In one paragraphReview in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
2 · The registryThe trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
3 · Its place in the literatureWho cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
4 · The recordCorrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what moneyAuthors and funding
15 authors.
Paraskevi Massara *Cornell Joan Klein Jacobs Center for Precision Nutrition and Health, Cornell University, Ithaca, NY, USA.ORCID 0000-0003-0906-0985 Jonathan Kirkland *Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA.ORCID 0009-0001-6633-1844 Ioanna PaganiCornell Joan Klein Jacobs Center for Precision Nutrition and Health, Cornell University, Ithaca, NY, USA.
Samantha L HueyCornell Joan Klein Jacobs Center for Precision Nutrition and Health, Cornell University, Ithaca, NY, USA.ORCID 0000-0002-9774-8642 Haym HirshCornell Joan Klein Jacobs Center for Precision Nutrition and Health, Cornell University, Ithaca, NY, USA.
Daniel McDonaldDepartment of Pediatrics, University of California San Diego, La Jolla, CA, USA.
Lucas PatelBioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA.ORCID 0000-0001-8607-2782 Julia L FinkelsteinCornell Joan Klein Jacobs Center for Precision Nutrition and Health, Cornell University, Ithaca, NY, USA.ORCID 0000-0001-9512-5559 Marie GantzRTI International, Durham, NC, USA.
Fei WangCornell Joan Klein Jacobs Center for Precision Nutrition and Health, Cornell University, Ithaca, NY, USA.ORCID 0000-0001-9459-9461 David EricksonCornell Joan Klein Jacobs Center for Precision Nutrition and Health, Cornell University, Ithaca, NY, USA.
Martin T WellsCornell Joan Klein Jacobs Center for Precision Nutrition and Health, Cornell University, Ithaca, NY, USA.
Olivier ElementoCornell Joan Klein Jacobs Center for Precision Nutrition and Health, Cornell University, Ithaca, NY, USA.ORCID 0000-0002-8061-9617 Rob KnightDepartment of Pediatrics, University of California San Diego, La Jolla, CA, USA. rknight@ucsd.edu.ORCID 0000-0002-0975-9019 Saurabh MehtaCornell Joan Klein Jacobs Center for Precision Nutrition and Health, Cornell University, Ithaca, NY, USA. smehta@cornell.edu.ORCID 0000-0003-3788-0665 Funding
MEDICAL SCIENTIST TRAINING PROGRAMT32GM007198 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI CHI, NEIL C, INSEL, PAUL A · 1985 to 2024
$29.3MGraduate Training Program in BioinformaticsT32GM139790 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI BAFNA, VINEET, GAASTERLAND, THERESA · 2021 to 2025
$2.1MFoundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.) 3U24HD107676Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.) 5T32HD087137, 5T32HD113301Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.) 5T32HD113301U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS) T32GM007198U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS) T32GM139790
6 · The paper itselfAbstract
A key feature of the Precision Nutrition and Health approach is the ability to tailor interventions to individual variability using multimodal data from large-scale biobanks and cohorts. Artificial intelligence (AI) and machine learning (ML) models offer new potential to model complex data but remain constrained by challenges related to data quality, interpretability, validation, and causal inference. This Perspective synthesizes current AI/ML methodologies in PN, elucidates their interplay with the distinctive features of multi-omic and nutritional data, such as being compositional, episodic, context-dependent, and error-prone, and delineates nutrition-specific best practices for achieving robust, interpretable, and clinically actionable AI integration in research and practice.
Indexed as
Artificial IntelligenceMachine LearningNutritional SciencesPrecision MedicineDeep LearningDietDigital TechnologyHumansLarge Language Models
Identifiers
PMID42409828
PMCPMC13338271
What OpenQuestion holds
Textmetadata
LicenceCC BY-NC-ND
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