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
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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- CoChat. (2026). Citation check: Metabolomics in nutritional epidemiology: identifying metabolites associated with diet and quantifying their potential to uncover diet-disease relations in populations. https://app.cochat.ai/papers/10.3945/ajcn.113.078758
- The paper
- Guertin, K. A., Moore, S. C., Sampson, J. N., Huang, W.-Y., Xiao, Q., Stolzenberg-Solomon, R. Z., Sinha, R., & Cross, A. J. (2014). Metabolomics in nutritional epidemiology: identifying metabolites associated with diet and quantifying their potential to uncover diet-disease relations in populations. The American Journal of Clinical Nutrition. https://doi.org/10.3945/ajcn.113.078758
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.
