A Guide to Signal Processing Algorithms for Nanopore Sensors Article Swipe
Chenyu Wen
,
Darío Demattíes
,
Shi‐Li Zhang
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1021/acssensors.1c01618
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1021/acssensors.1c01618
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.
Related Topics
Concepts
Computer science
Algorithm
Signal processing
SIGNAL (programming language)
Nanopore
Sorting
Noise (video)
Machine learning
Reliability (semiconductor)
Artificial intelligence
Analyte
Key (lock)
Data processing
Data mining
Nanotechnology
Materials science
Chemistry
Physics
Programming language
Computer security
Operating system
Telecommunications
Radar
Power (physics)
Physical chemistry
Quantum mechanics
Image (mathematics)
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1021/acssensors.1c01618
- https://pubs.acs.org/doi/pdf/10.1021/acssensors.1c01618
- OA Status
- hybrid
- Cited By
- 80
- References
- 106
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3204648240
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W3204648240Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1021/acssensors.1c01618Digital Object Identifier
- Title
-
A Guide to Signal Processing Algorithms for Nanopore SensorsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-10-04Full publication date if available
- Authors
-
Chenyu Wen, Darío Demattíes, Shi‐Li ZhangList of authors in order
- Landing page
-
https://doi.org/10.1021/acssensors.1c01618Publisher landing page
- PDF URL
-
https://pubs.acs.org/doi/pdf/10.1021/acssensors.1c01618Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
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https://pubs.acs.org/doi/pdf/10.1021/acssensors.1c01618Direct OA link when available
- Concepts
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Computer science, Algorithm, Signal processing, SIGNAL (programming language), Nanopore, Sorting, Noise (video), Machine learning, Reliability (semiconductor), Artificial intelligence, Analyte, Key (lock), Data processing, Data mining, Nanotechnology, Materials science, Chemistry, Physics, Programming language, Computer security, Operating system, Telecommunications, Radar, Power (physics), Physical chemistry, Quantum mechanics, Image (mathematics)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
80Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 28, 2024: 14, 2023: 21, 2022: 17Per-year citation counts (last 5 years)
- References (count)
-
106Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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