用机器学习预测CRISPR编辑的脱靶效应，准确率达84%。
Guide-Guard: Off-Target Predicting in CRISPR Applications
- 基于数据驱动方法构建机器学习模型，预测gRNA在编辑中的脱靶行为。
- 模型在多基因同时训练下仍保持84%准确率。
- 适合基因编辑安全评估、药物研发与临床前研究者使用。
随着网络物理基因组测序与编辑技术（如CRISPR）的引入，研究人员能更便捷地探索并开发遗传学与生命科学领域（如农业和医学）的解决方案。随着该领域发展，预测脱靶效应的能力成为新挑战。本文从数据驱动角度探究其潜在生物化学机制，并提出名为Guide-Guard的机器学习方案，可基于gRNA预测CRISPR基因编辑过程中的系统行为，准确率达到84%。该模型可在同一时间对多个不同基因进行训练，且维持高精度。
原文摘要 · Abstract (English)
With the introduction of cyber-physical genome sequencing and editing technologies, such as CRISPR, researchers can more easily access tools to investigate and create remedies for a variety of topics in genetics and health science (e.g. agriculture and medicine). As the field advances and grows, new concerns present themselves in the ability to predict the off-target behavior. In this work, we explore the underlying biological and chemical model from a data driven perspective. Additionally, we present a machine learning based solution named \textit{Guide-Guard} to predict the behavior of the system given a gRNA in the CRISPR gene-editing process with 84\% accuracy. This solution is able to be trained on multiple different genes at the same time while retaining accuracy.
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