Single-molecule research techniques such as patch-clamp electrophysiology deliver unique biological insight by capturing the movement of individual proteins in real time, unobscured by whole-cell ensemble averaging. The critical first step in analysis is event detection, so called "idealisation", where noisy raw data are turned into discrete records of protein movement. To date there have been practical limitations in patch-clamp data idealisation; high quality idealisation is typically laborious and becomes infeasible and subjective with complex biological data containing many distinct native single-ion channel proteins gating simultaneously. Here, we show a deep learning model based on convolutional neural networks and long short-term memory architecture can automatically idealise complex single molecule activity more accurately and faster than traditional methods. There are no parameters to set; baseline, channel amplitude or numbers of channels for example. We believe this approach could revolutionise the unsupervised automatic detection of single-molecule transition events in the future.

中文翻译：

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Deep-Channel 使用深度神经网络从膜片钳数据中检测单分子事件。

膜片钳电生理学等单分子研究技术通过实时捕捉单个蛋白质的运动提供独特的生物学见解，不受全细胞整体平均的影响。分析中关键的第一步是事件检测，即所谓的“理想化”，其中嘈杂的原始数据被转化为蛋白质运动的离散记录。迄今为止，膜片钳数据理想化存在实际限制；高质量的理想化通常是费力的，并且对于包含许多不同的天然单离子通道蛋白同时门控的复杂生物数据变得不可行和主观。这里，我们展示了一个基于卷积神经网络和长短期记忆架构的深度学习模型，可以比传统方法更准确、更快地自动理想化复杂的单分子活动。没有要设置的参数；例如基线、通道振幅或通道数。我们相信这种方法可以彻底改变未来单分子转换事件的无监督自动检测。