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Gradient-based optimization for mRNA sequence design
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DOI：10.1093/bioinformatics/btag667.png)
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Abstract
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Designing mRNA coding sequences that simultaneously optimize RNA accessibility in the translation initiation region and codon adaptation while preserving the encoded protein requires navigating a vast discrete combinatorial space. The inherently discrete nature of codon choices prevents direct application of gradient-based optimization, despite the availability of accurate deep learning predictors such as DeepRaccess for RNA accessibility prediction.
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NATURE BIOTECHNOLOGY
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NATURE BIOTECHNOLOGYIF41.7
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