ArticlePatterns (New York, N.Y.)2020
A Distance-Based Framework for the Characterization of Metabolic Heterogeneity in Large Sets of Genome-Scale Metabolic Models.
Article in Patterns (New York, N.Y.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 10 papers.
What it found
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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.
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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.
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Who cites it
10 citing papers in PubMed, 18 citations in OpenAlex.
- Trans-omics analyses identify the biochemical network of LPCAT1 associated with coronary artery disease.Biomarker research · 2025Article
- Dissecting metabolic landscape of alveolar macrophage.Scientific reports · 2024Article
- Logistic PCA explains differences between genome-scale metabolic models in terms of metabolic pathways.PLoS computational biology · 2024Article
- Flux sampling in genome-scale metabolic modeling of microbial communities.BMC bioinformatics · 2024Article
- Genome-scale modeling predicts metabolic differences between macrophage subtypes in colorectal cancer.iScience · 2023Article
- Flux Sampling in Genome-scale Metabolic Modeling of Microbial Communities.bioRxiv : the preprint server for biology · 2023Article
- Ensemble-based genome-scale modeling predicts metabolic differences between macrophage subtypes in colorectal cancer.bioRxiv : the preprint server for biology · 2023Article
- A group theoretic approach to model comparison with simplicial representations.Journal of mathematical biology · 2022Article
- SALARECON connects the Atlantic salmon genome to growth and feed efficiency.PLoS computational biology · 2022Article
- Simulating Metabolic Flexibility in Low Energy Expenditure Conditions Using Genome-Scale Metabolic Models.Metabolites · 2021Article
Corrections and comments
- Erratum issued
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gene expression and protein abundance data of cells or tissues belonging to healthy and diseased individuals can be integrated and mapped onto genome-scale metabolic networks to produce patient-derived models. As the number of available and newly developed genome-scale metabolic models increases, new methods are needed to objectively analyze large sets of models and to identify the determinants of metabolic heterogeneity. We developed a distance-based workflow that combines consensus machine learning and metabolic modeling techniques and used it to apply pattern recognition algorithms to collections of genome-scale metabolic models, both microbial and human. Model composition, network topology and flux distribution provide complementary aspects of metabolic heterogeneity in patient-specific genome-scale models of skeletal muscle. Using consensus clustering analysis we identified the metabolic processes involved in the individual responses to endurance training in older adults.
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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.