The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Identification of type 2 diabetes subgroups through topological analysis of patient similarity
Open accessAbstract
Patient networks constructed from genotype data and electronic medical records pinpointed three type 2 diabetes subtypes.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625
Cytoscape: A software Environment for integrated models of biomolecular interaction networks
Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., & Ideker, T. (2003) · Genome Research · doi:10.1101/gr.1239303 · Cited by 51,888
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482
An integrated map of genetic variation from 1,092 human genomes
The 1000 Genomes Project Consortium (2012) · Nature · doi:10.1038/nature11632 · Cited by 7,711
Diagnosis and classification of diabetes mellitus
American Diabetes Association (2010) · Diabetes Care · doi:10.2337/dc10-s062 · Cited by 5,491
Howie, B. N., Donnelly, P., & Marchini, J. (2009) · PLoS Genetics · doi:10.1371/journal.pgen.1000529 · Cited by 3,928
Cardiovascular effects of intensive lifestyle intervention in type 2 diabetes
The Look AHEAD Research Group (2013) · New England Journal of Medicine · doi:10.1056/nejmoa1212914 · Cited by 2,908
Giovannucci, E., Harlan, D. M., Archer, M. C., Bergenstal, R. M., Gapstur, S. M., Habel, L. A., Pollak, M., Regensteiner, J. G., & Yee, D. (2010) · Diabetes Care · doi:10.2337/dc10-0666 · Cited by 2,366
Carlsson, G. (2009) · Bulletin of the American Mathematical Society · doi:10.1090/s0273-0979-09-01249-x · Cited by 2,302 · cited 1×
“We used Ayasdi 3.0 ( 79 , 80 ) (http://ayasdi.com, Ayasdi Inc.) to perform TDA for generating the patient-patient network.”
MethodInfluentialStandards of Medical Care in Diabetes—2009
American Diabetes Association (2009) · Diabetes Care · doi:10.2337/dc09-s013 · Cited by 2,167
Cited by
Deep learning for healthcare: review, opportunities and challenges
Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bbx044 · Cited by 3,076
Self-citationAhlqvist, E., Storm, P., Käräjämäki, A., Martinell, M., Dorkhan, M., Carlsson, A., Vikman, P., Prasad, R. B., Aly, D. M., Almgren, P., et al. (2018) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(18)30051-2 · Cited by 2,335
Big data in healthcare: management, analysis and future prospects
Dash, S., Shakyawar, S. K., Sharma, M., & Kaushik, S. (2019) · Journal of Big Data · doi:10.1186/s40537-019-0217-0 · Cited by 1,747
Miotto, R., Li, L., Kidd, B. A., & Dudley, J. T. (2016) · Scientific Reports · doi:10.1038/srep26094 · Cited by 1,710
Self-citationArtificial intelligence in cardiology
Johnson, K. W., Torres Soto, J., Glicksberg, B. S., Shameer, K., Miotto, R., Ali, M., Ashley, E., & Dudley, J. T. (2018) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2018.03.521 · Cited by 1,202
Self-citationAhmad, E., Lim, S., Lamptey, R., Webb, D. R., & Davies, M. J. (2022) · The Lancet · doi:10.1016/s0140-6736(22)01655-5 · Cited by 1,120
Ahmed, Z., Mohamed, K., Zeeshan, S., & Dong, X. (2020) · Database · doi:10.1093/database/baaa010 · Cited by 1,035
Anti-obesity drug discovery: advances and challenges
Müller, T. D., Blüher, M., Tschöp, M. H., & DiMarchi, R. D. (2022) · Nature Reviews Drug Discovery · doi:10.1038/s41573-021-00337-8 · Cited by 1,021
Udler, M. S., Kim, J., von Grotthuss, M., Bonàs-Guarch, S., Cole, J. B., Chiou, J., ISGC, C. D. A. O. B. O. M. A. T., Boehnke, M., Laakso, M., Atzmon, G., et al. (2018) · PLOS Medicine · doi:10.1371/journal.pmed.1002654 · Cited by 542
Machine learning in cardiovascular medicine: are we there yet?
Shameer, K., Johnson, K. W., Glicksberg, B. S., Dudley, J. T., & Sengupta, P. P. (2018) · Heart · doi:10.1136/heartjnl-2017-311198 · Cited by 555
Self-citationEnabling 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
Hepatic fibrosis 2022: Unmet needs and a blueprint for the future
Friedman, S. L., & Pinzani, M. (2022) · Hepatology · doi:10.1002/hep.32285 · Cited by 430
AI in Health: State of the Art, Challenges, and Future Directions
Wang, F., & Preininger, A. (2019) · Yearbook of Medical Informatics · doi:10.1055/s-0039-1677908 · Cited by 398
Dapas, M., Lin, F. T. J., Nadkarni, G. N., Sisk, R., Legro, R. S., Urbanek, M., Hayes, M. G., & Dunaif, A. (2020) · PLOS Medicine · doi:10.1371/journal.pmed.1003132 · Cited by 297
Genetic Risk Scores for Diabetes Diagnosis and Precision Medicine
Udler, M. S., McCarthy, M. I., Florez, J. C., & Mahajan, A. (2019) · Endocrine Reviews · doi:10.1210/er.2019-00088 · Cited by 302
Ye, C., Fu, T., Hao, S., Zhang, Y., Wang, O., Jin, B., Xia, M., Liu, M., Zhou, X., Wu, Q., et al. (2018) · Journal of Medical Internet Research · doi:10.2196/jmir.9268 · Cited by 250
Lost in thought—the limits of the human mind and the future of medicine
Obermeyer, Z., & Lee, T. H. (2017) · New England Journal of Medicine · doi:10.1056/nejmp1705348 · Cited by 252
Rizvi, A. H., Camara, P. G., Kandror, E. K., Roberts, T. J., Schieren, I., Maniatis, T., & Rabadan, R. (2017) · Nature Biotechnology · doi:10.1038/nbt.3854 · Cited by 248
Ayala Solares, J. R., Diletta Raimondi, F. E., Zhu, Y., Rahimian, F., Canoy, D., Tran, J., Pinho Gomes, A. C., Payberah, A. H., Zottoli, M., Nazarzadeh, M., et al. (2020) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103337 · Cited by 247
Dysregulation of lipids in Alzheimer’s disease and their role as potential biomarkers
Wong, M. W., Braidy, N., Poljak, A., Pickford, R., Thambisetty, M., & Sachdev, P. S. (2017) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2017.01.008 · Cited by 223
Deep representation learning of electronic health records to unlock patient stratification at scale
Landi, I., Glicksberg, B. S., Lee, H.-C., Cherng, S., Landi, G., Danieletto, M., Dudley, J. T., Furlanello, C., & Miotto, R. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0301-z · Cited by 213
Self-citationShameer, K., Badgeley, M. A., Miotto, R., Glicksberg, B. S., Morgan, J. W., & Dudley, J. T. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbv118 · Cited by 198
Self-citationA novel diabetes typology: towards precision diabetology from pathogenesis to treatment
Herder, C., & Roden, M. (2022) · Diabetologia · doi:10.1007/s00125-021-05625-x · Cited by 140
Using Electronic Health Records To Generate Phenotypes For Research
Pendergrass, S. A., & Crawford, D. C. (2019) · Current Protocols in Human Genetics · doi:10.1002/cphg.80 · Cited by 120
Stress-impaired transcription factor expression and insulin secretion in transplanted human islets
Dai, C., Kayton, N. S., Shostak, A., Poffenberger, G., Cyphert, H. A., Aramandla, R., Thompson, C., Papagiannis, I. G., Emfinger, C., Shiota, M., et al. (2016) · Journal of Clinical Investigation · doi:10.1172/jci83657 · Cited by 109
Phenotypic and genetic classification of diabetes
Deutsch, A. J., Ahlqvist, E., & Udler, M. S. (2022) · Diabetologia · doi:10.1007/s00125-022-05769-4 · Cited by 108
Harnessing EHR data for health research
Tang, A. S., Woldemariam, S. R., Miramontes, S., Norgeot, B., Oskotsky, T. T., & Sirota, M. (2024) · Nature Medicine · doi:10.1038/s41591-024-03074-8 · Cited by 87
Enabling Precision Cardiology Through Multiscale Biology and Systems Medicine
Johnson, K. W., Shameer, K., Glicksberg, B. S., Readhead, B., Sengupta, P. P., Björkegren, J. L. M., Kovacic, J. C., & Dudley, J. T. (2017) · JACC: Basic to Translational Science · doi:10.1016/j.jacbts.2016.11.010 · Cited by 83
Self-citationArtificial Intelligence in Cardiovascular Medicine
Seetharam, K., Shrestha, S., & Sengupta, P. P. (2019) · Current Treatment Options in Cardiovascular Medicine · doi:10.1007/s11936-019-0728-1 · Cited by 76
Deletion of Tmtc4 activates the unfolded protein response and causes postnatal hearing loss
Li, J., Akil, O., Rouse, S. L., McLaughlin, C. W., Matthews, I. R., Lustig, L. R., Chan, D. K., & Sherr, E. H. (2018) · Journal of Clinical Investigation · doi:10.1172/jci97498 · Cited by 61
Tang, A. S., Oskotsky, T., Havaldar, S., Mantyh, W. G., Bicak, M., Solsberg, C. W., Woldemariam, S., Zeng, B., Hu, Z., Oskotsky, B., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-28273-0 · Cited by 45
Self-citationSystems and precision medicine approaches to diabetes heterogeneity: a Big Data perspective
Capobianco, E. (2017) · Clinical and Translational Medicine · doi:10.1186/s40169-017-0155-4 · Cited by 39
Trust and Transparency in Machine Learning-Based Clinical Decision Support
Gretton, C. (2018) · Human–Computer Interaction Series · doi:10.1007/978-3-319-90403-0_14 · Cited by 27
Metadata
- Authors
- Li, L., Cheng, W.-Y., Glicksberg, B. S., Gottesman, O., Tamler, R., Chen, R., Bottinger, E. P., & Dudley, J. T.
- Venue
- Science Translational Medicine
- Published
- 2015-10-28
- Volume
- 7
- Issue
- 311
- Pages
- 311ra174-311ra174
- ISSN
- 1946-6234 (ISSN-L)
- 1946-6242
- OpenAlex
- W1839682376
- Cited by
- 549
- References
- 19
- FWCI
- 27.55
- Type
- journal article
- Language
- en
- Open access
- green
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
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
