ArticleFrontiers in microbiology2023
EVMP: enhancing machine learning models for synthetic promoter strength prediction by Extended Vision Mutant Priority framework.
Article in Frontiers in microbiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Rapid growth phenotype of carbapenem-resistant Enterobacter cloacae: growth fitness, stability, and mechanistic insights.BMC genomics · 2026Article
- Synthetic promoter design in plants: integration of computational and experimental approaches.Frontiers in plant science · 2026Review
- Development of a semi-endogenous PRT-based expression system for Methylotuvimicrobium alcaliphilum and its application to enhance 3-hydroxybutyric acid production from methanol.Journal of biological engineering · 2025Review
- Deep learning guided programmable design of Escherichia coli core promoters from sequence architecture to strength control.Nucleic acids research · 2025Article
- Artificial Intelligence-Based Approaches for AAV Vector Engineering.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Exploring the Promoter Generation and Prediction ofInternational journal of molecular sciences · 2024Article
- CAPE: a deep learning framework with Chaos-Attention net for Promoter Evolution.Briefings in bioinformatics · 2024Article
- Fine-Tuning Gene Expression in Bacteria by Synthetic Promoters.Methods in molecular biology (Clifton, N.J.) · 2024Article
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Authors and funding
3 authors.
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Abstract
Introduction: In metabolic engineering and synthetic biology applications, promoters with appropriate strengths are critical. However, it is time-consuming and laborious to annotate promoter strength by experiments. Nowadays, constructing mutation-based synthetic promoter libraries that span multiple orders of magnitude of promoter strength is receiving increasing attention. A number of machine learning (ML) methods are applied to synthetic promoter strength prediction, but existing models are limited by the excessive proximity between synthetic promoters. Methods: In order to enhance ML models to better predict the synthetic promoter strength, we propose EVMP(Extended Vision Mutant Priority), a universal framework which utilize mutation information more effectively. In EVMP, synthetic promoters are equivalently transformed into base promoter and corresponding Results: In Trc synthetic promoter library, EVMP was applied to multiple ML models and the model effect was enhanced to varying extents, up to 61.30% (MAE), while the SOTA(state-of-the-art) record was improved by 15.25% (MAE) and 4.03% ( Discussion: In further study, extended vision (or
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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.