Article: EVMP: enhancing machine learning models for synthetic promoter strength prediction by Extended Vision Mutant Priority framework.
2023 Volume 14, Page(s) 1215609
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 ...
| 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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| Language | English |
| Publishing date | 2023-07-05 |
| Publishing country | Switzerland |
| Document type | Journal Article |
| ZDB-ID | 2587354-4 |
| ISSN | 1664-302X |
| ISSN | 1664-302X |
| DOI | 10.3389/fmicb.2023.1215609 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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