ReviewMetabolites2023
Integrating Omics Data in Genome-Scale Metabolic Modeling: A Methodological Perspective for Precision Medicine.
Review in Metabolites, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 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
27 citing papers in PubMed.
- A historical journey of metabolite-protein interaction discovery: from data harmonization to AI-driven prediction.Briefings in bioinformatics · 2026Review
- Predictive Algal Systems Biology: Integrating Omics, Genome-Scale Metabolic Models, and Machine Learning.Bioengineering (Basel, Switzerland) · 2026Review
- Metabolomics profiling of acute myelogenous leukemia patients to identify systemic differences associated with in vitro sensitivity to SYK inhibitors.Metabolomics : Official journal of the Metabolomic Society · 2026Article
- Article
- Computational and multi-omics systems biology for precision microbiome therapeutics.Frontiers in microbiomes · 2026Review
- Integrating Artificial Intelligence with Global Genomic Resources: A Narrative Review of Implications for Precision Medicine.Journal of multidisciplinary healthcare · 2026Review
- Mathematical Modeling in Cancer Metabolism: Tools for Translational Applications in Metabolism-Based Therapy.Advances in experimental medicine and biology · 2026Review
- Personalized Constraint-Based Modeling of Microbial Communities from Metagenomic Data.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Microbiome-mycotoxin interactions and probiotic strategies: implications for gut health and cancer.Frontiers in nutrition · 2026Review
- Precision nutrition through diet-gut microbiome interactions: Emerging insights driven by artificial intelligence, microbiome health metrics, and mechanistic modeling.Gut microbes reports · 2026Review
- AI-driven multi-omics integration in precision oncology: bridging the data deluge to clinical decisions.Clinical and experimental medicine · 2025Review
- Metatranscriptomics-based metabolic modeling of patient-specific urinary microbiome during infection.NPJ biofilms and microbiomes · 2025Article
- Longitudinal big biological data in the AI era.Molecular systems biology · 2025Review
- Article
- Model of metabolism and gene expression predicts proteome allocation in Pseudomonas putida.NPJ systems biology and applications · 2025Article
- COmmunity and Single Microbe Optimisation System (COSMOS).NPJ systems biology and applications · 2025Article
- Modulation of the Neuro-Cancer Connection by Metabolites of Gut Microbiota.Biomolecules · 2025Review
- Integration of metatranscriptomics data improves the predictive capacity of microbial community metabolic models.The ISME journal · 2025Article
- Editorial: Precision nutrition and nutrients: making the promise a reality.Frontiers in nutrition · 2025Article
- Charting the state of GEMs in microalgae: progress, challenges, and innovations.Frontiers in plant science · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Recent advancements in omics technologies have generated a wealth of biological data. Integrating these data within mathematical models is essential to fully leverage their potential. Genome-scale metabolic models (GEMs) provide a robust framework for studying complex biological systems. GEMs have significantly contributed to our understanding of human metabolism, including the intrinsic relationship between the gut microbiome and the host metabolism. In this review, we highlight the contributions of GEMs and discuss the critical challenges that must be overcome to ensure their reproducibility and enhance their prediction accuracy, particularly in the context of precision medicine. We also explore the role of machine learning in addressing these challenges within GEMs. The integration of omics data with GEMs has the potential to lead to new insights, and to advance our understanding of molecular mechanisms in human health and disease.
Indexed as
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What OpenQuestion 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 OpenQuestion graph.