ArticleACS synthetic biology2024
Machine Learning-Guided Optimization of
Article in ACS synthetic biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed, 27 citations in OpenAlex.
- Toward Rational Design of Precision-Fermented Milk Proteins: Integrating Cross-Species Selection, Post-Translational Modification, and AI Optimization.Comprehensive reviews in food science and food safety · 2026Review
- Integrating Machine Learning and Transcriptomics to Enhance β‑Carotene Production inACS omega · 2026Article
- Machine-Learning-Assisted Pathway Optimization in Large Combinatorial Design Spaces: A p-Coumaric Acid Case Study.ACS synthetic biology · 2026Article
- Metabolic Engineering Strategies for Flavonoid Biosynthesis in Saccharomyces cerevisiae: Current Advances and Future Perspectives.Biotechnology and bioengineering · 2026Review
- Synthesis of Amines for Active Pharmaceutical Ingredients Using the Whole-Cell Factory Saccharomyces Cerevisae.Chembiochem : a European journal of chemical biology · 2026Review
- Rhodo-Box: A Synthetic Biology Toolbox to Facilitate Metabolic Engineering ofACS synthetic biology · 2026Article
- Yeast Stress Response to Synthetic Constructs.ACS synthetic biology · 2026Review
- Comparing metabolic engineering scenarios using simulated design-build-test-learn-cycles.Frontiers in bioengineering and biotechnology · 2026Article
- Leveraging artificial intelligence for efficient microbial production.Bioresource technology · 2026Review
- Multiscale metabolic engineering in biological lignin valorization.Innovation (Cambridge (Mass.)) · 2025Review
- Unraveling flavor formation inFood chemistry: X · 2025Review
- Recent advances of muconic acid production using microbial synthetic biology.Archives of microbiology · 2025Review
- Machine Learning Empowering Microbial Cell Factory: A Comprehensive Review.Applied biochemistry and biotechnology · 2025Review
- Sucrose-driven carbon redox rebalancing eliminates the Crabtree effect and boosts energy metabolism in yeast.Nature communications · 2025Article
- Fermentation design and process optimization strategy based on machine learning.Biodesign research · 2025Review
- Integrating yeast biodiversity and machine learning for predictive metabolic engineering.FEMS yeast research · 2025Review
- Recent trends in the elucidation of complex triterpene biosynthetic pathways in horticultural trees.Horticulture research · 2025Article
- Combinatorial optimization of pathway, process and media for the production of p-coumaric acid by Saccharomyces cerevisiae.Microbial biotechnology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Industrial biotechnology uses Design-Build-Test-Learn (DBTL) cycles to accelerate the development of microbial cell factories, required for the transition to a biobased economy. To use them effectively, appropriate connections between the phases of the cycle are crucial. Using
Indexed as
Identifiers
What OpenQuestion holds
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.