ArticleBioinformatics (Oxford, England)2024
GPro: generative AI-empowered toolkit for promoter design.
Article in Bioinformatics (Oxford, England), 2024. 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- UnconditionalSynthetic and systems biotechnology · 2026Article
- DESIGN: a framework for generative AI-enabled crop molecular design-driven breeding.Science China. Life sciences · 2026Review
- Genetically encoded biosensors enabled high-throughput screening of microbial cell factories.Engineering microbiology · 2026Review
- Design prokaryotic cis-regulatory elements using language model.Nucleic acids research · 2026Article
- Synthetic promoter design in plants: integration of computational and experimental approaches.Frontiers in plant science · 2026Review
- Deep Generative Model-Driven Design of Microbial Synthetic Promoters.Journal of microbiology and biotechnology · 2025Review
- De novo design of insulated cis-regulatory elements based on deep learning-predicted fitness landscape.Nucleic acids research · 2025Article
- Review
- Synthetic promoter design iniScience · 2024Article
- Generating information-dense promoter sequences with optimal string packing.PLoS computational biology · 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
6 authors.
Funding
Abstract
motivationPromoters with desirable properties are crucial in biotechnological applications. Generative AI (GenAI) has demonstrated potential in creating novel synthetic promoters with significantly enhanced functionality. However, these methods' reliance on various programming frameworks and specific task-oriented contexts limits their flexibilities. Overcoming these limitations is essential for researchers to fully leverage the power of GenAI to design promoters for their tasks.
resultsHere, we introduce GPro (Generative AI-empowered toolkit for promoter design), a user-friendly toolkit that integrates a collection of cutting-edge GenAI-empowered approaches for promoter design. This toolkit provides a standardized pipeline covering essential promoter design processes, including training, optimization, and evaluation. Several detailed demos are provided to reproduce state-of-the-art promoter design pipelines. GPro's user-friendly interface makes it accessible to a wide range of users including non-AI experts. It also offers a variety of optional algorithms for each design process, and gives users the flexibility to compare methods and create customized pipelines. AVAILABILITY AND IMPLEMENTATION: GPro is released as an open-source software under the MIT license. The source code for GPro is available on GitHub for Linux, macOS, and Windows: https://github.com/WangLabTHU/GPro, and is available for download via Zenodo repository at https://zenodo.org/doi/10.5281/zenodo.10681733.
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.