College of Resources and Environmental Engineering, Guizhou University, Guiyang, 550025, China.
Key Laboratory of Karst Georesources and Environment, Ministry of Education, Guiyang, 550025, China.
Sci Rep. 2024 Sep 20;14(1):21966. doi: 10.1038/s41598-024-73140-1.
DNA cleavage by nanomaterials has the potential to be utilized as an innovative tool for gene editing. Numerous nanomaterials exhibiting DNA cleavage properties have been identified and cataloged. Yet, the exploitation of property data through data-driven machine-learning approaches remains unexplored. A database was developed, compiling thirty distinctive characteristics, encompassing physical and chemical properties, as well as experimental conditions of nanomaterials that have demonstrated DNA cleavage capability such as in articles published over the past two decades. The DNA cleavage effect and efficiency of nanomaterials were predicted using machine learning algorithms such as support vector machines, deep neural networks, and random forest, and a classification accuracy of 0.93 for the cleavage effect was achieved. Moreover, the potential of utilizing larger datasets to enhance the predictive capacity of models was discussed. The findings indicate the feasibility of predicting nanomaterial properties based on experimental data. Evaluating the performance and effectiveness of the machine learning models trained using the existing data can furnish valuable insights for future materials research endeavors, especially for the design of DNA cleavage with specific sites.
纳米材料的 DNA 切割具有作为基因编辑的创新工具的潜力。已经鉴定和分类了许多具有 DNA 切割特性的纳米材料。然而，通过数据驱动的机器学习方法利用特性数据的方法仍未得到探索。开发了一个数据库，其中包含了三十个独特的特性，包括物理和化学特性以及实验条件，这些特性表明纳米材料具有 DNA 切割能力，例如在过去二十年中发表的文章中。使用机器学习算法（如支持向量机、深度神经网络和随机森林）预测纳米材料的 DNA 切割效果和效率，并实现了 0.93 的切割效果分类准确性。此外，还讨论了利用更大的数据集来增强模型预测能力的潜力。研究结果表明，基于实验数据预测纳米材料特性是可行的。评估使用现有数据训练的机器学习模型的性能和效果，可以为未来的材料研究工作提供有价值的见解，特别是对于设计具有特定位点的 DNA 切割。
