ReviewMedComm2025
Obesity Biomarkers: Exploring Factors, Ramification, Machine Learning, and AI-Unveiling Insights in Health Research.
Review in MedComm, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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.
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.
Who cites it
8 citing papers in PubMed.
- Artificial intelligence in biomarker discovery for diseases: diagnostic and therapeutic prospects.Signal transduction and targeted therapy · 2026Review
- Article
- Sex Differences in Psychotropic Drug Exposure and Safety: A Systematic Review Toward Personalized Dosing Strategies.Journal of personalized medicine · 2026Review
- Oral and cardiometabolic health through the lens of biobanks and large-scale epidemiologic research.Frontiers in oral health · 2026Review
- FADS2 and ALDOC as potential adipose tissue biomarkers in obesity: responses to low-calorie diet-feeding.Journal of translational medicine · 2025Article
- Photobiomodulation with IR and RED light acutely applied to lipedema patients: preliminary study with 3 cases.Lasers in medical science · 2025Article
- Unraveling the Complexity of Metabolic Disorders Through Biomarkers: A Focus on Obesity and Type 2 Diabetes Mellitus.Biomedicines · 2025Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Biomarkers play a pivotal role in the detection and management of diseases, including obesity-a growing global health crisis with complex biological underpinnings. The multifaceted nature of obesity, coupled with socioeconomic disparities, underscores the urgent need for precise diagnostic and therapeutic approaches. Recent advances in biosciences, including next-generation sequencing, multi-omics analysis, high-resolution imaging, and smart sensors, have revolutionized data generation. However, effectively leveraging these data-rich technologies to identify and validate obesity-related biomarkers remains a significant challenge. This review bridges this gap by highlighting the potential of machine learning (ML) in obesity research. Specifically, it explores how ML techniques can process complex data sets to enhance the discovery and validation of biomarkers. Additionally, it examines the integration of advanced technologies for understanding obesity mechanisms, assessing risk factors, and optimizing treatment strategies. A detailed discussion is provided on the applications of ML in multi-omics analysis and high-throughput data integration for actionable insights. The academic value of this review lies in synthesizing the latest technological and analytical innovations in obesity research. By providing a comprehensive overview, it aims to guide future studies and foster the development of targeted, data-driven strategies in obesity management.
Indexed as
Identifiers
What Socratic holds
Registered trials
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.