A Guide to Signal Processing Algorithms for Nanopore Sensors

Autores
Wen, Chenyu; Dematties, Dario Jesus; Zhang, Shi-Li
Año de publicación
2021
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Nanopore technology holds great promise for a wide range of applications such as biomedical sensing, chemical detection, desalination, and energy conversion. For sensing performed in electrolytes in particular, abundant information about the translocating analytes is hidden in the fluctuating monitoring ionic current contributed from interactions between the analytes and the nanopore. Such ionic currents are inevitably affected by noise; hence, signal processing is an inseparable component of sensing in order to identify the hidden features in the signals and to analyze them. This Guide starts from untangling the signal processing flow and categorizing the various algorithms developed to extracting the useful information. By sorting the algorithms under Machine Learning (ML)-based versus non-ML-based, their underlying architectures and properties are systematically evaluated. For each category, the development tactics and features of the algorithms with implementation examples are discussed by referring to their common signal processing flow graphically summarized in a chart and by highlighting their key issues tabulated for clear comparison. How to get started with building up an ML-based algorithm is subsequently presented. The specific properties of the ML-based algorithms are then discussed in terms of learning strategy, performance evaluation, experimental repeatability and reliability, data preparation, and data utilization strategy. This Guide is concluded by outlining strategies and considerations for prospect algorithms.
Fil: Wen, Chenyu. Uppsala Universitet.; Suecia
Fil: Dematties, Dario Jesus. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Ciencias Humanas, Sociales y Ambientales; Argentina
Fil: Zhang, Shi-Li. Uppsala Universitet.; Suecia
Materia
Nanopore Sensing
Signal Processing Algorithm
Machine Learning
Naural network
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/286799
Acceder
id CONICETDig_2b8e5ebea4548f8279ebb30b08d49626
oai_identifier_str oai:ri.conicet.gov.ar:11336/286799
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling A Guide to Signal Processing Algorithms for Nanopore SensorsWen, ChenyuDematties, Dario JesusZhang, Shi-LiNanopore SensingSignal Processing AlgorithmMachine LearningNaural networkhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Nanopore technology holds great promise for a wide range of applications such as biomedical sensing, chemical detection, desalination, and energy conversion. For sensing performed in electrolytes in particular, abundant information about the translocating analytes is hidden in the fluctuating monitoring ionic current contributed from interactions between the analytes and the nanopore. Such ionic currents are inevitably affected by noise; hence, signal processing is an inseparable component of sensing in order to identify the hidden features in the signals and to analyze them. This Guide starts from untangling the signal processing flow and categorizing the various algorithms developed to extracting the useful information. By sorting the algorithms under Machine Learning (ML)-based versus non-ML-based, their underlying architectures and properties are systematically evaluated. For each category, the development tactics and features of the algorithms with implementation examples are discussed by referring to their common signal processing flow graphically summarized in a chart and by highlighting their key issues tabulated for clear comparison. How to get started with building up an ML-based algorithm is subsequently presented. The specific properties of the ML-based algorithms are then discussed in terms of learning strategy, performance evaluation, experimental repeatability and reliability, data preparation, and data utilization strategy. This Guide is concluded by outlining strategies and considerations for prospect algorithms.Fil: Wen, Chenyu. Uppsala Universitet.; SueciaFil: Dematties, Dario Jesus. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Ciencias Humanas, Sociales y Ambientales; ArgentinaFil: Zhang, Shi-Li. Uppsala Universitet.; SueciaAmerican Chemical Society2021-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/286799Wen, Chenyu; Dematties, Dario Jesus; Zhang, Shi-Li; A Guide to Signal Processing Algorithms for Nanopore Sensors; American Chemical Society; ACS Sensors; 6; 10; 10-2021; 3536-35552379-3694CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://pubs.acs.org/doi/10.1021/acssensors.1c01618info:eu-repo/semantics/altIdentifier/doi/10.1021/acssensors.1c01618info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2026-06-17T09:40:44Zoai:ri.conicet.gov.ar:11336/286799instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982026-06-17 09:40:44.511CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A Guide to Signal Processing Algorithms for Nanopore Sensors
title A Guide to Signal Processing Algorithms for Nanopore Sensors
spellingShingle A Guide to Signal Processing Algorithms for Nanopore Sensors
Wen, Chenyu
Nanopore Sensing
Signal Processing Algorithm
Machine Learning
Naural network
title_short A Guide to Signal Processing Algorithms for Nanopore Sensors
title_full A Guide to Signal Processing Algorithms for Nanopore Sensors
title_fullStr A Guide to Signal Processing Algorithms for Nanopore Sensors
title_full_unstemmed A Guide to Signal Processing Algorithms for Nanopore Sensors
title_sort A Guide to Signal Processing Algorithms for Nanopore Sensors
dc.creator.none.fl_str_mv Wen, Chenyu
Dematties, Dario Jesus
Zhang, Shi-Li
author Wen, Chenyu
author_facet Wen, Chenyu
Dematties, Dario Jesus
Zhang, Shi-Li
author_role author
author2 Dematties, Dario Jesus
Zhang, Shi-Li
author2_role author
author
dc.subject.none.fl_str_mv Nanopore Sensing
Signal Processing Algorithm
Machine Learning
Naural network
topic Nanopore Sensing
Signal Processing Algorithm
Machine Learning
Naural network
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Nanopore technology holds great promise for a wide range of applications such as biomedical sensing, chemical detection, desalination, and energy conversion. For sensing performed in electrolytes in particular, abundant information about the translocating analytes is hidden in the fluctuating monitoring ionic current contributed from interactions between the analytes and the nanopore. Such ionic currents are inevitably affected by noise; hence, signal processing is an inseparable component of sensing in order to identify the hidden features in the signals and to analyze them. This Guide starts from untangling the signal processing flow and categorizing the various algorithms developed to extracting the useful information. By sorting the algorithms under Machine Learning (ML)-based versus non-ML-based, their underlying architectures and properties are systematically evaluated. For each category, the development tactics and features of the algorithms with implementation examples are discussed by referring to their common signal processing flow graphically summarized in a chart and by highlighting their key issues tabulated for clear comparison. How to get started with building up an ML-based algorithm is subsequently presented. The specific properties of the ML-based algorithms are then discussed in terms of learning strategy, performance evaluation, experimental repeatability and reliability, data preparation, and data utilization strategy. This Guide is concluded by outlining strategies and considerations for prospect algorithms.
Fil: Wen, Chenyu. Uppsala Universitet.; Suecia
Fil: Dematties, Dario Jesus. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Ciencias Humanas, Sociales y Ambientales; Argentina
Fil: Zhang, Shi-Li. Uppsala Universitet.; Suecia
description Nanopore technology holds great promise for a wide range of applications such as biomedical sensing, chemical detection, desalination, and energy conversion. For sensing performed in electrolytes in particular, abundant information about the translocating analytes is hidden in the fluctuating monitoring ionic current contributed from interactions between the analytes and the nanopore. Such ionic currents are inevitably affected by noise; hence, signal processing is an inseparable component of sensing in order to identify the hidden features in the signals and to analyze them. This Guide starts from untangling the signal processing flow and categorizing the various algorithms developed to extracting the useful information. By sorting the algorithms under Machine Learning (ML)-based versus non-ML-based, their underlying architectures and properties are systematically evaluated. For each category, the development tactics and features of the algorithms with implementation examples are discussed by referring to their common signal processing flow graphically summarized in a chart and by highlighting their key issues tabulated for clear comparison. How to get started with building up an ML-based algorithm is subsequently presented. The specific properties of the ML-based algorithms are then discussed in terms of learning strategy, performance evaluation, experimental repeatability and reliability, data preparation, and data utilization strategy. This Guide is concluded by outlining strategies and considerations for prospect algorithms.
publishDate 2021
dc.date.none.fl_str_mv 2021-10
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/286799
Wen, Chenyu; Dematties, Dario Jesus; Zhang, Shi-Li; A Guide to Signal Processing Algorithms for Nanopore Sensors; American Chemical Society; ACS Sensors; 6; 10; 10-2021; 3536-3555
2379-3694
CONICET Digital
CONICET
url http://hdl.handle.net/11336/286799
identifier_str_mv Wen, Chenyu; Dematties, Dario Jesus; Zhang, Shi-Li; A Guide to Signal Processing Algorithms for Nanopore Sensors; American Chemical Society; ACS Sensors; 6; 10; 10-2021; 3536-3555
2379-3694
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://pubs.acs.org/doi/10.1021/acssensors.1c01618
info:eu-repo/semantics/altIdentifier/doi/10.1021/acssensors.1c01618
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv American Chemical Society
publisher.none.fl_str_mv American Chemical Society
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1868338997805187072
score 13.040872

Publicaciones similares