ReviewComputational and structural biotechnology journal2020
The era of big data: Genome-scale modelling meets machine learning.
Review in Computational and structural biotechnology journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 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
34 citing papers in PubMed.
- Bacterial consortium construction for inhibition ofFrontiers in microbiology · 2026Review
- Machine learning and metabolic modeling-based identification of hypoxia-driven metabolic signatures in pediatric cancers.Frontiers in pharmacology · 2026Article
- Multiobjective Design of Growth Media with Genome-Scale Metabolic Models and Bayesian Optimization.Computational and structural biotechnology journal · 2026Article
- Review
- RSEA: A Web Server for Pathway Enrichment Analysis of Metabolic Reaction Sets.Biotechnology and bioengineering · 2025Article
- Reconstruction of a Genome-Scale Metabolic Model forInternational journal of molecular sciences · 2025Article
- Fermentation design and process optimization strategy based on machine learning.Biodesign research · 2025Review
- Soil biodiversity and function under global change.PLoS biology · 2025Review
- Wheels turning: CHO cell modeling moves into a digital biomanufacturing era: Subtitle: CHO Metabolic Modeling.Computational and structural biotechnology journal · 2025Review
- Decoding sex differences in human immunity through systems immunology.Oxford open immunology · 2025Review
- Population health management fit lifecycles in analytics.Frontiers in artificial intelligence · 2025Article
- MINN: A metabolic-informed neural network for integrating omics data into genome-scale metabolic modeling.Computational and structural biotechnology journal · 2025Article
- A diel multi-tissue genome-scale metabolic model of Vitis vinifera.PLoS computational biology · 2024Article
- Integration of graph neural networks and genome-scale metabolic models for predicting gene essentiality.NPJ systems biology and applications · 2024Article
- Machine learning identifies key metabolic reactions in bacterial growth on different carbon sources.Molecular systems biology · 2024Article
- Development and applications of metabolic models in plant multi-omics research.Frontiers in plant science · 2024Review
- Metabolomic-Based Approaches for Endometrial Cancer Diagnosis and Prognosis: A Review.Cancers · 2023Review
- From observational to actionable: rethinking omics in biologics production.Trends in biotechnology · 2023Review
- Recent advances in data- and knowledge-driven approaches to explore primary microbial metabolism.Current opinion in chemical biology · 2023Review
- Integrating Omics Data in Genome-Scale Metabolic Modeling: A Methodological Perspective for Precision Medicine.Metabolites · 2023Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With omics data being generated at an unprecedented rate, genome-scale modelling has become pivotal in its organisation and analysis. However, machine learning methods have been gaining ground in cases where knowledge is insufficient to represent the mechanisms underlying such data or as a means for data curation prior to attempting mechanistic modelling. We discuss the latest advances in genome-scale modelling and the development of optimisation algorithms for network and error reduction, intracellular constraining and applications to strain design. We further review applications of supervised and unsupervised machine learning methods to omics datasets from microbial and mammalian cell systems and present efforts to harness the potential of both modelling approaches through hybrid modelling.
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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.