元学习可解决机器学习引导的抗体工程中的噪声和标记不足数据问题。

Cell systems Pub Date : 2024-01-17 Epub Date: 2024-01-08 DOI:10.1016/j.cels.2023.12.003
Mason Minot, Sai T Reddy
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引用次数: 0

摘要

以机器学习为指导的蛋白质工程学正在迅速发展;然而,收集高质量的大型数据集仍然是一个瓶颈。定向进化和蛋白质工程研究通常需要大量的实验过程来消除噪声和标记蛋白质序列功能数据。元学习在其他领域已被证明是有效的,它可以通过双层优化从噪声数据中学习,前提是要有一个带有可信标签的小型数据集。在这里,我们利用元学习方法来克服嘈杂和标记不足的数据,加快抗体工程的工作流程。我们生成酵母展示抗体诱变文库,并对其进行目标抗原结合筛选,然后进行深度测序。然后,我们创建了具有代表性的学习任务,包括从嘈杂的训练数据中学习、正向和非标记学习以及从分布属性中学习。我们证明,元学习有可能缩短实验筛选时间,并通过使用有噪声和未充分标记的训练数据来提高机器学习模型的鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Meta learning addresses noisy and under-labeled data in machine learning-guided antibody engineering.

Meta learning addresses noisy and under-labeled data in machine learning-guided antibody engineering.

Machine learning-guided protein engineering is rapidly progressing; however, collecting high-quality, large datasets remains a bottleneck. Directed evolution and protein engineering studies often require extensive experimental processes to eliminate noise and label protein sequence-function data. Meta learning has proven effective in other fields in learning from noisy data via bi-level optimization given the availability of a small dataset with trusted labels. Here, we leverage meta learning approaches to overcome noisy and under-labeled data and expedite workflows in antibody engineering. We generate yeast display antibody mutagenesis libraries and screen them for target antigen binding followed by deep sequencing. We then create representative learning tasks, including learning from noisy training data, positive and unlabeled learning, and learning out of distribution properties. We demonstrate that meta learning has the potential to reduce experimental screening time and improve the robustness of machine learning models by training with noisy and under-labeled training data.

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