ReviewGEN biotechnology2022
Deep Learning Concepts and Applications for Synthetic Biology.
Review in GEN biotechnology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- Critical review of artificial intelligence in synthetic biology: DBTL cycle applications, challenges, and design rules for microbial strain engineering.World journal of microbiology & biotechnology · 2026Review
- Opportunities for artificial intelligence and synthetic biology in designing living drug delivery systems.Advanced drug delivery reviews · 2026Review
- Article
- Design biology and mind-body health.Journal, genetic engineering & biotechnology · 2025Review
- Advancing cell therapies with artificial intelligence and synthetic biology.Current opinion in biomedical engineering · 2025Article
- Deep learning in template-free de novo biosynthetic pathway design of natural products.Briefings in bioinformatics · 2024Review
- Design of fully synthetic signal peptide library and its use for enhanced secretory production of recombinant proteins in Corynebacterium glutamicum.Microbial cell factories · 2024Article
- Deep model predictive control of gene expression in thousands of single cells.Nature communications · 2024Article
- Data hazards in synthetic biology.Synthetic biology (Oxford, England) · 2024Review
- The whack-a-mole governance challenge for AI-enabled synthetic biology: literature review and emerging frameworks.Frontiers in bioengineering and biotechnology · 2024Review
- Opportunities for engineering outer membrane vesicles using synthetic biology approaches.Extracellular vesicles and circulating nucleic acids · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Synthetic biology has a natural synergy with deep learning. It can be used to generate large data sets to train models, for example by using DNA synthesis, and deep learning models can be used to inform design, such as by generating novel parts or suggesting optimal experiments to conduct. Recently, research at the interface of engineering biology and deep learning has highlighted this potential through successes including the design of novel biological parts, protein structure prediction, automated analysis of microscopy data, optimal experimental design, and biomolecular implementations of artificial neural networks. In this review, we present an overview of synthetic biology-relevant classes of data and deep learning architectures. We also highlight emerging studies in synthetic biology that capitalize on deep learning to enable novel understanding and design, and discuss challenges and future opportunities in this space.
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.