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English(EN) Multi-fidelity batch Bayesian optimization for bioprocess development across scales

新的贝叶斯优化框架加速生物工艺开发

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种新的多保真度批次贝叶斯优化框架，旨在加速生物工艺开发并降低实验成本。该方法集成了高斯过程用于多保真度建模和混合变量优化，使其不仅能够提出实验条件，还能提出合适的规模和生物催化剂选择。该框架在模拟的中国仓鼠卵巢生物工艺上进行了测试，与传统的工业实验设计基线相比，实验成本有所降低，产量有所提高。 AI

影响 这种新颖的优化方法有望带来更高效、更具成本效益的生物技术产品开发。

排序理由 该集群包含一篇详细介绍新方法的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv stat.ML 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的贝叶斯优化框架加速生物工艺开发

本文如何被排名

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33 / 100
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Tool
该集群包含一篇详细介绍新方法的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]
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报道来源 [1]

  1. arXiv stat.ML TIER_1 English(EN) · Adrian Martens, Mathias Neufang, Alessandro Butt\'e, Moritz von Stosch, Antonio del Rio Chanona, Laura Marie Helleckes ·

    面向生物工艺开发的多保真度批量贝叶斯优化

    arXiv:2508.10970v2 Announce Type: replace-cross Abstract: Bioprocesses are central to modern biotechnology, enabling sustainable production of pharmaceuticals, specialty chemicals, cosmetics, and food. However, developing high-performing processes remains costly and complex, requ…

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