ReviewComputational and structural biotechnology journal2023
Redesigning plant specialized metabolism with supervised machine learning using publicly available reactome data.
Review 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 4 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.
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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
4 citing papers in PubMed.
- Constructing gene co-functional and co-regulatory networks from public transcriptomes using condition-specific ensemble co-expression.Nature communications · 2026Article
- Advances in natural product discovery: strategies, technologies, and insights.Natural products and bioprospecting · 2026Review
- AI-driven integration and optimization of medicinal plant multi-omics metabolic networks.Frontiers in plant science · 2026Review
- Evolution of plant metabolism: the state-of-the-art.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The immense structural diversity of products and intermediates of plant specialized metabolism (specialized metabolites) makes them rich sources of therapeutic medicine, nutrients, and other useful materials. With the rapid accumulation of reactome data that can be accessible on biological and chemical databases, along with recent advances in machine learning, this review sets out to outline how supervised machine learning can be used to design new compounds and pathways by exploiting the wealth of said data. We will first examine the various sources from which reactome data can be obtained, followed by explaining the different machine learning encoding methods for reactome data. We then discuss current supervised machine learning developments that can be employed in various aspects to help redesign plant specialized metabolism.
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Registered trials
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