ArticleFrontiers in plant science2024
Machine learning-based prediction models unleash the enhanced production of fucoxanthin in
Article in Frontiers in plant science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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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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Who cites it
3 citing papers in PubMed.
- Predictive Algal Systems Biology: Integrating Omics, Genome-Scale Metabolic Models, and Machine Learning.Bioengineering (Basel, Switzerland) · 2026Review
- Unveiling astaxanthin: biotechnological advances, delivery systems and versatile applications in nutraceuticals and cosmetics.Archives of microbiology · 2025Review
- Production of Fucoxanthin from MicroalgaeMarine drugs · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
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Abstract
Introduction: The marine microalga Methods: A novel integrated experimental approach was developed, analyzing the effect of varying phytohormone types and concentrations on fucoxanthin production in Results: The findings revealed that the Random Forest (RF) model was highly significant with a high Discussion: This study highlights the potential of machine learning models, particularly Random Forest, to optimize parameters influencing microalgal growth and fucoxanthin production. This approach offers a more efficient alternative to conventional methods, providing valuable insights into improving fucoxanthin production in microalgal cultivation. The findings suggest that leveraging diverse ML models can enhance the predictability and efficiency of fucoxanthin production, making it a promising tool for industrial applications.
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