Evidence map›Paper›PMID 39071116›Full record

ArticleTrends in analytical chemistry : TRAC2024

Artificial Intelligence in Metabolomics: A Current Review.

Jinhua Chi, Jingmin Shu, Ming Li, Rekha Mudappathi, Yan Jin, Freeman Lewis, Alexandria Boon, Xiaoyan Qin, Li Liu, Haiwei Gu

Abstract read
In one paragraph

Article in Trends in analytical chemistry : TRAC, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.

0numbers the graph read from it
0cells of the map it votes in
31citing 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

31 citing papers in PubMed.

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  19. A Pilot Metabolomic Study for DiagnosingInternational journal of molecular sciences · 2025
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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

10 authors.

Jinhua ChiCollege of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA.
Jingmin ShuCollege of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA.
Ming LiPhoenix VA Health Care System, Phoenix, AZ 85012, USA.
Rekha MudappathiCollege of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA.
Yan JinCenter for Translational Science, Florida International University, Port St. Lucie, FL 34987, USA.
Freeman LewisCenter for Translational Science, Florida International University, Port St. Lucie, FL 34987, USA.
Alexandria BoonCenter for Translational Science, Florida International University, Port St. Lucie, FL 34987, USA.
Xiaoyan QinCollege of Liberal Arts and Sciences, Arizona State University, Tempe, AZ 85281, USA.
Li LiuCollege of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA.
Haiwei GuCollege of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA.

Funding

Developmental PBDE exposure, gut microbiome, and diabetesR01ES030197 · NIEHS · UNIVERSITY OF WASHINGTON · PI Julia Yue Cui, Haiwei Gu · 2019 to 2026
$5.0M
Interdisciplinary Systems-based Training for Precision NutritionT32DK137525 · NIDDK · ARIZONA STATE UNIVERSITY-TEMPE CAMPUS · PI Li Liu, Corrie Marie Whisner · 2023 to 2026
$1.4M
Discover and Analyze Germline-Somatic Interactions in CancerR01LM013438 · NLM · ARIZONA STATE UNIVERSITY-TEMPE CAMPUS · PI LIU, LI, YANG, PING · 2021 to 2023
$1.0M
NIDDK NIH HHS T32 DK137525NIEHS NIH HHS R01 ES030197NLM NIH HHS R01 LM013438
6 · The paper itself

Abstract

Metabolomics and artificial intelligence (AI) form a synergistic partnership. Metabolomics generates large datasets comprising hundreds to thousands of metabolites with complex relationships. AI, aiming to mimic human intelligence through computational modeling, possesses extraordinary capabilities for big data analysis. In this review, we provide a recent overview of the methodologies and applications of AI in metabolomics studies in the context of systems biology and human health. We first introduce the AI concept, history, and key algorithms for machine learning and deep learning, summarizing their strengths and weaknesses. We then discuss studies that have successfully used AI across different aspects of metabolomic analysis, including analytical detection, data preprocessing, biomarker discovery, predictive modeling, and multi-omics data integration. Lastly, we discuss the existing challenges and future perspectives in this rapidly evolving field. Despite limitations and challenges, the combination of metabolomics and AI holds great promises for revolutionary advancements in enhancing human health.

Indexed as

Artificial IntelligenceDeep LearningDisease DiagnosisDrug DiscoveryMachine LearningMetabolomicsPrecision MedicineSystems Biology

Identifiers

PMID39071116
PMCPMC11271759

What Socratic holds

Textmetadata
LicenceTDM
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.