ArticleiScience2020
A Hybrid Flux Balance Analysis and Machine Learning Pipeline Elucidates Metabolic Adaptation in Cyanobacteria.
Article in iScience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 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.
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.
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Who cites it
24 citing papers in PubMed.
- Titanium Dioxide Nanoparticle-Driven Metabolic and Molecular Reprogramming in Cyanobacteria.Molecules (Basel, Switzerland) · 2026Review
- Myocardial Lipid Metabolism Imbalance: The Pathological Core and Novel Diagnostic-Therapeutic Directions of Cardiovascular Diseases.Journal of biochemical and molecular toxicology · 2026Review
- Cyanobacteria as a Photosynthetic Chassis for Metabolic Pathway Engineering with Heterologous Gene Expression.Current issues in molecular biology · 2026Review
- How Polyphenol Metabolites Spatiotemporally Reprogram Transcription Factors and Human Proteostasis: A Metabolite-Centric Framework.Current issues in molecular biology · 2026Review
- Enzyme-constrained genome-scale model of Yarrowia lipolytica predicts growth-phase specific metabolic engineering targets.Applied microbiology and biotechnology · 2026Article
- Evaluating transcriptomic integration for cyanobacterial constraint-based metabolic modelling.Frontiers in bioinformatics · 2026Article
- Development of an ethanol-based microbial platform for 3-hydroxypropionic acid production using engineered Pseudomonas putida KT2440.Journal of biological engineering · 2025Article
- Machine Learning and Deep Learning in Synthetic Biology: Key Architectures, Applications, and Challenges.ACS omega · 2024Review
- Machine learning methods for predicting essential metabolic genes from Plasmodium falciparum genome-scale metabolic network.PloS one · 2024Article
- Artificial intelligence and machine learning applications for cultured meat.Frontiers in artificial intelligence · 2024Review
- Environmental modulation of exopolysaccharide production in the cyanobacterium Synechocystis 6803.Applied microbiology and biotechnology · 2023Article
- Flux balance analysis-based metabolic modeling of microbial secondary metabolism: Current status and outlook.PLoS computational biology · 2023Review
- Glycosylation spectral signatures for glioma grade discrimination using Raman spectroscopy.BMC cancer · 2023Article
- Synthetic Biology Meets Machine Learning.Methods in molecular biology (Clifton, N.J.) · 2023Article
- SBML2HYB: a Python interface for SBML compatible hybrid modeling.Bioinformatics (Oxford, England) · 2023Article
- Predicting metabolic fluxes from omics data via machine learning: Moving from knowledge-driven towards data-driven approaches.Computational and structural biotechnology journal · 2023Article
- Genome-scale modeling of Chinese hamster ovary cells by hybrid semi-parametric flux balance analysis.Bioprocess and biosystems engineering · 2022Article
- Whole-genome sequencing and genome-scale metabolic modeling of Chromohalobacter canadensis 85B to explore its salt tolerance and biotechnological use.MicrobiologyOpen · 2022Article
- Optimization and Scale-Up of Fermentation Processes Driven by Models.Bioengineering (Basel, Switzerland) · 2022Review
- Transcriptomics Integrated with Metabolomics Reveals 2-Methoxy-1, 4-Naphthoquinone-Based Carbon Dots Induced Molecular Shifts inJournal of fungi (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning has recently emerged as a promising tool for inferring multi-omic relationships in biological systems. At the same time, genome-scale metabolic models (GSMMs) can be integrated with such multi-omic data to refine phenotypic predictions. In this work, we use a multi-omic machine learning pipeline to analyze a GSMM of
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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 OpenQuestion graph.