Base Motif Recognition and Design of DNA Templates for Fluorescent Silver Clusters by Machine Learning

Copp, Stacy M.; Bogdanov, Petko; Debord, Mark; Singh, Ambuj K.; Gwinn, E. G.

doi:10.1002/adma.201600801

Cited by 29 publications

(46 citation statements)

References 35 publications

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“…Learning. e development of big data smart shopping and its rapid deployment have led to the generation of large amounts of data at an unprecedented speed [5,6]. Unfortunately, due to the lack of established mechanisms and standards that benefit from the availability of such data, most of the generated data is wasted without extracting potentially useful information and knowledge [7].…”

Section: Shopping Under the Influence Of Big Data And Machinementioning

confidence: 99%

Statistical Modeling and Simulation of Online Shopping Customer Loyalty Based on Machine Learning and Big Data Analysis

Huang

Fong

et al. 2021

Security and Communication Networks

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With the increase in the number of online shopping users, customer loyalty is directly related to product sales. This research mainly explores the statistical modeling and simulation of online shopping customer loyalty based on machine learning and big data analysis. This research mainly uses machine learning clustering algorithm to simulate customer loyalty. Call the k-means interactive mining algorithm based on the Hash structure to perform data mining on the multidimensional hierarchical tree of corporate credit risk, continuously adjust the support thresholds for different levels of data mining according to specific requirements and select effective association rules until satisfactory results are obtained. After conducting credit risk assessment and early warning modeling for the enterprise, the initial preselected model is obtained. The information to be collected is first obtained by the web crawler from the target website to the temporary web page database, where it will go through a series of preprocessing steps such as completion, deduplication, analysis, and extraction to ensure that the crawled web page is correctly analyzed, to avoid incorrect data due to network errors during the crawling process. The correctly parsed data will be stored for the next step of data cleaning or data analysis. For writing a Java program to parse HTML documents, first set the subject keyword and URL and parse the HTML from the obtained file or string by analyzing the structure of the website. Secondly, use the CSS selector to find the web page list information, retrieve the data, and store it in Elements. In the overall fit test of the model, the root mean square error approximation (RMSEA) value is 0.053, between 0.05 and 0.08. The results show that the model designed in this study achieves a relatively good fitting effect and strengthens customers’ perception of shopping websites, and relationship trust plays a greater role in maintaining customer loyalty.

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Section: Shopping Under the Influence Of Big Data And Machinementioning

confidence: 99%

Statistical Modeling and Simulation of Online Shopping Customer Loyalty Based on Machine Learning and Big Data Analysis

Huang

Fong

et al. 2021

Security and Communication Networks

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“…In particular, silver (Ag) nanoclusters (AgNC) have been widely developed due to their appealing fluorescent features and promising biomedical applications such as bioimaging and biosensing . The synthesis, size, and fluorescent properties of AgNC could be regulated by various scaffolds (or termed as templates), such as carboxylate groups (–COO − ), thiol‐compounds, polymers, amino acids, and peptides . Alternatively, DNA has been proven an excellent scaffold due to its unique molecular recognition and sequence programmability, which could regulate the formation of AgNC and tune the size and fluorescent properties of AgNC .…”

Section: Introductionmentioning

confidence: 99%

Synthesis of Branched DNA Scaffolded Super‐Nanoclusters with Enhanced Antibacterial Performance

Yao

Feng

et al. 2018

Small

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Metal nanoclusters (NCs) possess unique optical properties, and exhibit a wide variety of potential applications. DNA with robust molecular programmability is demonstrated as an ideal scaffold to regulate the formation of NCs, offering a rational approach to precisely tune the spatial structures of NCs. Herein, the first use of branched DNA as scaffold to regulate the formation of silver nanoclusters (super-AgNC) is reported, in which the spatial structures are precisely designed and constructed. Super-AgNC with tunable shapes and arm-lengths including Y-, X-, and (Y-X)- shaped super-AgNC is achieved. The molecular structures and optical properties of super-AgNCs are systemically studied. As a proof of application, remarkably, super-AgNCs exhibit superior antibacterial performance. In addition, super-AgNCs show excellent biocompatibility with three types of tissue cells including 293T (human embryonic kidney cells), SMCs (vascular smooth muscle cells), and GLC-82 (lung adenocarcinoma cells). These performances enable the super-AgNCs adaptable in a variety of applications such as biosensing, bioimaging, and antibacterial agents.

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“…Improvements in sensitivity might be achieved through directed mutation or automated screens of DNA sequence and nanocluster synthesis space. Given the documented successes of such screens 39 as well as the ability to engineer FRET pairs, 40 there is a real possibility of discovering clusters with sufficient sensitivities and temporal responses to enable real-time, quantitative imaging of neurotransmitters in vivo.…”

Section: Specificity Of Silver Nanoclusters For Dopamine Over Known Homologues and Competitorsmentioning

confidence: 99%

DNA-Stabilized Silver Nanoclusters as Specific, Ratiometric Fluorescent Dopamine Sensors

Bonis-O’Donnell

Thakrar

Hirschberg

et al. 2017

ACS Chem. Neurosci.

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Neurotransmitters are small molecules that orchestrate complex patterns of brain activity. Unfortunately, there exist few sensors capable of directly detecting individual neurotransmitters. Those sensors that do exist are either unspecific or fail to capture the temporal or spatial dynamics of neurotransmitter release. DNA-stabilized silver nanoclusters (DNA-AgNCs) are a new class of biocompatible, fluorescent nanostructures that have recently been shown to offer promise as biosensors. In this work, we identify two different DNA sequences that form dopamine-sensitive nanoclusters. We demonstrate that each sequence supports two distinct DNA-AgNCs capable of providing specific, ratiometric fluorescent sensing of dopamine concentration in vitro. DNA-Ag nanoclusters therefore offer a novel, low-cost approach to quantification of dopamine, creating the potential for real-time monitoring in vivo.

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Base Motif Recognition and Design of DNA Templates for Fluorescent Silver Clusters by Machine Learning

Cited by 29 publications

References 35 publications

Statistical Modeling and Simulation of Online Shopping Customer Loyalty Based on Machine Learning and Big Data Analysis

Statistical Modeling and Simulation of Online Shopping Customer Loyalty Based on Machine Learning and Big Data Analysis

Synthesis of Branched DNA Scaffolded Super‐Nanoclusters with Enhanced Antibacterial Performance

DNA-Stabilized Silver Nanoclusters as Specific, Ratiometric Fluorescent Dopamine Sensors

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