Evidence map›Paper›PMID 41559079›Full record

ArticleNPJ science of food2026

A unified knowledge graph linking foodomics to chemical-disease networks and flavor profiles.

Fangzhou Li, Jason Youn, Kaichi Xie, Trevor Chan, Pranav Gupta, Arielle Yoo, Michael Gunning, Keer Ni, Ilias Tagkopoulos

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ science of food, 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
1 · What the graph read from it

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 registry

The 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 literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Fangzhou LiDepartment of Computer Science, the University of California at Davis, Davis, CA, USA.
Jason YounDepartment of Computer Science, the University of California at Davis, Davis, CA, USA.
Kaichi XieDepartment of Computer Science, the University of California at Davis, Davis, CA, USA.
Trevor ChanDepartment of Computer Science, the University of California at Davis, Davis, CA, USA.
Pranav GuptaDepartment of Computer Science, the University of California at Davis, Davis, CA, USA.
Arielle YooGenome Center, the University of California at Davis, Davis, CA, USA.
Michael GunningDepartment of Computer Science, the University of California at Davis, Davis, CA, USA.
Keer NiDepartment of Computer Science, the University of California at Davis, Davis, CA, USA.
Ilias TagkopoulosDepartment of Computer Science, the University of California at Davis, Davis, CA, USA. itagkopoulos@ucdavis.edu.

Funding

National Institute of Food and Agriculture 2020-67021-32855
6 · The paper itself

Abstract

Modern nutrition science still lacks a comprehensive, machine-readable map linking diet to molecular composition and biological effects. Here we present FoodAtlas, a large-scale knowledge graph that links 1430 foods to 3610 chemicals, 2181 diseases, and 958 flavor descriptors through 96,981 provenance-tracked edges. A transformer-based text-mining pipeline extracted 48,474 quantitative food-chemical associations from 125,723 literature sentences (F

Identifiers

PMID41559079
PMCPMC12868623

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.