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, Joseph M. Hamill, Gréta Mezei, Brent Lawson, Umar Rashid, András Halbritter*, Maria Kamenetska*, Veerabhadrarao Kaliginedi* and Gemma C. Solomon*, ","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":"4 4","pages":"250–262 250–262"},"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, Joseph M. Hamill, Gréta Mezei, Brent Lawson, Umar Rashid, András Halbritter*, Maria Kamenetska*, Veerabhadrarao Kaliginedi* and Gemma C. Solomon*, \",\"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. 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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.
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.