ReviewMolecules (Basel, Switzerland)2024
AI-Assisted Rational Design and Activity Prediction of Biological Elements for Optimizing Transcription-Factor-Based Biosensors.
Review in Molecules (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed.
- An advanced LLM-powered article extraction pipeline delivers a database of buffers for protein studies and protein expression conditions.Briefings in bioinformatics · 2026Article
- Programming Next-Generation Synthetic Biosensors by Genetic Circuit Design.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- High-throughput yeast engineering in biofoundries: towards autonomous and scalable synthetic biology.FEMS yeast research · 2026Review
- Artificial Intelligence-Assisted Nanosensors for Clinical Diagnostics: Current Advances and Future Prospects.Biosensors · 2025Review
- Challenges and Opportunities in Smart Biosensing for Biomanufacturing.ACS synthetic biology · 2025Review
- Advancing lignocellulosic conversion though biosensor-enabled metabolic engineering.Green chemistry : an international journal and green chemistry resource : GC · 2025Review
- Microbial Transcription Factor-Based Biosensors: Innovations from Design to Applications in Synthetic Biology.Biosensors · 2025Review
- Biological Switches: Past and Future Milestones of Transcription Factor-Based Biosensors.ACS synthetic biology · 2025Review
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.
Funding
Abstract
The rational design, activity prediction, and adaptive application of biological elements (bio-elements) are crucial research fields in synthetic biology. Currently, a major challenge in the field is efficiently designing desired bio-elements and accurately predicting their activity using vast datasets. The advancement of artificial intelligence (AI) technology has enabled machine learning and deep learning algorithms to excel in uncovering patterns in bio-element data and predicting their performance. This review explores the application of AI algorithms in the rational design of bio-elements, activity prediction, and the regulation of transcription-factor-based biosensor response performance using AI-designed elements. We discuss the advantages, adaptability, and biological challenges addressed by the AI algorithms in various applications, highlighting their powerful potential in analyzing biological data. Furthermore, we propose innovative solutions to the challenges faced by AI algorithms in the field and suggest future research directions. By consolidating current research and demonstrating the practical applications and future potential of AI in synthetic biology, this review provides valuable insights for advancing both academic research and practical applications in biotechnology.
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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.