AI 中文总结
针对纳米孔信号复杂难以分类的问题，引入多模态深度学习架构，联合处理多种信号表示，在42肽基准测试中大幅超越现有方法，转移到20氨基酸数据集也有高准确率，证明机器学习助力纳米孔传感器高精度分子识别的潜力。
AI 中文摘要
纳米孔装置已成为单分子传感的强大工具，具有快速、便携诊断的潜力。它们通过分析物进入纳米级孔时检测离子电流变化来识别生物标志物，但信号复杂，可靠分类是挑战。本文引入多模态深度学习架构，联合处理原始时间序列数据、基于小波的图像和静态特征向量等多种信号表示。该方法在42肽基准测试中比现有方法高出10多个百分点，并以近乎完美的准确率转移到20氨基酸数据集。模型整合了这些表示的互补信息，结果证明了机器学习在纳米孔传感器进行强大、高精度分子识别方面的潜力。
英文摘要
Nanopore devices have emerged as powerful tools for single-molecule sensing, with potential for rapid, portable diagnostics. They detect changes in ionic current as analytes enter nanometer-scale pores, providing a means of identifying diverse biomarkers from their characteristic signal patterns. However, these signals are highly complex, and reliably assigning them to specific molecules remains a major challenge. Here, we address this by introducing a multi-modal deep learning architecture that jointly processes multiple signal representations, including raw time-series data, wavelet-based images, and static feature vectors. Our approach surpasses existing methods by more than 10 percentage points on a 42-peptide benchmark and transfers to a 20-amino-acid dataset with near-perfect accuracy. The model integrates complementary information from these representations, with attention analysis showing that the time-series and wavelet-image inputs emphasize different features of the same event. Together, these results demonstrate the potential of machine learning to enable robust, high-accuracy molecular identification with nanopore sensors.
Comments22 pages (incl. references), 8 figures
