to enjoy more ﬂexible and convenient access to care, which in turn will help them to live healthier lives [110–112].”
Background
Robust pathway sampling in phenotype prediction. Application to triple negative breast cancer
Cernea, A., Fernández‐Martínez, J. L., deAndrés‐Galiana, E. J., Fernández-Ovies, F. J., Álvarez-Machancoses, Ó., Fernández‐Muñiz, Z., Saligan, L. N., & Sonis, S. T. (2020) · BMC Bioinformatics · doi:10.1186/s12859-020-3356-6 · Cited by 10 · cited 2× · see citing works
“The genetic and epigenetic regulation of the altered pathways in a cell is one of the main topics in pharmacogenomics and consists of understanding how a mutation in the DNA impacts the transcriptome and the proteome downstream [70–72].”
BackgroundSelf-citation
Robust Prediction of Single and Multiple Point Protein Mutations Stability Changes
Álvarez-Machancoses, Ó., deAndrés‐Galiana, E. J., Fernández‐Martínez, J. L., & Kloczkowski, A. (2019) · Biomolecules · doi:10.3390/biom10010067 · Cited by 9 · cited 1× · see citing works
“There are multiple applications of this methodology in the ﬁeld of proteomics [95], genomics [96], clinical prognosis [97], cancer treatment [98], aging [99], analysis of defective pathways [100,101], and drug repositioning [102].”
MethodSelf-citation
The True Cost of Mobility?
Shull, F. J. (2014) · IEEE Software · doi:10.1109/ms.2014.47 · Cited by 8 · cited 1× · see citing works
“Moreover, there are various open software applications that can track cell phone locations and the names of social media users through MAC and GPS big data, which might be used for malicious reasons [132,133].”
Background
Efficient estimation of nonlinear posterior model covariances using maximally sparse cubature rules
Tompkins, M. J. (2012) · Geophysics · doi:10.1190/geo2011-0400.1 · Cited by 8 · see citing works
How to Deal with Uncertainty in Inverse and Classification Problems
Fernández‐Martínez, J. L., Fernández‐Muñiz, Z., Cernea, A., Pallero, J. L. G., deAndrés‐Galiana, E. J., González, L. M. P., Álvarez-Machancoses, Ó., & Fernández-Ovies, F. J. (2020) · Springer geophysics · doi:10.1007/978-3-030-28909-6_15 · Cited by 6 · cited 1× · see citing works
“As we have stated in the conceptualization of big data section, there could be inﬁnite models that solve a biomedical problem due to the huge uncertainty space [93].”
BackgroundSelf-citation
Robust Sampling of Defective Pathways in Multiple Myeloma
Fernández‐Martínez, J. L., deAndrés‐Galiana, E. J., Fernández-Ovies, F. J., Cernea, A., & Kloczkowski, A. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20194681 · Cited by 6 · cited 1× · see citing works
“There are multiple applications of this methodology in the ﬁeld of proteomics [95], genomics [96], clinical prognosis [97], cancer treatment [98], aging [99], analysis of defective pathways [100,101], and drug repositioning [102].”
MethodSelf-citation
Towards Identifying De-anonymisation Risks in Distributed Health Data Silos
Podlesny, N. J., Kayem, A. V. D. M., & Meinel, C. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-27615-7_3 · Cited by 5 · cited 1× · see citing works
“Big data in the health care department has the power to discover new medications [122–124].”
Background
The Utilization of Different Classifiers to Perform Drug Repositioning in Inclusion Body Myositis Supports the Concept of Biological Invariance
Álvarez-Machancoses, Ó., deAndrés‐Galiana, E. J., Fernández‐Martínez, J. L., & Kloczkowski, A. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-61401-0_55 · Cited by 4 · cited 1× · see citing works
“There are multiple applications of this methodology in the ﬁeld of proteomics [95], genomics [96], clinical prognosis [97], cancer treatment [98], aging [99], analysis of defective pathways [100,101], and drug repositioning [102].”
MethodSelf-citation
Deep neural networks for phenotype prediction in rare diseases
deAndrés‐Galiana, E. J., Fernández-Ovies, F. J., Cernea, A., Fernández‐Martínez, J. L., & Kloczkowski, A. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-817133-2.00008-2 · Cited by 4 · see citing works
Self-citation
Real-life clinical data mining: generating hypotheses for evidence-based medicine
Bibault, J. (2020) · Annals of Translational Medicine · doi:10.21037/atm.2019.10.99 · Cited by 4 · cited 1× · see citing works
“Distinct dimensions are included in the deﬁnition of “big data”, namely, volume, velocity, variety, value, variability, visualization, virality, and veracity, which describes the massive volume of structured, semi-structured, and unstructured data (Figure 1) [11–14].”
Background
Impediments in Healthcare Digital Transformation
Furda, R., & Greguš, M. (2018) · International Journal of Applied Research on Public Health Management · doi:10.4018/ijarphm.2019010102 · Cited by 2 · see citing works
Knowledge-based trade, technical change and location environment: the case of small and medium sized enterprises engaged in advanced producer software services in the South East region
Rahman, J. A. (2005) · OpenGrey (Institut de l'Information Scientifique et Technique) · Cited by 1 · see citing works

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Research works printed but not linked: 2

[17]Fernandez Martinez J.L. Fernandez Muniz M.Z. Tompkins M.J. On the topography of the cost functional in linear and nonlinear inverse problems Geophysics 2012 77 W1 W15 10.1190/geo2011-0341.1 · DOI
[114]Zikopoulos P. Eaton C. Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data McGraw-Hill Osborne Media New York, NY, USA 2011

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 · see citing works
From machine learning to deep learning: Advances of the recent data-driven paradigm shift in medicine and healthcare
Chakraborty, C., Bhattacharya, M., Pal, S., & Lee, S. (2023) · Current Research in Biotechnology · doi:10.1016/j.crbiot.2023.100164 · Cited by 295 · see citing works
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 231 · see citing works
Transforming Clinical Research: The Power of High-Throughput Omics Integration
Vitorino, R. M. P. (2024) · Proteomes · doi:10.3390/proteomes12030025 · Cited by 197 · see citing works
“These advances not only improve diagnostic accuracy, but also open new avenues for targeted drug development and precision medicine [45].”
Background
Transforming 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 · see citing works
AI's role in revolutionizing personalized medicine by reshaping pharmacogenomics and drug therapy
Taherdoost, H., & Ghofrani, A. (2024) · Intelligent Pharmacy · doi:10.1016/j.ipha.2024.08.005 · Cited by 125 · see citing works
Frontiers in pancreatic cancer on biomarkers, microenvironment, and immunotherapy
Yu, B., Shao, S., & Ma, W. (2024) · Cancer Letters · doi:10.1016/j.canlet.2024.217350 · Cited by 112 · see citing works
Biomarker discovery in hepatocellular carcinoma (HCC) for personalized treatment and enhanced prognosis
Yu, B., & Ma, W. (2024) · Cytokine & Growth Factor Reviews · doi:10.1016/j.cytogfr.2024.08.006 · Cited by 102 · see citing works
A review of cancer data fusion methods based on deep learning
Zhao, Y., Li, X., Zhou, C., Peng, H., Zheng, Z., Chen, J., & Ding, W. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102361 · Cited by 86 · see citing works
Emerging applications of machine learning in genomic medicine and healthcare
Chafai, N., Bonizzi, L., Botti, S., & Badaoui, B. (2023) · Critical Reviews in Clinical Laboratory Sciences · doi:10.1080/10408363.2023.2259466 · Cited by 81 · see citing works
“For example, using biomarkers to forecast the level of cholesterol in the blood [11].”
Method
THE IMPACT OF BIG DATA ON HEALTHCARE PRODUCT DEVELOPMENT: A THEORETICAL AND ANALYTICAL REVIEW
Ogundipe, D. O. (2024) · International Medical Science Research Journal · doi:10.51594/imsrj.v4i3.932 · Cited by 69 · see citing works
A comprehensive and systematic literature review on the big data management techniques in the internet of things
Naghib, A., Navimipour, N. J., Hosseinzadeh, M., & Sharifi, A. (2022) · Wireless Networks · doi:10.1007/s11276-022-03177-5 · Cited by 75 · see citing works
Interpretable Machine Learning for Discovery: Statistical Challenges and Opportunities
Allen, G. I., Gan, L., & Zheng, L. (2023) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-040120-030919 · Cited by 72 · see citing works
Comparative review of big data analytics and GIS in healthcare decision-making
Akindote, O. J., Adegbite, A. O., Dawodu, S. O., Omotosho, A., Anyanwu, A. C., & Maduka, C. P. (2023) · World Journal of Advanced Research and Reviews · doi:10.30574/wjarr.2023.20.3.2589 · Cited by 65 · see citing works
“Big data analytics in healthcare represents a paradigm shift, harnessing the power of advanced analytics to derive meaningful insights from the vast and varied datasets within the healthcare ecosystem (Hassan et al., 2022; Saheb & Izadi, 2019; L. Wang & Alexander, 2020).”
An extensive review on lung cancer therapeutics using machine learning techniques: state-of-the-art and perspectives
Ahmad, S., & Raza, K. (2024) · Journal of drug targeting · doi:10.1080/1061186x.2024.2347358 · Cited by 65 · see citing works
“Machine learning algorithms can also contribute to personalised medicine by analysing patient-specific data, such as genetic information or clinical records, to predict individual responses to specific drugs [10,11].”
Background
The Metaverse as a Virtual Model of Platform Urbanism: Its Converging AIoT, XReality, Neurotech, and Nanobiotech and Their Applications, Challenges, and Risks
Bibri, S. E. (2023) · Smart Cities · doi:10.3390/smartcities6030065 · Cited by 58 · see citing works
Artificial intelligence in stroke rehabilitation: From acute care to long-term recovery
Kopalli, S. R., Shukla, M., Jayaprakash, B., Kundlas, M., Srivastava, A., Jagtap, J., Gulati, M., Chigurupati, S., Ibrahim, E., Khandige, P. S., et al. (2025) · Neuroscience · doi:10.1016/j.neuroscience.2025.03.017 · Cited by 54 · see citing works
Revolutionizing Biological Science: The Synergy of Genomics in Health, Bioinformatics, Agriculture, and Artificial Intelligence
Biswas, A., Kumari, A., Gaikwad, D. S., & Pandey, D. K. (2023) · OMICS A Journal of Integrative Biology · doi:10.1089/omi.2023.0197 · Cited by 56 · see citing works
“As technology and research methods continue to evolve, genetic exploration is expected to lead to even more exciting breakthroughs and transformative applications in the future (Hassan et al., 2022).”
From screens to scenes: A survey of embodied AI in healthcare
Liu, Y., Cao, X., Chen, T., Jiang, Y., You, J., Wu, M., Wang, X., Feng, M., Jin, Y., & Chen, J. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103033 · Cited by 59 · see citing works
Progress in the application of artificial intelligence in skin wound assessment and prediction of healing time
Chen, M. (2024) · American Journal of Translational Research · doi:10.62347/myhe3488 · Cited by 48 · see citing works
The promise of explainable deep learning for omics data analysis: Adding new discovery tools to AI
Santorsola, M., & Lescai, F. (2023) · New Biotechnology · doi:10.1016/j.nbt.2023.06.002 · Cited by 44 · see citing works
The double-edged sword of nutraceuticals: comprehensive review of protective agents and their hidden risks
Ashrafpour, S., & Ashrafpour, M. (2025) · Frontiers in Nutrition · doi:10.3389/fnut.2025.1524627 · Cited by 47 · see citing works
“By translating genomic data into actionable insights, these tools play a crucial role in developing personalized monitoring strategies (112), particularly for elderly patients or those on polypharmacy regimens who use high-risk nutraceuticals.”
Federated Learning Based Privacy Preserving Cloud Computing Platform for Health Management
Kashyap, R., Mustafa, A. M., Abdulhasan, M. M., Nimah, N. R., & Dunka, V. K. (2025) · OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry (OTCON) · doi:10.1109/otcon65728.2025.11071159 · Cited by 37 · see citing works
“It's ideal for banks and hospitals [14-15].”
BIG DATA ANALYTICS IN HEALTHCARE: A REVIEW OF RECENT ADVANCES AND POTENTIAL FOR PERSONALIZED MEDICINE
Obijuru, A., Arowoogun, J. O., Onwumere, C., Odilibe, I. P., Anyanwu, E. C., & Daraojimba, A. I. (2024) · International Medical Science Research Journal · doi:10.51594/imsrj.v4i2.810 · Cited by 34 · see citing works
Bio‐Functionalized MXenes: Synthesis and Versatile Applications
Vojoudi, H., & Soroush, M. (2025) · Advanced Healthcare Materials · doi:10.1002/adhm.202500359 · Cited by 34 · see citing works
Ethical and social issues related to AI in healthcare
Mondal, H., & Mondal, S. (2024) · Methods in microbiology · doi:10.1016/bs.mim.2024.05.009 · Cited by 32 · see citing works
Wolbachia-Based Approaches to Controlling Mosquito-Borne Viral Threats: Innovations, AI Integration, and Future Directions in the Context of Climate Change
Branda, F., Cella, E., Scarpa, F., Slavov, S. N., Bevivino, A., Moretti, R., Abate, L. D., Pecchia, L., Rizzo, A., Defilippo, F., et al. (2024) · Viruses · doi:10.3390/v16121868 · Cited by 30 · see citing works
“Wolbachia can influence mosquito fitness and competitiveness, potentially disrupting existing species interactions, which may impact food webs or lead to the emergence of more resilient vector species [89–91] (Figure 3).”
Genomic insights and advanced machine learning: characterizing autism spectrum disorder biomarkers and genetic interactions
Nahas, L. D., Datta, A., Alsamman, A. M., Adly, M. H., Al-Dewik, N. I. E., Sekaran, K., Sasikumar, K., Verma, K., Doss, C. G. P., & Zayed, H. (2023) · Metabolic Brain Disease · doi:10.1007/s11011-023-01322-3 · Cited by 30 · see citing works
“(Hassan et al. 2022).”
Advancing personalized medicine: Integrating statistical algorithms with omics and nano-omics for enhanced diagnostic accuracy and treatment efficacy
Coşkun, A., Ertaylan, G., Pusparum, M., Hoof, R. V., Kaya, Z. Z., Khosravi, A., & Zarrabi, A. (2024) · Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease · doi:10.1016/j.bbadis.2024.167339 · Cited by 29 · see citing works
Toward Fully Automated Personalized Orthopedic Treatments: Innovations and Interdisciplinary Gaps
Luo, Y. (2024) · Bioengineering · doi:10.3390/bioengineering11080817 · Cited by 29 · see citing works
“Genomic data can provide insights into a patient’s tissue regeneration capabilities [62].”
Background
A comprehensive survey on secure healthcare data processing with homomorphic encryption: attacks and defenses
Lee, C. H., Lim, K. H., & Eswaran, S. (2025) · Discover Public Health · doi:10.1186/s12982-025-00505-w · Cited by 33 · see citing works
“The advent of technologies such as EHRs [2–4], telemedicine [5, 6], and personalised medicine [7, 8] underscores the necessity for robust data privacy and security mechanisms.”
Advancement in public health through machine learning: a narrative review of opportunities and ethical considerations
Dhanda, S. S., Panwar, D., Lin, C., Sharma, T. K., Rastogi, D., Bindewari, S., Singh, A. M., Li, Y., Agarwal, N. K., & Agarwal, S. (2025) · Journal Of Big Data · doi:10.1186/s40537-025-01201-x · Cited by 27 · see citing works
Genetic Determinants of Endurance: A Narrative Review on Elite Athlete Status and Performance
Bicakci, B., Cięszczyk, P., & Humińska‐Lisowska, K. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms252313041 · Cited by 35 · see citing works
Machine Learning Differentiation of Autism Spectrum Sub-Classifications
Thapa, R., Garikipati, A., Ciobanu, M., Singh, N. P., Browning, E., Decurzio, J., Barnes, G., Dinenno, F., Mao, Q., & Das, R. (2023) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-023-06121-4 · Cited by 25 · see citing works
“…in personalized medicine and has been validated for a variety of prediction and diagnostic tasks for acute and chronic medical issues ( Cognoa - Leading the Way for Pediatric Behavioral Health , n.d.; Hassan et al., 2022; Lam et al., 2022; Thapa et al., 2022; Tso et al., 2022; Varma et al., n.d.).”
Background
Digital Health Revolution: Enhancing Well-Being Through Technology
Singh, V., & Singh, A. (2023) · Advances in logistics, operations, and management science book series · doi:10.4018/979-8-3693-2065-5.ch016 · Cited by 24 · see citing works
Sample Preparation for Multi‐Omics Analysis: Considerations and Guidance for Identifying the Ideal Workflow
Woodland, B., Farrell, L. A., Brockbals, L., Rezcallah, M. C., Brennan, A., Sunnucks, E. J., Gould, S. T., Stanczak, A. M., O’Rourke, M. B., & Padula, M. P. (2025) · PROTEOMICS · doi:10.1002/pmic.13983 · Cited by 25 · see citing works
“High-throughput processes have resulted in vast amounts of data meaning that analysis is no longer intuitive and requires highly specialised computational approaches and infrastructure [264].”
Bioinformatics and personalized medicine in the U.S.: A comprehensive review: Scrutinizing the advancements in genomics and their potential to revolutionize healthcare delivery
Olorunsogo, T. O., Balogun, O. D., Ayo-Farai, O., Ogundairo, O., Maduka, C. P., Okongwu, C. C., & Onwumere, C. (2024) · World Journal of Advanced Research and Reviews · doi:10.30574/wjarr.2024.21.1.0016 · Cited by 21 · see citing works
“Hassan et al. (2022) review the significance of big data analytics in personalized medicine and health care, focusing on machine learning perspectives.”
Background
Foundations for a Personalized Psycho-Oncology: The State of the Art
Perna, G., Pinto, E., Spiti, A., Torti, T., Cucchi, M., & Caldirola, D. (2024) · Journal of Personalized Medicine · doi:10.3390/jpm14090892 · Cited by 20 · see citing works
“This approach represents a paradigm shift away from the one-size-fits-all model towards providing the right treatment to the right person at the right time, improving therapeutic outcomes and minimizing side effects [1].”
Background
Big data analytics in healthcare: current practices, innovations, and future prospects
Shahzad, K., Khan, S. A., Latif, M., Javeed, A. M. D., & Iqbal, A. (2025) · Journal Of Big Data · doi:10.1186/s40537-025-01288-2 · Cited by 20 · see citing works
Precision medicine in gynecological cancer (Review)
Aravantinou‐Fatorou, A., GEORGAKOPOULOU, V. E., Dimopoulos, M. A., & Liontos, M. (2025) · Biomedical Reports · doi:10.3892/br.2025.1921 · Cited by 19 · see citing works
“AIKATERINI ARAVANTINOU‑FATOROU 1 , VASILIKI EPAMEINONDAS GEORGAKOPOULOU 2 , MELETIOS ATHANASIOS DIMOPOULOS 1 and MICHALIS LIONTOS 1 datasets to identify which therapies will be beneficial for patients depending on their genetic and molecular profiling (4).”
Influential
Artificial intelligence in orthopedics: fundamentals, current applications, and future perspectives
Song, J., Wang, G.-C., Wang, S., He, C., Zhang, Y., Chen, X., & Su, J. (2025) · Military Medical Research · doi:10.1186/s40779-025-00633-z · Cited by 19 · see citing works
“Supervised ML facilitates the development of predictive models, enhancing capabilities in areas such as disease detection and therapeutic outcome forecasting [28, 29].”
Integrating pharmacogenomic testing into personalized medicine practices in the USA: Implications for medication quality control and therapeutic efficacy
Bature, J. T., Eruaga, M. A., & Itua, E. O. (2024) · GSC Biological and Pharmaceutical Sciences · doi:10.30574/gscbps.2024.26.3.0081 · Cited by 19 · see citing works
The current landscape of personalised preventive approaches for non-communicable diseases: A scoping review
Farina, S., Osti, T., Russo, L., Maio, A., Scarsi, N., Savoia, C., Taha, A., Villani, L., Pastorino, R., & Boccia, S. (2025) · PLoS ONE · doi:10.1371/journal.pone.0317379 · Cited by 18 · see citing works
Quantum computing and the implementation of precision medicine
Nassir, N., Hashmi, M., Raji, K. G., Jamalalail, B. A., Maksymowsky, A., Scherer, S. W., Alsheikh-Ali, A. A. A., & Uddin, M. J. (2025) · npj Genomic Medicine · doi:10.1038/s41525-025-00537-w · Cited by 18 · see citing works
Artificial Intelligence for Personalized Genetics and New Drug Development: Benefits and Cautions
Gallo, C. (2023) · Bioengineering · doi:10.3390/bioengineering10050613 · Cited by 18 · see citing works
“Among the application areas of the technology under consideration to the health care sector, personalized genetics represents fertile ground for the use of predictive analytics [48].”
Background
Revolutionizing Oncology: A Comprehensive Review of Digital Health Applications
Borkar, S., Chakole, S. V., Prasad, R., & Bansod, S. (2024) · Cureus · doi:10.7759/cureus.59203 · Cited by 21 · see citing works
“Additionally, machine learning models can evolve and adapt by continuously learning from new data, providing clinicians with the latest insights into cancer biology and treatment strategies [16].”
Bridging the gap: From petri dish to patient - Advancements in translational drug discovery
El‐Tanani, M., Rabbani, S. A., El‐Tanani, Y., Matalka, I. I., & Khalil, I. A. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e41317 · Cited by 17 · see citing works
The Intersection of Artificial Intelligence and Big Data in Drug Discovery: A Review of Current Trends and Future Implications
Ashiwaju, B. I., Orikpete, O. F., & Uzougbo, C. G. (2023) · Matrix Science Pharma · doi:10.4103/mtsp.mtsp_14_23 · Cited by 20 · see citing works
A Review of Big Data Trends and Challenges in Healthcare
Baloch, L., Bazai, S. U., Marjan, S., Aftab, F., Aslam, S., Neo, T., & Amphawan, A. (2023) · International Journal of Technology · doi:10.14716/ijtech.v14i6.6643 · Cited by 17 · see citing works
Towards Autonomous Healthcare: Integrating Artificial Intelligence (AI) for Personalized Medicine and Disease PredictionPreprint
Rane, N. L., Choudhary, S., & Rane, J. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4637894 · Cited by 18 · see citing works
The data scientist as a mainstay of the tumor board: global implications and opportunities for the global south
Tan, M. J. T., Lichlyter, D. A., Maravilla, N. M. A. T., Schrock, W. J., Ting, F. I. L., Choa-Go, J. M., Francisco, K. K. Y., Byers, M. C., Karim, H. B. A., & AlDahoul, N. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1535018 · Cited by 18 · see citing works
Leveraging machine learning in nursing: innovations, challenges, and ethical insights
Yip, S. S. W., Ning, S., Wong, N. Y. K., Chan, J., Ng, K. S., Kwok, B. O. T., Anders, R. L., & Lam, S. C. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1514133 · Cited by 15 · see citing works
What is digital health, eHealth, uHealth and Healthcare 4.0?
Mangion, A., & Piller, N. (2025) · Informatics and Health · doi:10.1016/j.infoh.2025.06.002 · Cited by 14 · see citing works
Harnessing the power of artificial intelligence in pharmaceuticals: Current trends and future prospects
Aritra, S., Shikha, C. B., & Indu, S. (2025) · Intelligent Pharmacy · doi:10.1016/j.ipha.2024.12.001 · Cited by 17 · see citing works
Pancreatic Tumor‐Targeting Stemsome Therapeutics
Park, J., Park, J. Y., Jeong, Y., Park, J., Park, Y. H., Kim, S., & Khang, D. (2023) · Advanced Materials · doi:10.1002/adma.202300934 · Cited by 14 · see citing works
Internet of Things and Big Data Analytics in Preventive Healthcare: A Synthetic Review
(2024) · doi:10.3390/electronics13183642 · Cited by 19 · see citing works
Advancing Medical Research Through Artificial Intelligence: Progressive and Transformative Strategies: A Literature Review
Al-Qudimat, A. R., Fares, Z. E., Elaarag, M., Osman, M., Al‐Zoubi, R. M., & Aboumarzouk, O. M. (2025) · Health Science Reports · doi:10.1002/hsr2.70200 · Cited by 13 · see citing works
“Based on these predictions, it provides tailored recommendations and treatments that can significantly impact an individual's well ‐ being [65 – 67].”
Smart Elderly Healthcare Services in Industry 5.0: A Survey of Key Enabling Technologies and Future Trends
Wang, X., Huang, L., Wang, D., Liu, L., & Guo, P. (2025) · IEEE Access · doi:10.1109/access.2025.3596694 · Cited by 15 · see citing works
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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: Hassan, Mubashir
Awan, Faryal Mehwish
Naz, Anam
Cernea, Ana
Fernández-Martínez, Juan Luis
Kloczkowski, Andrzej
Venue: International Journal of Molecular Sciences
Publisher: MDPI AG
Published: 2022-04-22
Volume: 23
Issue: 9
Pages: 4645
ISSN: 1422-0067 (ISSN-L)
DOI: 10.3390/ijms23094645
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: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
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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Innovations in Genomics and Big Data Analytics for Personalized Medicine and Health Care: A Review

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