ReviewFrontiers in molecular biosciences2021
Review of Machine Learning Methods for the Prediction and Reconstruction of Metabolic Pathways.
Review in Frontiers in molecular biosciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- LeGenD: High-throughput N-glycan profiling using explainable AI and lectin profiling.The Journal of biological chemistry · 2026Article
- Probiotics and Postbiotics in Life-Style Disease Management: A Comprehensive Review on the Technologies in the Era of Omics and Artificial Intelligence.Probiotics and antimicrobial proteins · 2026Review
- Generative design of synthetic gene circuits for functional and evolutionary properties.NPJ systems biology and applications · 2026Article
- Predicting the pathway involvement of metabolites annotated in the MetaCyc knowledgebase.BMC bioinformatics · 2026Article
- A review of flavonoids at the crossroads of plant defense: integrating biotic and abiotic stress tolerance through AI- and CRISPR/Cas-guided metabolic reprogramming.Frontiers in plant science · 2026Review
- Metabolite Identification Data in Drug Discovery, Part 1: Data Generation and Trend Analysis.Molecular pharmaceutics · 2025Article
- From data to discovery: leveraging big data in plant natural products biosynthesis research.The Plant journal : for cell and molecular biology · 2025Review
- Navigating the challenges of engineering composite specialized metabolite pathways in plants.The Plant journal : for cell and molecular biology · 2025Review
- Metadag: a web tool to generate and analyse metabolic networks.BMC bioinformatics · 2025Article
- Unlocking the potential of flavonoid biosynthesis through integrated metabolic engineering.Frontiers in plant science · 2025Review
- Classifying Dry Eye Disease Patients from Healthy Controls Using Machine Learning and Metabolomics Data.Diagnostics (Basel, Switzerland) · 2024Article
- Conformal novelty detection for multiple metabolic networks.BMC bioinformatics · 2024Article
- Revolutionizing dairy waste: emerging solutions in conjunction with microbial engineering.Biodegradation · 2024Review
- Review
- LeGenD: determining N-glycoprofiles using an explainable AI-leveraged model with lectin profiling.bioRxiv : the preprint server for biology · 2024Article
- Control Theory and Systems Biology: Potential Applications in Neurodegeneration and Search for Therapeutic Targets.International journal of molecular sciences · 2023Review
- Identification of Colon Immune Cell Marker Genes Using Machine Learning Methods.Life (Basel, Switzerland) · 2023Article
- Drug-disease association prediction with literature based multi-feature fusion.Frontiers in pharmacology · 2023Article
- Elucidating Human Milk Oligosaccharide biosynthetic genes through network-based multi-omics integration.Nature communications · 2022Article
- The future of Microbial Biotechnology.Microbial biotechnology · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Prediction and reconstruction of metabolic pathways play significant roles in many fields such as genetic engineering, metabolic engineering, drug discovery, and are becoming the most active research topics in synthetic biology. With the increase of related data and with the development of machine learning techniques, there have many machine leaning based methods been proposed for prediction or reconstruction of metabolic pathways. Machine learning techniques are showing state-of-the-art performance to handle the rapidly increasing volume of data in synthetic biology. To support researchers in this field, we briefly review the research progress of metabolic pathway reconstruction and prediction based on machine learning. Some challenging issues in the reconstruction of metabolic pathways are also discussed in this paper.
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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.