Cited by 522Technology and Human Factors in Education and HealthAdvanced Biosensing Techniques and ApplicationsMetabolomics and Mass Spectrometry Studies

The DOI matches its Crossref record; 209 of the 212 references it cites were found in a registry or an index.

Enabling Technologies for Personalized and Precision Medicine

Open access

Ho, Dean, Quake, Stephen R., McCabe, Edward R.B., Chng, Wee Joo, Chow, Edward K., Ding, Xianting, Gelb, Bruce D., Ginsburg, Geoffrey S., Hassenstab, Jason, Ho, Chih-Ming, Mobley, William C., Nolan, Garry P., Rosen, Steven T., Tan, Patrick, Yen, Yun, Zarrinpar, Ali·2020·Trends in Biotechnology

DOI 10.1016/j.tibtech.2019.12.021PMC PMC7924935

Open this paper in CoChat — free account

Abstract

Individualizing patient treatment is a core objective of the medical field. Reaching this objective has been elusive owing to the complex set of factors contributing to both disease and health; many factors, from genes to proteins, remain unknown in their role in human physiology. Accurately diagnosing, monitoring, and treating disorders requires advances in biomarker discovery, the subsequent development of accurate signatures that correspond with dynamic disease states, as well as therapeutic interventions that can be continuously optimized and modulated for dose and drug selection. This work highlights key breakthroughs in the development of enabling technologies that further the goal of personalized and precision medicine, and remaining challenges that, when addressed, may forge unprecedented capabilities in realizing truly individualized patient care.

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

Crossref · 10.1016/j.tibtech.2019.12.021

Cites

209 of 209 references verified0 citation errors · 0 not checkable

Statistics

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?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Fully integrated wearable sensor arrays for multiplexed in situ perspiration analysis
Gao, W., Emaminejad, S., Nyein, H. Y. Y., Challa, S., Chen, K., Peck, A., Fahad, H. M., Ota, H., Shiraki, H., Kiriya, D., et al. (2016) · Nature · doi:10.1038/nature16521 · Cited by 5,087 · cited 1×
“Such noninvasive, multiplexed monitoring may ultimately identify personalized biomarker signatures [150].”
Background
Reconstituting organ-level lung functions on a chip
Huh, D., Matthews, B. D., Mammoto, A., Montoya-Zavala, M., Hsin, H. Y., & Ingber, D. E. (2010) · Science · doi:10.1126/science.1188302 · Cited by 4,250
Nucleic acid detection with CRISPR-Cas13a/C2c2
Gootenberg, J. S., Abudayyeh, O. O., Lee, J. W., Essletzbichler, P., Dy, A. J., Joung, J., Verdine, V. K., Donghia, N. M., Daringer, N. M., Freije, C. A., et al. (2017) · Science · doi:10.1126/science.aam9321 · Cited by 3,887
Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning
Coudray, N., Ocampo, P. S., Sakellaropoulos, T., Narula, N., Snuderl, M., Fenyö, D., Moreira, A. L., Razavian, N., & Tsirigos, A. (2018) · Nature Medicine · doi:10.1038/s41591-018-0177-5 · Cited by 2,930
End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography
Ardila, D., Kiraly, A. P., Bharadwaj, S., Choi, B., Reicher, J. J., Peng, L., Tse, D., Etemadi, M., Ye, W., Corrado, G., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0447-x · Cited by 2,167
The Path to Personalized Medicine
Hamburg, M. A., & Collins, F. S. (2010) · New England Journal of Medicine · doi:10.1056/nejmp1006304 · Cited by 1,970
Genomics is failing on diversity
Popejoy, A. B., & Fullerton, S. M. (2016) · Nature · doi:10.1038/538161a · Cited by 1,924
Vemurafenib in Multiple Nonmelanoma Cancers with BRAF V600 Mutations
Hyman, D. M., Puzanov, I., Subbiah, V., Faris, J. E., Chau, I., Blay, J.-Y., Wolf, J., Raje, N. S., Diamond, E. L., Hollebecque, A., et al. (2015) · New England Journal of Medicine · doi:10.1056/nejmoa1502309 · Cited by 1,764
Estimation of clinical trial success rates and related parameters
Wong, C. H., Siah, K. W., & Lo, A. W. (2019) · Biostatistics · doi:10.1093/biostatistics/kxx069 · Cited by 1,585
Big data and machine learning algorithms for health-care delivery
Ngiam, K. Y., & Khor, I. W. (2019) · The Lancet Oncology · doi:10.1016/s1470-2045(19)30149-4 · Cited by 1,424

Show all 212 references

Research works printed but not linked: 1

2018, Cancer Discov., 8, 379

Sorted by citations

Advances and applications of nanophotonic biosensors
Altug, H., Oh, S., Maier, S. A., & Homola, J. (2022) · Nature Nanotechnology · doi:10.1038/s41565-021-01045-5 · Cited by 914
Advancing Precision Medicine: A Review of Innovative In Silico Approaches for Drug Development, Clinical Pharmacology and Personalized Healthcare
Marques, L., Costa, B., Pereira, M., Silva, A., Santos, J., Saldanha, L., da Silva, I. C. B., Magalhães, P., Schmidt, S., & Vale, N. (2024) · Pharmaceutics · doi:10.3390/pharmaceutics16030332 · Cited by 444
Precision medicine in the era of artificial intelligence: implications in chronic disease management
Subramanian, M., Wojtusciszyn, A., Favre, L., Boughorbel, S., Shan, J., Letaief, K. B., Pitteloud, N., & Chouchane, L. (2020) · Journal of Translational Medicine · doi:10.1186/s12967-020-02658-5 · Cited by 362
Functional Fiber Materials to Smart Fiber Devices
Chen, C., Feng, J., Li, J., Guo, Y., Shi, X., & Peng, H. (2022) · Chemical Reviews · doi:10.1021/acs.chemrev.2c00192 · Cited by 322
Nano-Based Approved Pharmaceuticals for Cancer Treatment: Present and Future Challenges
Rodríguez, F., Caruana, P., de la Fuente, N., Español, P., Gámez, M., Balart, J., Llurba, E., Rovira, R., Ruiz, R., Martín-Lorente, C., et al. (2022) · Biomolecules · doi:10.3390/biom12060784 · Cited by 296
Artificial Intelligence in Dermatology Image Analysis: Current Developments and Future Trends
Li, Z., Koban, K. C., Schenck, T. L., Giunta, R. E., Li, Q., & Sun, Y. (2022) · Journal of Clinical Medicine · doi:10.3390/jcm11226826 · Cited by 291
Shared and unique brain network features predict cognitive, personality, and mental health scores in the ABCD study
Chen, J., Tam, A., Kebets, V., Orbán, C., Ooi, L. Q. R., Asplund, C. L., Marek, S. A., Dosenbach, N. U. F., Eickhoff, S. B., Bzdok, D., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-29766-8 · Cited by 240
Artificial intelligence and machine learning in precision medicine: A paradigm shift in big data analysis
Sahu, M., Gupta, R. V., Ambasta, R. K., & Kumar, P. (2022) · Progress in molecular biology and translational science · doi:10.1016/bs.pmbts.2022.03.002 · Cited by 214
AI-driven innovations in Alzheimer's disease: Integrating early diagnosis, personalized treatment, and prognostic modelling
Kale, M. B., Wankhede, N. L., Pawar, R. S., Ballal, S., Kumawat, R., Goswami, M., Khalid, M., Taksande, B. G., Upaganlawar, A. B., Umekar, M. J., et al. (2024) · Ageing Research Reviews · doi:10.1016/j.arr.2024.102497 · Cited by 208
Selective Laser Sintering (SLS), a New Chapter in the Production of Solid Oral Forms (SOFs) by 3D Printing
Gueche, Y. A., Sanchez‐Ballester, N. M., Cailleaux, S., Bataille, B., & Soulairol, I. (2021) · Pharmaceutics · doi:10.3390/pharmaceutics13081212 · Cited by 184
Textile Triboelectric Nanogenerators for Wearable Pulse Wave Monitoring
Chen, G., Au, C., & Chen, J. (2021) · Trends in biotechnology · doi:10.1016/j.tibtech.2020.12.011 · Cited by 173
TGF-β signaling: critical nexus of fibrogenesis and cancer
Giarratana, A. O., Prendergast, C. M., Salvatore, M. M., & Capaccione, K. M. (2024) · Journal of Translational Medicine · doi:10.1186/s12967-024-05411-4 · Cited by 180
Digital health: trends, opportunities and challenges in medical devices, pharma and bio-technology
Kasoju, N., Remya, N. S., Sasi, R., Sreedharan, S., Soman, B., Kesavadas, C., Muraleedharan, C. V., Varma, P. R. H., & Behari, S. (2023) · CSI Transactions on ICT · doi:10.1007/s40012-023-00380-3 · Cited by 197
Prostate Cancer Biomarkers: From diagnosis to prognosis and precision-guided therapeutics
Adamaki, M., & Zoumpourlis, V. (2021) · Pharmacology & Therapeutics · doi:10.1016/j.pharmthera.2021.107932 · Cited by 146
3D Bioprinting for Next-Generation Personalized Medicine
Lam, E. H. Y., Yu, F., Zhu, S., & Wang, Z. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24076357 · Cited by 151
The Future of Precision Medicine in the Cure of Alzheimer’s Disease
Arafah, A., Khatoon, S., Rasool, I., Khan, A., Rather, M. A., Abujabal, K. A., Faqih, Y. A. H., Rashid, H., Rashid, S. M., Bilal Ahmad, S., et al. (2023) · Biomedicines · doi:10.3390/biomedicines11020335 · Cited by 133
Using artificial intelligence to improve pain assessment and pain management: a scoping review
Zhang, M., Zhu, L., Lin, S., Herr, K., Chi, C., Demir, İ., Lopez, K. D., & Chi, N. (2022) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocac231 · Cited by 144
Shape memory materials and 4D printing in pharmaceutics
Melocchi, A., Uboldi, M., Cerea, M., Foppoli, A., Maroni, A., Moutaharrik, S., Palugan, L., Zema, L., & Gazzaniga, A. (2021) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2021.03.013 · Cited by 116
Synergizing Nanomaterials and Artificial Intelligence in Advanced Optical Biosensors for Precision Antimicrobial Resistance Diagnosis
Taha, B. A., Ahmed, N. M., Talreja, R. K., Haider, A. J., Mashhadany, Y. I. M. A., Al-Jubouri, Q., Huddin, A. B., Mokhtar, M. H. H., Rustagi, S., Kaushik, A. K., et al. (2024) · ACS Synthetic Biology · doi:10.1021/acssynbio.4c00070 · Cited by 110
Phytochemicals in Cancer Therapy: A Structured Review of Mechanisms, Challenges, and Progress in Personalized Treatment
Aljabali, A. A. A., Obeid, M. A., Bashatwah, R. M., Qnais, E. Y., Gammoh, O., Alqudah, A. M., Mishra, V., Mishra, Y., Khan, M. A., Parvez, S., et al. (2025) · Chemistry & Biodiversity · doi:10.1002/cbdv.202402479 · Cited by 104
Assertiveness-based Agent Communication for a Personalized Medicine on Medical Imaging Diagnosis
Calisto, F. M., Fernandes, J., Morais, M., Santiago, C., Abrantes, J., Nunes, N., & Nascimento, J. C. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3580682 · Cited by 116
Digital Technologies and Data Science as Health Enablers: An Outline of Appealing Promises and Compelling Ethical, Legal, and Social Challenges
Cordeiro, J. V. (2021) · Frontiers in Medicine · doi:10.3389/fmed.2021.647897 · Cited by 121
Small extracellular vesicles from menstrual blood-derived mesenchymal stem cells (MenSCs) as a novel therapeutic impetus in regenerative medicine
Chen, L., Qu, J., Mei, Q., Chen, X., Fang, Y., Chen, L., Li, Y., & Xiang, C. (2021) · Stem Cell Research & Therapy · doi:10.1186/s13287-021-02511-6 · Cited by 93
Advancements in Wearable and Implantable BioMEMS Devices: Transforming Healthcare Through Technology
Abhinav, V., Basu, P., Verma, S. S., Verma, J., Das, A., Kumari, S., Yadav, P. R., & Kumar, V. (2025) · Micromachines · doi:10.3390/mi16050522 · Cited by 106
Artificial Intelligence, Healthcare, Clinical Genomics, and Pharmacogenomics Approaches in Precision Medicine
Abdelhalim, H., Berber, A., Lodi, M. K., Jain, R., Nair, A. S., Pappu, A., Patel, K., Venkat, V., Venkatesan, C., Wable, R., et al. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.929736 · Cited by 99
Modelling Cell Metabolism: A Review on Constraint-Based Steady-State and Kinetic Approaches
Yasemi, M., & Jolicœur, M. (2021) · Processes · doi:10.3390/pr9020322 · Cited by 88
Adhesion, metastasis, and inhibition of cancer cells: a comprehensive review
Yayan, J., Franke, K.-J., Berger, M. P., Windisch, W., & Rasche, K. (2024) · Molecular Biology Reports · doi:10.1007/s11033-023-08920-5 · Cited by 85
Mechanistic insights of CRISPR/Cas nucleases for programmable targeting and early-stage diagnosis: A review
Habimana, J. D. D., Huang, R., Muhoza, B., Kalisa, N. Y., Han, X. B., Deng, W., & Li, Z. (2022) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2022.114033 · Cited by 84
Artificial Intelligence in Medicine and Dentistry
Vodanović, M., Subašić, M., Milošević, D., & Pavičin, I. S. (2023) · Acta Stomatologica Croatica · doi:10.15644/asc57/1/8 · Cited by 88
Recent Advances in Skin-Interfaced Wearable Sweat Sensors: Opportunities for Equitable Personalized Medicine and Global Health Diagnostics
Clark, K. M., & Ray, T. R. (2023) · ACS Sensors · doi:10.1021/acssensors.3c01512 · Cited by 87
Global, regional, and national epidemiology of rheumatoid arthritis among people aged 20–54 years from 1990 to 2021
Zhang, Z., Gao, X., Liu, S., Wang, Q., Wang, Y., Hou, S., Wang, J., & Zhang, Y. (2025) · Scientific Reports · doi:10.1038/s41598-025-92150-1 · Cited by 83
Druggable genome and precision medicine in cancer: current challenges
Dupont, C., Riegel, K., Pompaiah, M., Juhl, H. H., & Rajalingam, K. (2021) · FEBS Journal · doi:10.1111/febs.15788 · Cited by 83
Big data: Historic advances and emerging trends in biomedical research
Cremin, C. J., Dash, S., & Huang, X. (2022) · Current Research in Biotechnology · doi:10.1016/j.crbiot.2022.02.004 · Cited by 91
Rheological, structural, and biological trade-offs in bioink design for 3D bioprinting
Elango, J., & Zamora-Ledezma, C. (2025) · Gels · doi:10.3390/gels11080659 · Cited by 75
A new horizon of precision medicine: combination of the microbiome and extracellular vesicles
Yang, J., Shin, T.-S., Kim, J. S., Jee, Y., & Kim, Y. (2022) · Experimental & Molecular Medicine · doi:10.1038/s12276-022-00748-6 · Cited by 74
Microfluidic Nanoparticle Separation for Precision Medicine
Lan, Z., Chen, R., Zou, D., & Zhao, C. (2024) · Advanced Science · doi:10.1002/advs.202411278 · Cited by 68
Project IDentif.AI: Harnessing Artificial Intelligence to Rapidly Optimize Combination Therapy Development for Infectious Disease Intervention
Abdulla, A., Wang, B., Feng, Q., Kee, T. W., Blasiak, A., Ong, Y. H., Hooi, L., Parekh, F. K., Soriano, R., Olinger, G. G., et al. (2020) · Advanced Therapeutics · doi:10.1002/adtp.202000034 · Cited by 65
Self-citation
Next-Generation Cognitive-Behavioral Therapy for Depression: Integrating Digital Tools, Teletherapy, and Personalization for Enhanced Mental Health Outcomes
Gkintoni, E., Vassilopoulos, S. P., & Νικολάου, Γ. (2025) · Medicina · doi:10.3390/medicina61030431 · Cited by 77
From Machine Learning to Patient Outcomes: A Comprehensive Review of AI in Pancreatic Cancer
Tripathi, S., Tabari, A., Mansur, A., Dabbara, H., Bridge, C. P., & Daye, D. (2024) · Diagnostics · doi:10.3390/diagnostics14020174 · Cited by 70
Next-Generation strategies to combat antimicrobial resistance: Integrating genomics, CRISPR, and novel therapeutics for effective treatment
Olatunji, A. O., Olaboye, J. A., Maha, C. C., Kolawole, T. O., & Abdul, S. (2024) · Engineering Science & Technology Journal · doi:10.51594/estj.v5i7.1344 · Cited by 61
Fabrication of aerosol-based nanoparticles and their applications in biomedical fields
Gautam, M., Kim, J. O., & Yong, C. S. (2021) · Journal of Pharmaceutical Investigation · doi:10.1007/s40005-021-00523-1 · Cited by 64
Surface Topography-Adaptive Robotic Superstructures for Biofilm Removal and Pathogen Detection on Human Teeth
Oh, M. J., Babeer, A., Liu, Y., Ren, Z. F., Wu, J., Issadore, D., Stebe, K. J., Lee, D., Steager, E. B., & Koo, H. (2022) · ACS Nano · doi:10.1021/acsnano.2c01950 · Cited by 62
Artificial intelligence in smart drug delivery systems: a step toward personalized medicine
Panchpuri, M., Painuli, R., & Kumar, C. (2025) · RSC Pharmaceutics · doi:10.1039/d5pm00089k · Cited by 66
Oxidative Stress: Molecular Mechanisms, Diseases, and Therapeutic Targets
Qin, Y., Qian, C., Li, W., Wang, Q., Sheng, Q., Chen, Z., Zhang, W., Li, W., Ge, G., Yan, Z., et al. (2026) · MedComm · doi:10.1002/mco2.70600 · Cited by 78
Platelet Proteomes, Pathways, and Phenotypes as Informants of Vascular Wellness and Disease
Aslan, J. E. (2021) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/atvbaha.120.314647 · Cited by 56
Addressing COVID‐19 Drug Development with Artificial Intelligence
Ho, D. (2020) · Advanced Intelligent Systems · doi:10.1002/aisy.202000070 · Cited by 57
Self-citation
Advances in Drug Delivery Integrated with Regenerative Medicine: Innovations, Challenges, and Future Frontiers
Rasekh, M., Arshad, M. S., & Ahmad, Z. (2025) · Pharmaceutics · doi:10.3390/pharmaceutics17040456 · Cited by 65
Next-generation nanophotonic-enabled biosensors for intelligent diagnosis of SARS-CoV-2 variants
Taha, B. A., Mashhadany, Y. I. M. A., Al-Jubouri, Q., Rashid, A. R. A., Luo, Y., Chen, Z., Rustagi, S., Chaudhary, V., & Arsad, N. (2023) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2023.163333 · Cited by 59
Advances in Escherichia coli-Based Therapeutic Protein Expression: Mammalian Conversion, Continuous Manufacturing, and Cell-Free Production
Niazi, S. K., & Magoola, M. (2023) · Biologics · doi:10.3390/biologics3040021 · Cited by 63
Recent advances in nanogels for drug delivery and biomedical applications
Vashist, A., Alvarez, G. P., Camargo, V. A., Raymond, A., Yndart, A., Kolishetti, N., Vashist, A., Manickam, P., Aggarwal, S., & Nair, M. (2024) · Biomaterials Science · doi:10.1039/d4bm00224e · Cited by 55
Targeting increased levels of APP in Down syndrome: Posiphen‐mediated reductions in APP and its products reverse endosomal phenotypes in the Ts65Dn mouse model
Chen, X., Salehi, A., Pearn, M. L., Overk, C., Nguyen, P. D., Kleschevnikov, A. M., Maccecchini, M. L., & Mobley, W. C. (2020) · Alzheimer s & Dementia · doi:10.1002/alz.12185 · Cited by 55
Self-citation
Beyond ‘AI for Social Good’ (AI4SG): social transformations—not tech-fixes—for health equity
Holzmeyer, C. (2021) · Interdisciplinary Science Reviews · doi:10.1080/03080188.2020.1840221 · Cited by 61
Implementation of targeted next-generation sequencing for the diagnosis of drug-resistant tuberculosis in low-resource settings: a programmatic model, challenges, and initial outcomes
de Araujo, L., Cabibbe, A. M., Mhuulu, L., Ruswa, N., Dreyer, V., Diergaardt, A., Günther, G., Claassens, M. M., Gerlach, C., Utpatel, C., et al. (2023) · Frontiers in Public Health · doi:10.3389/fpubh.2023.1204064 · Cited by 55
Artificial Intelligence-Driven Strategies for Targeted Delivery and Enhanced Stability of RNA-Based Lipid Nanoparticle Cancer Vaccines
Bhujel, R., Enkmann, V., Burgstaller, H., & Maharjan, R. (2025) · Pharmaceutics · doi:10.3390/pharmaceutics17080992 · Cited by 51
Prosthetic Materials Used for Implant-Supported Restorations and Their Biochemical Oral Interactions: A Narrative Review
Ionescu, R. N., Totan, A., Imre, M. M., Țâncu, A. M. C., Pantea, M., Butucescu, M., & Farcașiu, A.-T. (2022) · Materials · doi:10.3390/ma15031016 · Cited by 59
Single-cell pathogen diagnostics for combating antibiotic resistance
Li, H., Hsieh, K., Wong, P. K., Mach, K. E., Liao, J. C., & Wang, T. (2023) · Nature Reviews Methods Primers · doi:10.1038/s43586-022-00190-y · Cited by 54
Behavioral health and generative AI: a perspective on future of therapies and patient care
Sezgın, E., & McKay, I. T. (2024) · npj Mental Health Research · doi:10.1038/s44184-024-00067-w · Cited by 55
Beyond Skin Deep: Phospholipid-Based Nanovesicles as Game-Changers in Transdermal Drug Delivery
Akl, M. A., Eldeen, M. A., & Kassem, A. M. (2024) · AAPS PharmSciTech · doi:10.1208/s12249-024-02896-6 · Cited by 50
Revolutionizing Sleep Health: The Emergence and Impact of Personalized Sleep Medicine
Garbarino, S., & Bragazzi, N. L. (2024) · Journal of Personalized Medicine · doi:10.3390/jpm14060598 · Cited by 49
RNA nanotherapeutics for hepatocellular carcinoma treatment
Yuan, Y., Sun, W., Xie, J., Zhang, Z., Luo, J., Han, X., Xiong, Y., Yang, Y., & Zhang, Y. (2024) · Theranostics · doi:10.7150/thno.102964 · Cited by 55
Leveraging transcriptomics for precision diagnosis: Lessons learned from cancer and sepsis
Tsakiroglou, M., Evans, A. G., & Pirmohamed, M. (2023) · Frontiers in Genetics · doi:10.3389/fgene.2023.1100352 · Cited by 49
Plasmonic and metamaterial biosensors: a game-changer for virus detection
Wang, J., Xu, Z., & Kotsifaki, D. G. (2023) · Sensors & Diagnostics · doi:10.1039/d2sd00217e · Cited by 47
Nanomedicine of tyrosine kinase inhibitors
Šmídová, V., Michálek, P., Goliášová, Z., Eckschlager, T., Hodek, P., Adam, V., & Heger, Z. (2020) · Theranostics · doi:10.7150/thno.48662 · Cited by 49
Trends and gaps in precision health research: a scoping review
Viaña, J. N. M., Edney, S. M., Gondalia, S. V., Mauch, C. E., Sellak, H., O’Callaghan, N. J., & Ryan, J. C. (2021) · BMJ Open · doi:10.1136/bmjopen-2021-056938 · Cited by 45
Tailoring drug delivery systems by microfluidics for tumor therapy
Zhang, Q., Kuang, G., Wang, L., Fan, L., & Zhao, Y. (2024) · Materials Today · doi:10.1016/j.mattod.2024.01.004 · Cited by 45
Review of Personalized Medicine and Pharmacogenomics of Anti-Cancer Compounds and Natural Products
Zhou, Y., Peng, S., Wang, H., Cai, X., & Wang, Q. (2024) · Genes · doi:10.3390/genes15040468 · Cited by 49
The Evolution of Systems Biology and Systems Medicine: From Mechanistic Models to Uncertainty Quantification
Qiao, L., Khalilimeybodi, A., Linden-Santangeli, N. J., & Rangamani, P. (2025) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-102723-065309 · Cited by 57
Precision clinical trials: a framework for getting to precision medicine for neurobehavioural disorders
Lenze, E. J., Nicol, G. E., Barbour, D. L., Kannampallil, T., Wong, A. W. K., Piccirillo, J., Drysdale, A. T., Sylvester, C. M., Haddad, R., Miller, J. P., et al. (2021) · Journal of Psychiatry and Neuroscience · doi:10.1503/jpn.200042 · Cited by 43
Personalized explanations for clinician-AI interaction in breast imaging diagnosis by adapting communication to expertise levels
Calisto, F. M., Abrantes, J., Santiago, C., Nunes, N., & Nascimento, J. C. (2025) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2025.103444 · Cited by 46
Transforming Pharmacogenomics and CRISPR Gene Editing with the Power of Artificial Intelligence for Precision Medicine
Srivastav, A. K., Mishra, M. K., Lillard, J. W., & Singh, R. (2025) · Pharmaceutics · doi:10.3390/pharmaceutics17050555 · Cited by 48
Biomaterials for intestinal organoid technology and personalized disease modeling
Hirota, A., AlMusawi, S., Nateri, A. S., Ordóñez‐Morán, P., & Imajo, M. (2021) · Acta Biomaterialia · doi:10.1016/j.actbio.2021.05.010 · Cited by 41
Monitoring Cardiac Biomarkers with Aptamer‐Based Molecular Pendulum Sensors
Mahmud, A., Chang, D., Das, J., Gomis, S., Foroutan, F., Chen, J. B., Pandey, L., Flynn, C. D., Yousefi, H., Geraili, A., et al. (2023) · Angewandte Chemie International Edition · doi:10.1002/anie.202213567 · Cited by 41
Recent Developments in Inertial and Centrifugal Microfluidic Systems along with the Involved Forces for Cancer Cell Separation: A Review
Farahinia, A., Zhang, W., & Badea, I. (2023) · Sensors · doi:10.3390/s23115300 · Cited by 43
The role of artificial intelligence in radiotherapy clinical practice
Landry, G., Kurz, C., & Traverso, A. (2023) · BJR|Open · doi:10.1259/bjro.20230030 · Cited by 41
Overcoming Pilotitis in Digital Medicine at the Intersection of Data, Clinical Evidence, and Adoption
Egermark, M., Blasiak, A., Remus, A., Sapanel, Y., & Ho, D. (2022) · Advanced Intelligent Systems · doi:10.1002/aisy.202200056 · Cited by 38
Self-citation
Nanomaterials as drug delivery agents for overcoming the blood-brain barrier: A comprehensive review
Kulkarni, M., Patel, K., Patel, A. K., Patel, S., Desai, J. L., Patel, M. M., Shah, U., Patel, A. D., & Solanki, N. D. (2023) · ADMET & DMPK · doi:10.5599/admet.2043 · Cited by 46
Nucleic acid-based therapeutics for dermal wound healing
Sharma, P. M., Kumar, A., Agarwal, T., Dey, A. D., Moghaddam, F. D., Rahimmanesh, I., Ghovvati, M., Yousefiasl, S., Borzacchiello, A., Mohammadi, A., et al. (2022) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2022.08.099 · Cited by 38
Artificial intelligence in veterinary and animal science: applications, challenges, and future prospects
Hossein‐Zadeh, N. G. (2025) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2025.110395 · Cited by 43
Enhanced Biohomogeneous Composite Membrane-Encapsulated Nanoplatform with Podocyte Targeting for Precise and Safe Treatment of Diabetic Nephropathy
Fan, K., Yuan, S., Zhou, M., Yu, Y., Guo, J., Fang, L., Zhou, C., Cui, P., Zhang, S., Li, R., et al. (2023) · ACS Nano · doi:10.1021/acsnano.3c04671 · Cited by 34
Multi-level exploration of auricular acupuncture: from traditional Chinese medicine theory to modern medical application
Guo, K., Lü, Y., Wang, X., Duan, Y., Li, H., Gao, F., & Wang, J. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2024.1426618 · Cited by 44
Theranostics: Integrated Diagnostics and Therapy Using Nanomedicine
Singh, V. (2024) · Nanotechnology in the life sciences · doi:10.1007/978-3-031-72467-1_17 · Cited by 35
Longitudinal big biological data in the AI era
Mardinoğlu, A., Türkez, H., Shong, M., Srinivasulu, V. P., Nielsen, J., Palsson, B. Ø., Hood, L., & Uhlén, M. (2025) · Molecular Systems Biology · doi:10.1038/s44320-025-00134-0 · Cited by 36
3D‐Knit Dry Electrodes using Conductive Elastomeric Fibers for Long‐Term Continuous Electrophysiological Monitoring
Eskandarian, L., Toossi, A., Nassif, F., Rostami, S. G., Ni, S., Mahnam, A., Alizadeh-Meghrazi, M., Takarada, W., Kikutani, T., & Naguib, H. E. (2022) · Advanced Materials Technologies · doi:10.1002/admt.202101572 · Cited by 34
Modular Digital and 3D-Printed Dental Models with Applicability in Dental Education
Petre, A. E., Pantea, M., Drafta, S., Imre, M. M., Țâncu, A. M. C., Liciu, E., Didilescu, A. C., & Pițuru, S. (2023) · Medicina · doi:10.3390/medicina59010116 · Cited by 34
Heterogeneity and molecular landscape of melanoma: implications for targeted therapy
Beigi, Y. Z., Lanjanian, H., Fayazi, R., Salimi, M., Hoseyni, B. H. M., Noroozizadeh, M. H., & Masoudi‐Nejad, A. (2024) · Molecular Biomedicine · doi:10.1186/s43556-024-00182-2 · Cited by 33
The Advent of Molecular Targeted Therapies Against Cancer. Toward Multi‐Targeting Drugs Through Materials Engineering: A Possible Future Scenario
Puzzo, M., Santo, M. D., Morelli, C., Leggio, A., & Pasqua, L. (2024) · Small Science · doi:10.1002/smsc.202400113 · Cited by 37
Advances in Materials Science for Precision Melanoma Therapy: Nanotechnology-Enhanced Drug Delivery Systems
Moni, S. S., Moshi, J. M., Matou‐Nasri, S., Alotaibi, S., Hawsawi, Y. M., Elmobark, M. E., Hakami, A., Jeraiby, M., Sulayli, A. A., & Moafa, H. N. (2025) · Pharmaceutics · doi:10.3390/pharmaceutics17030296 · Cited by 36
Paper-based flexible electronic devices: processing, integration, and applications
Ying, W., Zhou, C., Sun, H., & Huang, W. (2025) · npj Flexible Electronics · doi:10.1038/s41528-025-00446-z · Cited by 33
A systematic review of the application of machine-learning algorithms in multiple sclerosis
Vázquez-Marrufo, M., Sarrias-Arrabal, E., García-Torres, M., Martı́n-Clemente, R., & Izquierdo, G. (2022) · Neurología (English Edition) · doi:10.1016/j.nrleng.2020.10.013 · Cited by 32
Human Papillomavirus Infections and the Role Played by Cervical and Cervico-Vaginal Microbiota—Evidence from Next-Generation Sequencing Studies
Głowienka, M., Bagińska, K., Szubert, S., Hennig, E. E., Horała, A., Dąbrowska, M., Micek, M., Ciebiera, M., & Żeber‐Lubecka, N. (2024) · Cancers · doi:10.3390/cancers16020399 · Cited by 34
How Prescriptive Analytics Influences Decision Making in Precision Medicine
Mosavi, N. S., & Santos, M. F. (2020) · Procedia Computer Science · doi:10.1016/j.procs.2020.10.073 · Cited by 31
Defining precision health: a scoping review protocol
Ryan, J. C., Viaña, J. N. M., Sellak, H., Gondalia, S. V., & O’Callaghan, N. J. (2021) · BMJ Open · doi:10.1136/bmjopen-2020-044663 · Cited by 33
Detection of EGFR Mutations Using Bronchial Washing-Derived Extracellular Vesicles in Patients with Non-Small-Cell Lung Carcinoma
Park, J., Lee, C., Eom, J. S., Kim, M., & Cho, Y. (2020) · Cancers · doi:10.3390/cancers12102822 · Cited by 30
Artificial intelligence perspective in the future of endocrine diseases
Hasanzad, M., Meybodi, H. R. A., Sarhangi, N., & Larijani, B. A. (2022) · Journal of Diabetes & Metabolic Disorders · doi:10.1007/s40200-021-00949-2 · Cited by 30
Dual role of the nasal microbiota in neurological diseases—An unignorable risk factor or a potential therapy carrier
Xie, J., Tian, S., Liu, J., Cao, R., Yue, P., Cai, X., Shang, Q., Yang, M. H., Han, L., & Zhang, D. (2022) · Pharmacological Research · doi:10.1016/j.phrs.2022.106189 · Cited by 30
Artificial intelligence in forensic medicine and forensic dentistry.
Vodanović, M., Subašić, M., Milošević, D., Galić, I., & Brkić, H. (2023) · The Journal of Forensic Odonto-Stomatology · Cited by 30
Role of Exosomes in Parkinson’s and Alzheimer’s Diseases
Sharma, H., Tyagi, S. J., Chandra, P., Verma, A. K., Kumar, P., Ashique, S., & Hussain, A. (2024) · Springer eBooks · doi:10.1007/978-981-99-8373-5_6 · Cited by 34
Bio–Nano Toolbox for Precision Alzheimer's Disease Gene Therapy
Liu, Y., Xia, X., Zheng, M., & Shi, B. (2024) · Advanced Materials · doi:10.1002/adma.202314354 · Cited by 31
Nanobiotechnology boosts ferroptosis: opportunities and challenges
Han, S., Zou, J., Xiao, F., Xian, J., Liu, Z., Li, M., Luo, W., Feng, C., & Kong, N. (2024) · Journal of Nanobiotechnology · doi:10.1186/s12951-024-02842-5 · Cited by 34
Transforming Diagnostics: A Comprehensive Review of Advances in Digital Pathology
Fatima, G., Al-Hmadi, H. B., Mahdi, A. A., Hadi, N. R., Fedačko, J., Magomedova, A. G., Parvez, S., & Raza, A. M. (2024) · Cureus · doi:10.7759/cureus.71890 · Cited by 29

Authors: Ho, D., Quake, S. R., McCabe, E. R. B., Chng, W. J., Chow, E. K., Ding, X., Gelb, B. D., Ginsburg, G. S., Hassenstab, J., Ho, C.-M., Mobley, W. C., Nolan, G. P., Rosen, S. T., Tan, P., Yen, Y., & Zarrinpar, A.
Affiliations: National University of Singapore (SG)
Chan Zuckerberg Biohub San Francisco (US)
March of Dimes (US)
Shanghai Jiao Tong University (CN)
Icahn School of Medicine at Mount Sinai (US)
Duke University (US)
Washington University in St. Louis (US)
University of California, Los Angeles (US)
University of California San Diego (US)
Stanford University (US)
City Of Hope National Medical Center (US)
Taipei Medical University (TW)
University of Florida Health (US)
Corresponding author: Ho, Dean
McCabe, Edward R.B.
ORCID: Ho, Dean
Quake, Stephen R.
McCabe, Edward R.B.
Chng, Wee Joo
Chow, Edward K.
Ding, Xianting
Gelb, Bruce D.
Ginsburg, Geoffrey S.
Hassenstab, Jason
Ho, Chih-Ming
Mobley, William C.
Nolan, Garry P.
Rosen, Steven T.
Tan, Patrick
Yen, Yun
Zarrinpar, Ali
Venue: Trends in Biotechnology
Publisher: Elsevier BV
Published: 2020-05
Volume: 38
Issue: 5
Pages: 497-518
ISSN: 0167-7799 (ISSN-L)
DOI: 10.1016/j.tibtech.2019.12.021
PMC: PMC7924935
OpenAlex: W3001878481
Cited by: 517
References: 212
Citations by year: 2026: 97 · 2025: 143 · 2024: 106 · 2023: 67 · 2022: 48 · 2021: 39 · 2020: 17
FWCI: 7.24
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
Locations: Trends in biotechnology (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
eScholarship (California Digital Library) (submittedVersion)
Funders: V Foundation for Cancer Research
National Research Foundation Singapore
Wallace H. Coulter Foundation
National Institutes of Health
Ministry of Education - Singapore
Topics: Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Technology and Human Factors in Education and Health
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Advanced Biosensing Techniques and Applications
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
Keywords: personalized medicine, precision medicine, biomarker discovery, disease signatures, personalized treatment, drug selection, dose optimization, enabling technologies, individualized care
MeSH terms: Artificial Intelligence, Diagnosis, Humans, Therapeutics, Biomarkers, 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

Enabling Technologies for Personalized and Precision Medicine

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks