ArticleComputational and structural biotechnology journal2023
Predicting metabolic fluxes from omics data via machine learning: Moving from knowledge-driven towards data-driven approaches.
Article in Computational and structural biotechnology journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Upcycling Vegetable Waste Into Functional Food Ingredients via Synergistic Microbial Engineering and Artificial Intelligence.Comprehensive reviews in food science and food safety · 2026Review
- AI-Assisted Spatial Metabolic Engineering in Plants: Integrating Flux Design, Spatial Omics, and Synthetic Biology.Metabolites · 2026Review
- Machine learning for extracellular vesicles enables diagnostic and therapeutic nanobiotechnology.Journal of nanobiotechnology · 2026Review
- A fusion-based multiomics classification approach for enhanced gene discovery in non-small cell lung cancer.Bioinformatics advances · 2026Article
- Machine Learning Empowering Microbial Cell Factory: A Comprehensive Review.Applied biochemistry and biotechnology · 2025Review
- Wheels turning: CHO cell modeling moves into a digital biomanufacturing era: Subtitle: CHO Metabolic Modeling.Computational and structural biotechnology journal · 2025Review
- RBI: a novel algorithm for regulatory-metabolic network model in designing the optimal mutant strain.PeerJ. Computer science · 2025Article
- MINN: A metabolic-informed neural network for integrating omics data into genome-scale metabolic modeling.Computational and structural biotechnology journal · 2025Article
- Metabolic Fluxes Using Deep Learning Based on Enzyme Variations:International journal of molecular sciences · 2024Article
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Authors and funding
3 authors.
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Abstract
The accurate prediction of phenotypes in microorganisms is a main challenge for systems biology. Genome-scale models (GEMs) are a widely used mathematical formalism for predicting metabolic fluxes using constraint-based modeling methods such as flux balance analysis (FBA). However, they require prior knowledge of the metabolic network of an organism and appropriate objective functions, often hampering the prediction of metabolic fluxes under different conditions. Moreover, the integration of omics data to improve the accuracy of phenotype predictions in different physiological states is still in its infancy. Here, we present a novel approach for predicting fluxes under various conditions. We explore the use of supervised machine learning (ML) models using transcriptomics and/or proteomics data and compare their performance against the standard parsimonious FBA (pFBA) approach using case studies of
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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.