用多模态模型提升纳米孔信号识别准确率，突破现有方法10个百分点以上。
Multi-modal transformer for signal classification in nanopore blockade experiments
- 融合原始时序、小波图像和特征向量三类信号输入
- 在42肽数据集上准确率超现有方法10%以上，20氨基酸数据集接近完美
- 适合需要高精度分子识别的生物传感研究者
纳米孔器件已成为单分子检测的强大工具，可用于快速、便携的诊断。通过监测分析物进入纳米级孔道时引起的离子电流变化，可依据特征信号模式识别多种生物标志物。然而，这些信号极为复杂，可靠地将其与特定分子关联仍是重大挑战。本文提出一种多模态深度学习架构，联合处理原始时间序列数据、小波基图像及静态特征向量。该方法在42肽基准测试中准确率超过现有方法10个百分点以上，并在20氨基酸数据集上实现近完美识别。注意力分析显示，时序与小波图像输入关注同一事件的不同特征。结果表明，机器学习可显著提升纳米孔传感器的分子识别鲁棒性与准确性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。