ArticleAdvances in nutrition (Bethesda, Md.)2023
Perspective: Multiomics and Machine Learning Help Unleash the Alternative Food Potential of Microalgae.
Article in Advances in nutrition (Bethesda, Md.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed, 50 citations in OpenAlex.
- Predictive Algal Systems Biology: Integrating Omics, Genome-Scale Metabolic Models, and Machine Learning.Bioengineering (Basel, Switzerland) · 2026Review
- Artificial Intelligence in Advancing Algal Bioactive Ingredients: Production, Characterization, and Application.Foods (Basel, Switzerland) · 2025Review
- Harnessing Artificial Intelligence to Revolutionize Microalgae Biotechnology: Unlocking Sustainable Solutions for High-Value Compounds and Carbon Neutrality.Marine drugs · 2025Review
- Natural color biofactories: advancing the spectrum of pigment production in phototrophic microbes.FEMS microbes · 2025Review
- Machine learning: an advancement in biochemical engineering.Biotechnology letters · 2024Review
- Microbial Pigments: Major Groups and Industrial Applications.Microorganisms · 2023Review
- Significance of Fermentation in Plant-Based Meat Analogs: A Critical Review of Nutrition, and Safety-Related Aspects.Foods (Basel, Switzerland) · 2023Review
- The microbial food revolution.Nature communications · 2023Review
- The potentials and challenges of using fermentation to improve the sensory quality of plant-based meat analogs.Frontiers in microbiology · 2023Review
- Use of industry 4.0 technologies to reduce and valorize seafood waste and by-products: A narrative review on current knowledge.Current research in food science · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Food security has become a pressing issue in the modern world. The ever-increasing world population, ongoing COVID-19 pandemic, and political conflicts together with climate change issues make the problem very challenging. Therefore, fundamental changes to the current food system and new sources of alternative food are required. Recently, the exploration of alternative food sources has been supported by numerous governmental and research organizations, as well as by small and large commercial ventures. Microalgae are gaining momentum as an effective source of alternative laboratory-based nutritional proteins as they are easy to grow under variable environmental conditions, with the added advantage of absorbing carbon dioxide. Despite their attractiveness, the utilization of microalgae faces several practical limitations. Here, we discuss both the potential and challenges of microalgae in food sustainability and their possible long-term contribution to the circular economy of converting food waste into feed via modern methods. We also argue that systems biology and artificial intelligence can play a role in overcoming some of the challenges and limitations; through data-guided metabolic flux optimization, and by systematically increasing the growth of the microalgae strains without negative outcomes, such as toxicity. This requires microalgae databases rich in omics data and further developments on its mining and analytics methods.
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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.