Machine Learning Routines for Nanopore Sensing

纳米孔技术正被成功地应用于核酸的测序，然而，分析异质生物分子样本仍然具有挑战性。对纳米孔传感器产生的数据集的分析依赖于一个信号处理链，该链使用先进的算法将移位事件分类为通过纳米孔的特定分析物。通常，复杂分析物混合物的特征太复杂，不能用目前的数据分析协议进行高精度的分析。该项目将开发一种适合用途的数据分析方法，以实现对高度复杂的纳米孔数据集的高精度实时分析。虽然一些特征可以很容易地使用分析工具(如峰值幅度和停留时间)来量化，但其他特征(如形状)更具挑战性，将更适合于使用机器学习方法进行分析。该项目将采用机器学习方法，对使用纳米孔和功能DNA折纸生成的单分子生物传感数据集进行分类和分类，以支持下一代医疗诊断设备的开发。该项目还将涉及开发催化纳米颗粒系统的多模式表征，其中纳米孔传感将与单一实体层面的电化学表征相辅相成。该项目将实施传感器融合算法，以产生改进的信号分类，从而为低碳未来开发和表征新材料。

Nanopore technology is being successfully deployed for the sequencing of nucleic acids however it is still challenging to analyse heterogenous biomolecular samples. The analysis of datasets generated from nanopore sensors relies on a signal processing chain that employs advanced algorithms to classify translocation events to specific analytes passing through the nanopore. Often, the signatures of complex analyte mixtures are too convoluted to be analysed with high precision with current data analysis protocols. This project will develop of a fit-for-purpose data analytics approach to enable high-precision real-time analysis of highly convoluted nanopore datasets. While some features can readily be quantified using analytical tools (such as peak amplitude and dwell time), others (such as shape) are more challenging and will be better suited for analysis with machine learning approaches. The project will implement machine learning approaches for the clustering and classification of single molecule biosensing datasets generated using nanopores and functional DNA origami to support the development of the next generation of medical diagnostic devices.The project will also involve the development of multimodal characterization of catalytic nanoparticle systems where nanopore sensing will be complemented with electrochemical characterization at the single entity level. The project will implement sensor fusion algorithms to generate improved signal classification that will allow the development and characterization of new materials for a low-carbon future.