The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Metabolomics in nutritional epidemiology: identifying metabolites associated with diet and quantifying their potential to uncover diet-disease relations in populations
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Cites
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
Human metabolic individuality in biomedical and pharmaceutical research
CARDIoGRAM, Suhre, K., Shin, S.-Y., Petersen, A.-K., Mohney, R. P., Meredith, D., Wägele, B., Altmaier, E., Deloukas, P., Erdmann, J., et al. (2011) · Nature · doi:10.1038/nature10354 · Cited by 1,017
Biomarkers in nutritional epidemiology: applications, needs and new horizons
Jenab, M., Slimani, N., Bictash, M., Ferrari, P., & Bingham, S. A. (2009) · Human Genetics · doi:10.1007/s00439-009-0662-5 · Cited by 448
O'Sullivan, A., Gibney, M. J., & Brennan, L. (2011) · The American Journal of Clinical Nutrition · doi:10.3945/ajcn.110.000950 · Cited by 297
Pujos-Guillot, E., Hubert, J., Martin, J.-F., Lyan, B., Quintana, M., Claude, S., Chabanas, B., Rothwell, J. A., Bennetau-Pelissero, C., Scalbert, A., et al. (2013) · Journal of Proteome Research · doi:10.1021/pr300997c · Cited by 166
Cited by
Aune, D., Keum, N., Giovannucci, E., Fadnes, L. T., Boffetta, P., Greenwood, D. C., Tonstad, S., Vatten, L. J., Riboli, E., & Norat, T. (2018) · The American Journal of Clinical Nutrition · doi:10.1093/ajcn/nqy097 · Cited by 392
Individual variability in human blood metabolites identifies age-related differences
Chaleckis, R., Murakami, I., Takada, J., Kondoh, H., & Yanagida, M. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1603023113 · Cited by 373
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
Dietary patterns and cancer risk
Steck, S. E., & Murphy, E. A. (2020) · Nature Reviews Cancer · doi:10.1038/s41568-019-0227-4 · Cited by 292
Biomarkers of Nutrition and Health: New Tools for New Approaches
Picó, C., Serra, F., Rodríguez, A. M., Keijer, J., & Palou, A. (2019) · Nutrients · doi:10.3390/nu11051092 · Cited by 287
Wearable and Mobile Sensors for Personalized Nutrition
Sempionatto, J. R., Montiel, V. R.-V., Vargas, E., Teymourian, H., & Wang, J. (2021) · ACS Sensors · doi:10.1021/acssensors.1c00553 · Cited by 237
Human metabolome variation along the upper intestinal tract
Folz, J., Culver, R. N., Morales, J. M., Grembi, J., Triadafilopoulos, G., Relman, D. A., Huang, K. C., Shalon, D., & Fiehn, O. (2023) · Nature Metabolism · doi:10.1038/s42255-023-00777-z · Cited by 143
Guertin, K. A., Loftfield, E., Boca, S. M., Sampson, J. N., Moore, S. C., Xiao, Q., Huang, W.-Y., Xiong, X., Freedman, N. D., Cross, A. J., et al. (2015) · The American Journal of Clinical Nutrition · doi:10.3945/ajcn.114.096099 · Cited by 134
Self-citationMetabolomics and microbiomes as potential tools to evaluate the effects of the Mediterranean diet
Jin, Q., Black, A., Kales, S. N., Vattem, D., Ruiz-Canela, M., & Sotos-Prieto, M. (2019) · Nutrients · doi:10.3390/nu11010207 · Cited by 100
Performance of plasma trigonelline as a marker of coffee consumption in an epidemiologic setting
Midttun, Ø., Ulvik, A., Nygård, O., & Ueland, P. M. (2018) · The American Journal of Clinical Nutrition · doi:10.1093/ajcn/nqy059 · Cited by 50
The PLCO Biorepository: Creating, Maintaining, and Administering a Unique Biospecimen Resource
Carrick, D., Black, A., Gohagan, J., Khan, A., Pettit, K., Williams, C., Yu, K., Yurgalevitch, S., Huang, W.-Y., & Zhu, C. (2015) · Reviews on Recent Clinical Trials · doi:10.2174/1574887110666150730121429 · Cited by 29
Self-citationTwin studies advance the understanding of gene-environment interplay in human nutrigenomics
Pallister, T., Spector, T. D., & Menni, C. (2014) · Nutrition Research Reviews · doi:10.1017/s095442241400016x · Cited by 20
Gadgil, M. D., Kanaya, A. M., Sands, C., Chekmeneva, E., Lewis, M. R., Kandula, N. R., & Herrington, D. M. (2022) · The Journal of Nutrition · doi:10.1093/jn/nxac191 · Cited by 16
Metadata
- Authors
- Guertin, K. A., Moore, S. C., Sampson, J. N., Huang, W.-Y., Xiao, Q., Stolzenberg-Solomon, R. Z., Sinha, R., & Cross, A. J.
- Venue
- The American Journal of Clinical Nutrition
- Publisher
- Elsevier BV
- Published
- 2014-07
- Volume
- 100
- Issue
- 1
- Pages
- 208-217
- ISSN
- 0002-9165 (ISSN-L)
- OpenAlex
- W2104309625
- Cited by
- 283
- References
- 5
- FWCI
- 8.15
- Type
- journal article
- Language
- en
- Open access
- bronze
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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.
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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.
