通过对纳米孔转位实验的统计分析进行多肽分类

Julian Hoßbach, Samuel Tovey, Tobias Ensslen, Jan C. Behrends, Christian Holm
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引用次数: 0

摘要

使用基于纳米孔的设备进行蛋白质表征有望成为基础研究、诊断和分析领域的突破性方法。目前的研究包括使用机器学习来完成这项任务。在这项工作中,对纳米孔电流信号进行了全面的统计分析,结果表明足以对多达 42 种肽进行分类,准确率高达 70%。我们比较了两组特征,即统计矩(statistical moments)和捕获集(catch22 set),这两组特征的表现形式和训练小型分类器神经网络后的结果。我们证明,catch22 集和中心矩捕捉到的事件复杂特征是对具有相似平均电流的肽进行分类的关键。这些结果凸显了对纳米孔数据进行纯统计分析的功效,并为更复杂的分类技术指明了前进的道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Peptide Classification from Statistical Analysis of Nanopore Translocation Experiments
Protein characterization using nanopore-based devices promises to be a breakthrough method in basic research, diagnostics, and analytics. Current research includes the use of machine learning to achieve this task. In this work, a comprehensive statistical analysis of nanopore current signals is performed and demonstrated to be sufficient for classifying up to 42 peptides with 70 % accuracy. Two sets of features, the statistical moments and the catch22 set, are compared both in their representations and after training small classifier neural networks. We demonstrate that complex features of the events, captured in both the catch22 set and the central moments, are key in classifying peptides with otherwise similar mean currents. These results highlight the efficacy of purely statistical analysis of nanopore data and suggest a path forward for more sophisticated classification techniques.
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