ArticlemSystems2025
Combining diffusion and transformer models for enhanced promoter synthesis and strength prediction in deep learning.
Article in mSystems, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
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Who cites it
5 citing papers in PubMed.
- UnconditionalSynthetic and systems biotechnology · 2026Article
- A set of constitutive promoters with graded strengths for gene expression in diverse cyanobacterial strains.Applied and environmental microbiology · 2026Article
- Systems-Level Metabolic Regulation Strategies of Xylose Metabolism in Saccharomyces cerevisiae.Biotechnology journal · 2026Review
- Synthetic Biology Strategies for Activating Cryptic BGCs inACS omega · 2026Review
- Deep Generative Model-Driven Design of Microbial Synthetic Promoters.Journal of microbiology and biotechnology · 2025Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
In the field of synthetic biology, the engineering of synthetic promoters that outperform their natural counterparts is of paramount importance, which can optimize the expression of exogenous genes, enhance the efficiency of metabolic pathways, and possess substantial commercial value. Research indicates that some synthetic promoters have higher transcriptional activity compared to strong natural promoters. However, with the exponential increase in complexity due to the 4 IMPORTANCE: We demonstrated that diffusion models are superior in accomplishing the promoter synthesis task compared to other state-of-the-art deep learning models. The effectiveness of our method was validated using data sets of
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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 Socratic graph.