Evidence map›Paper›PMID 42773640›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2026

Towards a Compositional Framework for Describing Human Phenotypes.

Wanting Hu, Yunchao Ling, Xinyue Hu, Wei Chen, Rong Zhou, Xinhao Zhuang, Yanfeng Jiang, Jialu Zhao, Xingdong Chen, Jingchun Luo and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 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

12 authors.

Wanting Hu *Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of the Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.
Yunchao Ling *Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of the Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.
Xinyue Hu *Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of the Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.
Wei Chen *Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of the Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.
Rong ZhouBio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of the Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.ORCID https://orcid.org/0000-0001-7637-6547
Xinhao ZhuangBio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of the Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.
Yanfeng JiangHuman Phenome Institute, Fudan University, Shanghai, China.ORCID https://orcid.org/0000-0003-4976-6333
Jialu ZhaoHuman Phenome Institute, Fudan University, Shanghai, China.
Xingdong ChenState Key Laboratory of Genetics and Development of Complex Phenotypes, School of Public Health, National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China.ORCID https://orcid.org/0000-0003-3763-160X
Jingchun LuoHuman Phenome Institute, Fudan University, Shanghai, China.ORCID https://orcid.org/0000-0001-5138-6458
Li JinHuman Phenome Institute, Fudan University, Shanghai, China.
Guoqing ZhangBio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of the Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.ORCID https://orcid.org/0000-0001-8827-7546

Funding

Noncommunicable Chronic Diseases-National Science and Technology Major Project 2023ZD0510003Shanghai Municipal Science and Technology Major Project 2017SHZDZX01Shanghai Municipal Science and Technology Major Project 2023SHZDZX02,GTPShanghai Oriental Talents Program BJKJ2025055Shanghai Science and Technology Innovation Action Plan 23JS1401500
6 · The paper itself

Abstract

Deep phenotyping increasingly spans scales from macro to micro, yet heterogeneity in measurement and documentation still constrains standardization and limits comparability across cohorts. This study introduces a compositional framework that integrates the Phenotype Assembly Method (PhenoAM), Phenome Data Elements (PhenoDE), and a suite of large language model (LLM)-based PhenoAgents to support systematic, interpretable, and scalable phenotype standardization with quality assessment. PhenoAM decomposes phenotype descriptions into features and qualifiers to enable component-level mapping, while PhenoDE provides standardized representations of 58 371 phenotype data elements across 22 measurement platforms of the International Human Phenome Project (IHPP); pooled mapping coverage was 98.1% within the 18-platform analysis set and 99.8% across all 22 platforms. Using this representation, analyses of illustrative phenotypes from IHPP, National Health and Nutrition Examination Survey (NHANES), and the UK Biobank show that differences in features or qualifiers can influence data distributions and analytic outcomes, underscoring the importance of explicit measurement context in cross-cohort studies. PhenoAgents support semantic parsing, terminology candidate retrieval, and data-quality review through curator-assisted modules. PhenoDEs and their statistical distributions within IHPP are accessible on the PhenoDE Portal.

Indexed as

anomaly screeningdata element standardizationdata quality assessmentintegrative researchinternational human phenome projectlarge language model agentsphenotype assembly

Identifiers

PMID42773640
PMCPMC13598380

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Registered trials

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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 OpenQuestion graph.