用小波变换将纳米孔电流信号转为图像，实现81%肽类分类准确率。
Deep Learning-Driven Peptide Classification in Biological Nanopores
- 将电流信号转为尺度图，融合时频幅信息提升可读性
- 42种肽类分类准确率达81%，创该领域新纪录
- 支持模型迁移，适合部署到临床实时诊断设备
能实现实时肽类蛋白分类的设备，有望在临床环境中实现低成本、快速疾病诊断。纳米孔器件是其中一种候选技术，通过测量肽或蛋白进入纳米级孔道时产生的电流信号进行识别。若该电流与肽结构及其与孔道相互作用具有唯一对应关系，则可实现精准鉴定。然而，当前信号复杂性限制了识别精度。本文通过小波变换将电流信号转换为尺度图，有效捕捉振幅、频率和时间信息，使其更适于机器学习算法处理。在42种肽类样本上测试，分类准确率达到约81%，创下该领域新高，推动了即时诊疗中肽/蛋白检测的实际应用。此外，我们还展示了模型迁移技术，对实际硬件部署至关重要，为实时疾病诊断提供了新路径。
原文摘要 · Abstract (English)
A device capable of performing real time classification of proteins in a clinical setting would allow for inexpensive and rapid disease diagnosis. One such candidate for this technology are nanopore devices. These devices work by measuring a current signal that arises when a protein or peptide enters a nanometer-length-scale pore. Should this current be uniquely related to the structure of the peptide and its interactions with the pore, the signals can be used to perform identification. While such a method would allow for real time identification of peptides and proteins in a clinical setting, to date, the complexities of these signals limit their accuracy. In this work, we tackle the issue of classification by converting the current signals into scaleogram images via wavelet transforms, capturing amplitude, frequency, and time information in a modality well-suited to machine learning algorithms. When tested on 42 peptides, our method achieved a classification accuracy of ~$81\,\%$, setting a new state-of-the-art in the field and taking a step toward practical peptide/protein diagnostics at the point of care. In addition, we demonstrate model transfer techniques that will be critical when deploying these models into real hardware, paving the way to a new method for real-time disease diagnosis.
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