ReviewMetabolites2018
Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling.
Review in Metabolites, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers, 1 of them a synthesis that pooled 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.
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
56 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A priori sample size determination and power analysis in metabolic phenotyping and integrative metabolomics: an application framework based on a systematic review of literature.Metabolomics : Official journal of the Metabolomic Society · 2026Pooled it
- Metabolic engineering strategies: utilizing different microbial strains and advanced technologies for the synthesis of high-valued terpenoids.Antonie van Leeuwenhoek · 2026Review
- A Machine Learning Model to Reframe the Concept of Shelf-Life in Bakery Products: PDO Sourdough as a Technological Preservation Model.Foods (Basel, Switzerland) · 2025Article
- Exploring gene expression heterogeneity in burn wounds through machine learning models:Annals of medicine and surgery (2012) · 2025Article
- Binary classification of gynecological cancers based on ATR-FTIR spectroscopy and machine learning using urine samples.Clinical and experimental medicine · 2025Article
- Recent Developments in Single-Cell Metabolomics by Mass Spectrometry─A Perspective.Journal of proteome research · 2025Review
- Artificial intelligence driven innovations in biochemistry: A review of emerging research frontiers.Biomolecules & biomedicine · 2025Review
- Metabolomic profiling of dengue infection: unraveling molecular signatures by LC-MS/MS and machine learning models.Metabolomics : Official journal of the Metabolomic Society · 2024Article
- Artificial Intelligence in Metabolomics: A Current Review.Trends in analytical chemistry : TRAC · 2024Article
- Enhancing Withanolide Production in thePlants (Basel, Switzerland) · 2024Review
- Predicting pathways for old and new metabolites through clustering.Journal of theoretical biology · 2024Article
- Regulatory mechanisms of one-carbon metabolism enzymes.The Journal of biological chemistry · 2023Review
- Article
- Integrated data-driven cross-disciplinary framework to prevent chemical water pollution.One earth (Cambridge, Mass.) · 2023Article
- Integrating Omics Data in Genome-Scale Metabolic Modeling: A Methodological Perspective for Precision Medicine.Metabolites · 2023Review
- Deciphering the recent trends in pesticide bioremediation using genome editing and multi-omics approaches: a review.World journal of microbiology & biotechnology · 2023Review
- Association of Metabolomic Biomarkers with Sleeve Gastrectomy Weight Loss Outcomes.Metabolites · 2023Article
- Bayesian kinetic modeling for tracer-based metabolomic data.BMC bioinformatics · 2023Article
- Machine learning-assisted medium optimization revealed the discriminated strategies for improved production of the foreign and native metabolites.Computational and structural biotechnology journal · 2023Article
- Metabolomics and modelling approaches for systems metabolic engineering.Metabolic engineering communications · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning uses experimental data to optimize clustering or classification of samples or features, or to develop, augment or verify models that can be used to predict behavior or properties of systems. It is expected that machine learning will help provide actionable knowledge from a variety of big data including metabolomics data, as well as results of metabolism models. A variety of machine learning methods has been applied in bioinformatics and metabolism analyses including self-organizing maps, support vector machines, the kernel machine, Bayesian networks or fuzzy logic. To a lesser extent, machine learning has also been utilized to take advantage of the increasing availability of genomics and metabolomics data for the optimization of metabolic network models and their analysis. In this context, machine learning has aided the development of metabolic networks, the calculation of parameters for stoichiometric and kinetic models, as well as the analysis of major features in the model for the optimal application of bioreactors. Examples of this very interesting, albeit highly complex, application of machine learning for metabolism modeling will be the primary focus of this review presenting several different types of applications for model optimization, parameter determination or system analysis using models, as well as the utilization of several different types of machine learning technologies.
Indexed as
Identifiers
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.