物尽其用:单分子传输研究的一类分类法

IF 4.8 Q2 NANOSCIENCE & NANOTECHNOLOGY
William Bro-Jørgensen, Joseph M. Hamill, Gréta Mezei, Brent Lawson, Umar Rashid, András Halbritter*, Maria Kamenetska*, Veerabhadrarao Kaliginedi* and Gemma C. Solomon*, 
{"title":"物尽其用:单分子传输研究的一类分类法","authors":"William Bro-Jørgensen,&nbsp;Joseph M. Hamill,&nbsp;Gréta Mezei,&nbsp;Brent Lawson,&nbsp;Umar Rashid,&nbsp;András Halbritter*,&nbsp;Maria Kamenetska*,&nbsp;Veerabhadrarao Kaliginedi* and Gemma C. Solomon*,&nbsp;","doi":"10.1021/acsnanoscienceau.4c0001510.1021/acsnanoscienceau.4c00015","DOIUrl":null,"url":null,"abstract":"<p >Single-molecule experiments offer a unique means to probe molecular properties of individual molecules–yet they rest upon the successful control of background noise and irrelevant signals. In single-molecule transport studies, large amounts of data that probe a wide range of physical and chemical behaviors are often generated. However, due to the stochasticity of these experiments, a substantial fraction of the data may consist of blank traces where no molecular signal is evident. One-class (OC) classification is a machine learning technique to identify a specific class in a data set that potentially consists of a wide variety of classes. Here, we examine the utility of two different types of OC classification models on four diverse data sets from three different laboratories. Two of these data sets were measured at cryogenic temperatures and two at room temperature. By training the models solely on traces from a blank experiment, we demonstrate the efficacy of OC classification as a powerful and reliable method for filtering out blank traces from a molecular experiment in all four data sets. On a labeled 4,4′-bipyridine data set measured at 4.2 K, we achieve an accuracy of 96.9 ± 0.3 and an area under the receiver operating characteristic curve of 99.5 ± 0.3 as validated over a fivefold cross-validation. Given the wide range of physical and chemical properties that can be probed in single-molecule experiments, the successful application of OC classification to filter out blank traces is a major step forward in our ability to understand and manipulate molecular properties.</p>","PeriodicalId":29799,"journal":{"name":"ACS Nanoscience Au","volume":null,"pages":null},"PeriodicalIF":4.8000,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acsnanoscienceau.4c00015","citationCount":"0","resultStr":"{\"title\":\"Making the Most of Nothing: One-Class Classification for Single-Molecule Transport Studies\",\"authors\":\"William Bro-Jørgensen,&nbsp;Joseph M. Hamill,&nbsp;Gréta Mezei,&nbsp;Brent Lawson,&nbsp;Umar Rashid,&nbsp;András Halbritter*,&nbsp;Maria Kamenetska*,&nbsp;Veerabhadrarao Kaliginedi* and Gemma C. Solomon*,&nbsp;\",\"doi\":\"10.1021/acsnanoscienceau.4c0001510.1021/acsnanoscienceau.4c00015\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p >Single-molecule experiments offer a unique means to probe molecular properties of individual molecules–yet they rest upon the successful control of background noise and irrelevant signals. In single-molecule transport studies, large amounts of data that probe a wide range of physical and chemical behaviors are often generated. However, due to the stochasticity of these experiments, a substantial fraction of the data may consist of blank traces where no molecular signal is evident. One-class (OC) classification is a machine learning technique to identify a specific class in a data set that potentially consists of a wide variety of classes. Here, we examine the utility of two different types of OC classification models on four diverse data sets from three different laboratories. Two of these data sets were measured at cryogenic temperatures and two at room temperature. By training the models solely on traces from a blank experiment, we demonstrate the efficacy of OC classification as a powerful and reliable method for filtering out blank traces from a molecular experiment in all four data sets. On a labeled 4,4′-bipyridine data set measured at 4.2 K, we achieve an accuracy of 96.9 ± 0.3 and an area under the receiver operating characteristic curve of 99.5 ± 0.3 as validated over a fivefold cross-validation. Given the wide range of physical and chemical properties that can be probed in single-molecule experiments, the successful application of OC classification to filter out blank traces is a major step forward in our ability to understand and manipulate molecular properties.</p>\",\"PeriodicalId\":29799,\"journal\":{\"name\":\"ACS Nanoscience Au\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.8000,\"publicationDate\":\"2024-06-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://pubs.acs.org/doi/epdf/10.1021/acsnanoscienceau.4c00015\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACS Nanoscience Au\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://pubs.acs.org/doi/10.1021/acsnanoscienceau.4c00015\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"NANOSCIENCE & NANOTECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Nanoscience Au","FirstCategoryId":"1085","ListUrlMain":"https://pubs.acs.org/doi/10.1021/acsnanoscienceau.4c00015","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"NANOSCIENCE & NANOTECHNOLOGY","Score":null,"Total":0}
引用次数: 0

摘要

单分子实验为探究单个分子的分子特性提供了一种独特的方法--然而,这种方法依赖于对背景噪声和无关信号的成功控制。在单分子输运研究中,通常会产生大量数据来探测各种物理和化学行为。然而,由于这些实验的随机性,相当一部分数据可能由空白迹线组成,其中没有明显的分子信号。单类(OC)分类是一种机器学习技术,用于在可能由多种类别组成的数据集中识别特定类别。在此,我们对来自三个不同实验室的四个不同数据集上的两种不同类型的 OC 分类模型的实用性进行了研究。其中两个数据集是在低温条件下测量的,两个是在室温条件下测量的。通过仅在空白实验痕量上训练模型,我们证明了 OC 分类作为一种强大而可靠的方法,在所有四个数据集中过滤掉分子实验中的空白痕量的功效。在 4.2 K 时测量的标记 4,4′-联吡啶数据集上,我们的准确度达到 96.9 ± 0.3,接收器工作特征曲线下的面积达到 99.5 ± 0.3,并通过五倍交叉验证进行了验证。鉴于单分子实验可探测的物理和化学性质范围很广,成功应用 OC 分类来过滤空白痕量是我们在理解和操纵分子性质的能力方面迈出的重要一步。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Making the Most of Nothing: One-Class Classification for Single-Molecule Transport Studies

Making the Most of Nothing: One-Class Classification for Single-Molecule Transport Studies

Single-molecule experiments offer a unique means to probe molecular properties of individual molecules–yet they rest upon the successful control of background noise and irrelevant signals. In single-molecule transport studies, large amounts of data that probe a wide range of physical and chemical behaviors are often generated. However, due to the stochasticity of these experiments, a substantial fraction of the data may consist of blank traces where no molecular signal is evident. One-class (OC) classification is a machine learning technique to identify a specific class in a data set that potentially consists of a wide variety of classes. Here, we examine the utility of two different types of OC classification models on four diverse data sets from three different laboratories. Two of these data sets were measured at cryogenic temperatures and two at room temperature. By training the models solely on traces from a blank experiment, we demonstrate the efficacy of OC classification as a powerful and reliable method for filtering out blank traces from a molecular experiment in all four data sets. On a labeled 4,4′-bipyridine data set measured at 4.2 K, we achieve an accuracy of 96.9 ± 0.3 and an area under the receiver operating characteristic curve of 99.5 ± 0.3 as validated over a fivefold cross-validation. Given the wide range of physical and chemical properties that can be probed in single-molecule experiments, the successful application of OC classification to filter out blank traces is a major step forward in our ability to understand and manipulate molecular properties.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
ACS Nanoscience Au
ACS Nanoscience Au 材料科学、纳米科学-
CiteScore
4.20
自引率
0.00%
发文量
0
期刊介绍: ACS Nanoscience Au is an open access journal that publishes original fundamental and applied research on nanoscience and nanotechnology research at the interfaces of chemistry biology medicine materials science physics and engineering.The journal publishes short letters comprehensive articles reviews and perspectives on all aspects of nanoscience and nanotechnology:synthesis assembly characterization theory modeling and simulation of nanostructures nanomaterials and nanoscale devicesdesign fabrication and applications of organic inorganic polymer hybrid and biological nanostructuresexperimental and theoretical studies of nanoscale chemical physical and biological phenomenamethods and tools for nanoscience and nanotechnologyself- and directed-assemblyzero- one- and two-dimensional materialsnanostructures and nano-engineered devices with advanced performancenanobiotechnologynanomedicine and nanotoxicologyACS Nanoscience Au also publishes original experimental and theoretical research of an applied nature that integrates knowledge in the areas of materials engineering physics bioscience and chemistry into important applications of nanomaterials.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信