The Path to Personalized Medicine
Open accessAbstract
M ajor investments in basic science have created an opportunity for significant progress in clinical medicine.Researchers have discovered hundreds of genes that harbor variations contributing
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
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?
Development and application of fair approaches will be critical for implementation of unbiased and interpretable models for Precision Medicine.
“The development and application of fair approaches will be critical for the implementation of unbiased and interpretable models for Precision Medicine106,116.”
From Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare (2020) · cites this paper as [116]
The increasing interest in personalized medicine has come on the back of GWAS studies, under the rationale that patient risk can be prognosticated with increasing precision as more is learned about the roles of genes in disease.
“However, the increasing interest in personalized medicine has really come on the back of GWAS studies, under the rationale that patient risk can be prognosticated with increasing precision as more is learned about the roles of genes in disease [228].”
From Personality and Longevity: Knowns, Unknowns, and Implications for Public Health and Personalized Medicine (2011) · cites this paper as [228]
Interest in individual differences in health risk and treatment response has gained great traction over the last decade under the rubric of personalized medicine.
“Interest in individual differences in health risk and treatment response has gained great traction over the last decade under the rubric of personalized medicine [228].”
From Personality and Longevity: Knowns, Unknowns, and Implications for Public Health and Personalized Medicine (2011) · cites this paper as [228]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cited by
Sorted by citations
(2012) · doi:10.1016/j.jmoldx.2011.08.002 · Cited by 2,567 · see citing works
Opportunities and obstacles for deep learning in biology and medicine
Ching, T., Himmelstein, D. S., Beaulieu-Jones, B. K., Kalinin, A. A., Do, B. T., Way, G. P., Ferrero, E., Agapow, P.-M., Zietz, M., Hoffman, M. M., et al. (2018) · Journal of The Royal Society Interface · doi:10.1098/rsif.2017.0387 · Cited by 2,310 · see citing works
Causability and explainability of artificial intelligence in medicine
Holzinger, A., Langs, G., Denk, H., Zatloukal, K., & Müller, H. (2019) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1312 · Cited by 1,885 · see citing works
Wearable and flexible electronics for continuous molecular monitoring
Yang, Y., & Gao, W. (2019) · Chemical Society Reviews · doi:10.1039/c7cs00730b · Cited by 1,315 · see citing works
Overview of the BioBank Japan Project: Study design and profile
Nagai, A., Hirata, M., Kamatani, Y., Muto, K., Matsuda, K., Kiyohara, Y., Ninomiya, T., Tamakoshi, A., Yamagata, Z., Mushiroda, T., et al. (2017) · Journal of Epidemiology · doi:10.1016/j.je.2016.12.005 · Cited by 739 · see citing works
Delivering affordable cancer care in high-income countries
Sullivan, R., Peppercorn, J., Sikora, K., Zalcberg, J., Meropol, N. J., Amir, E., Khayat, D., Boyle, P., Autier, P., Tannock, I. F., et al. (2011) · The Lancet Oncology · doi:10.1016/s1470-2045(11)70141-3 · Cited by 718 · see citing works
The n-of-1 clinical trial: the ultimate strategy for individualizing medicine?
Lillie, E. O., Patay, B., Diamant, J., Issell, B., Topol, E. J., & Schork, N. J. (2011) · Personalized Medicine · doi:10.2217/pme.11.7 · Cited by 706 · see citing works
What do we need to build explainable AI systems for the medical domain?
Holzinger, A., Biemann, C., Pattichis, C. S., & Kell, D. B. (2017) · arXiv · doi:10.48550/arxiv.1712.09923 · Cited by 636 · see citing works
Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare
Cirillo, D., Catuara-Solarz, S., Morey, C., Guney, E., Subirats, L., Mellino, S., Gigante, A., Valencia, A., Rementeria, M. J., Chadha, A. S., et al. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0288-5 · Cited by 553 · see citing works
DeRubeis, R. J., Cohen, Z. D., Forand, N. R., Fournier, J. C., Gelfand, L. A., & Lorenzo-Luaces, L. (2014) · PLoS ONE · doi:10.1371/journal.pone.0083875 · Cited by 509 · see citing works
Enabling Technologies for Personalized and Precision Medicine
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., et al. (2020) · Trends in Biotechnology · doi:10.1016/j.tibtech.2019.12.021 · Cited by 517 · see citing works
Friedman, C., Rubin, J., Brown, J., Buntin, M., Corn, M., Etheredge, L., Gunter, C., Musen, M., Platt, R., Stead, W., et al. (2015) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2014-002977 · Cited by 509 · see citing works
Treatment selection in depression.
Cohen, Z. D., & DeRubeis, R. J. (2018) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-050817-084746 · Cited by 435 · see citing works
Big data and artificial intelligence modeling for drug discovery
Zhu, H. (2020) · Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-010919-023324 · Cited by 455 · see citing works
Treatment resistance in psychiatry: state of the art and new directions
Howes, O. D., Thase, M. E., & Pillinger, T. (2022) · Molecular Psychiatry · doi:10.1038/s41380-021-01200-3 · Cited by 413 · see citing works
The fast-growing field of photo-driven theranostics based on aggregation-induced emission
Zhang, Z., Kang, M., Tan, H., Song, N., Li, M., Xiao, P., Yan, D., Zhang, L., Wang, D., & Tang, B. Z. (2022) · Chemical Society Reviews · doi:10.1039/d1cs01138c · Cited by 374 · see citing works
Lucas, H. C., Agarwal, R., Clemons, E. K., El Sawy, O. A., & Weber, B. (2013) · MIS Quarterly · doi:10.25300/misq/2013/37.2.03 · Cited by 358 · see citing works
Ligand-installed nanocarriers toward precision therapy
Mi, P., Cabral, H., & Kataoka, K. (2020) · Advanced Materials · doi:10.1002/adma.201902604 · Cited by 350 · see citing works
Breaking Down the Barriers to Precision Cancer Nanomedicine
von Roemeling, C., Jiang, W., Chan, C. K., Weissman, I. L., & Kim, B. Y. S. (2017) · Trends in Biotechnology · doi:10.1016/j.tibtech.2016.07.006 · Cited by 330 · see citing works
Annual Research Review: Building a science of personalized intervention for youth mental health
Ng, M. Y., & Weisz, J. R. (2016) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.12470 · Cited by 291 · see citing works
3D printed anatomical nerve regeneration pathways
Johnson, B. N., Lancaster, K. Z., Zhen, G., He, J., Gupta, M. K., Kong, Y. L., Engel, E. A., Krick, K. D., Ju, A., Meng, F., et al. (2015) · Advanced Functional Materials · doi:10.1002/adfm.201501760 · Cited by 293 · see citing works
Fillman, S. G., Weickert, T. W., Lenroot, R. K., Catts, S. V., Bruggemann, J. M., Catts, V. S., & Weickert, C. S. (2016) · Molecular Psychiatry · doi:10.1038/mp.2015.90 · Cited by 270 · see citing works
Chapman, B. P., Roberts, B., & Duberstein, P. (2011) · Journal of Aging Research · doi:10.4061/2011/759170 · Cited by 283 · see citing works
Toward a personalized science of emotion regulation
Doré, B. P., Silvers, J. A., & Ochsner, K. N. (2016) · Social and Personality Psychology Compass · doi:10.1111/spc3.12240 · Cited by 189 · see citing works
Chapman, B. P., Hampson, S., & Clarkin, J. (2014) · Developmental Psychology · doi:10.1037/a0034135 · Cited by 166 · see citing works
Open trial of a personalized modular treatment for mood and anxiety
Fisher, A. J., Bosley, H. G., Fernandez, K. C., Reeves, J. W., Soyster, P. D., Diamond, A. E., & Barkin, J. (2019) · Behaviour Research and Therapy · doi:10.1016/j.brat.2019.01.010 · Cited by 156 · see citing works
Wallach, J. D., Sullivan, P. G., Trepanowski, J. F., Sainani, K. L., Steyerberg, E. W., & Ioannidis, J. P. A. (2017) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2016.9125 · Cited by 148 · see citing works
Miller, F. A., Hayeems, R. Z., Bytautas, J. P., Bedard, P. L., Ernst, S., Hirte, H., Hotte, S., Oza, A., Razak, A., Welch, S., et al. (2014) · European Journal of Human Genetics · doi:10.1038/ejhg.2013.158 · Cited by 116 · see citing works
Donker, T., Batterham, P. J., Warmerdam, L., Bennett, K., Bennett, A., Cuijpers, P., Griffiths, K. M., & Christensen, H. (2013) · Journal of Affective Disorders · doi:10.1016/j.jad.2013.06.020 · Cited by 115 · see citing works
Wallach, J. D., Sullivan, P. G., Trepanowski, J. F., Steyerberg, E. W., & Ioannidis, J. P. A. (2016) · BMJ · doi:10.1136/bmj.i5826 · Cited by 89 · see citing works
Lewis, C., Roberts, N. P., Simon, N., Bethell, A., & Bisson, J. I. (2019) · Acta Psychiatrica Scandinavica · doi:10.1111/acps.13079 · Cited by 91 · see citing works
Lewis, C. E., Farewell, D., Groves, V., Kitchiner, N. J., Roberts, N. P., Vick, T., & Bisson, J. I. (2017) · Depression and Anxiety · doi:10.1002/da.22645 · Cited by 84 · see citing works
Pearson, R., Pisner, D., Meyer, B., Shumake, J., & Beevers, C. G. (2019) · Psychological Medicine · doi:10.1017/s003329171800315x · Cited by 81 · see citing works
A common factors perspective on mindfulness-based interventions
Goldberg, S. B. (2022) · Nature Reviews Psychology · doi:10.1038/s44159-022-00090-8 · Cited by 75 · see citing works
Development and initial implementation of the Dynamic Assessment Treatment Algorithm (DATA)
Fernandez, K. C., Fisher, A. J., & Chi, C. (2017) · PLOS ONE · doi:10.1371/journal.pone.0178806 · Cited by 68 · see citing works
Blood-informative transcripts define nine common axes of peripheral blood gene expression
Preininger, M., Arafat, D., Kim, J., Nath, A. P., Idaghdour, Y., Brigham, K. L., & Gibson, G. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003362 · Cited by 59 · see citing works
Rubel, J. A., Zilcha-Mano, S., Giesemann, J., Prinz, J., & Lutz, W. (2020) · Psychotherapy Research · doi:10.1080/10503307.2019.1597994 · Cited by 56 · see citing works
Zilcha-Mano, S., Keefe, J. R., Chui, H., Rubin, A., Barrett, M. S., & Barber, J. P. (2016) · The Journal of Clinical Psychiatry · doi:10.4088/jcp.15m10081 · Cited by 55 · see citing works
Comprehensive pharmacogenomic characterization of gastric cancer
Sa, J. K., Hong, J. Y., Lee, I.-K., Kim, J.-S., Sim, M.-H., Kim, H. J., An, J. Y., Sohn, T. S., Lee, J. H., Bae, J. M., et al. (2020) · Genome Medicine · doi:10.1186/s13073-020-0717-8 · Cited by 46 · see citing works
Hankin, B. L., Young, J. F., Gallop, R., & Garber, J. (2018) · Journal of Abnormal Child Psychology · doi:10.1007/s10802-018-0401-2 · Cited by 43 · see citing works
Son, Y., Clouston, S. A. P., Kotov, R., Eichstaedt, J. C., Bromet, E. J., Luft, B. J., & Schwartz, H. A. (2023) · Psychological Medicine · doi:10.1017/s0033291721002294 · Cited by 42 · see citing works
Outstanding Scientific Achievement Award Lecture 2011: Defeating Diabesity
Tschöp, M. H., & DiMarchi, R. D. (2012) · Diabetes · doi:10.2337/db12-0272 · Cited by 32 · see citing works
Brotto, L. A., Zdaniuk, B., Rietchel, L., Basson, R., & Bergeron, S. (2020) · The Journal of Sexual Medicine · doi:10.1016/j.jsxm.2020.07.080 · Cited by 29 · see citing works
Making preventive medicine more personalized. Implications for exercise-related research.
Buford, T. W., & Pahor, M. (2012) · Preventive Medicine · doi:10.1016/j.ypmed.2012.05.001 · Cited by 28 · see citing works
Deshpande, S., Rivera, D. E., Younger, J. W., & Nandola, N. N. (2014) · Translational Behavioral Medicine · doi:10.1007/s13142-014-0282-z · Cited by 24 · see citing works
Madani, N., & Mojra, A. (2017) · Journal of the Mechanical Behavior of Biomedical Materials · doi:10.1016/j.jmbbm.2017.01.044 · Cited by 21 · see citing works
Taub, R., Horesh, D., Rubin, N., Glick, I., Reem, O., Shriqui, G., & Agmon-Levin, N. (2021) · Journal of Clinical Medicine · doi:10.3390/jcm10194450 · Cited by 19 · see citing works
Separation Anxiety Symptom Profiles and Parental Accommodation Across Pediatric Anxiety Disorders
Phillips, K. E., Norris, L. A., & Kendall, P. C. (2020) · Child Psychiatry & Human Development · doi:10.1007/s10578-019-00949-7 · Cited by 8 · see citing works
Multiplexing AAV Serotype-Specific Neutralizing Antibodies in Preclinical Animal Models and Humans
Kuoch, H., Krotova, K., Graham, M. L., Brantly, M. L., & Aslanidi, G. (2023) · Biomedicines · doi:10.3390/biomedicines11020523 · Cited by 5 · see citing works
- Authors
- Hamburg, M. A., & Collins, F. S.
- Affiliations
- National Institutes of Health (US)
- ORCID
- Collins, Francis S.
- Venue
- New England Journal of Medicine
- Publisher
- Massachusetts Medical Society
- Published
- 2010-07-22
- Volume
- 363
- Issue
- 4
- Pages
- 301-304
- ISSN
- 0028-4793 (ISSN-L)
- 1533-4406
- OpenAlex
- W2093065590
- Cited by
- 1,970
- References
- 0
- Citations by year
- 2026: 55 · 2025: 93 · 2024: 114 · 2023: 91 · 2022: 106 · 2021: 113 · 2020: 134 · 2019: 125 · 2018: 113 · 2017: 148 · 2016: 149 · 2015: 177 · 2014: 153 · 2013: 128 · 2012: 125
- FWCI
- 127.19
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- New England Journal of Medicine (publishedVersion)
- PubMed (publishedVersion)
- DigitalGeorgetown (Georgetown University Library) (submittedVersion)
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Pharmacology › Pharmacogenetics and Drug Metabolism
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Keywords
- personalized medicine, genetic variation, genetic susceptibility, clinical translation, basic research
- MeSH terms
- Cooperative Behavior, Drug Evaluation, Preclinical, Genetic Testing, Health Policy, Humans, National Institutes of Health (U.S.), Pharmacogenetics, Registries, United States, United States Food and Drug Administration, Diagnostic Techniques and Procedures, Government Regulation, Rare Diseases, Translational Research, Biomedical, 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
