ArticleACS sensors2021
A Guide to Signal Processing Algorithms for Nanopore Sensors.
Article in ACS sensors, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed, 89 citations in OpenAlex.
- Article
- Beyond AI-Driven Curve Fitting: Mechanistic Insight as the Cornerstone of Reliable Sensing and Accurate Fitting.Small methods · 2026Article
- Threading Precision: Progress and Emerging Trends in Aptamer-Based Nanopore Sensing.Biosensors · 2026Review
- Nanopore event detection in a simple and adaptive way.bioRxiv : the preprint server for biology · 2026Article
- The evolution of nanopore measurements: from biological out-of-plane pores to plastic in-plane pores.Lab on a chip · 2026Review
- Nanopore Trap for Label-Free Fingerprinting of Surface-modified Single Nanoparticles.Small methods · 2026Article
- Continuous, Low Latency Estimation of the Size and Shape of Single Proteins from Real-Time Nanopore Data.Analytical chemistry · 2026Article
- Nanopore-based sensing for biomarker detection: from fundamental principles to translational diagnostics.Journal of nanobiotechnology · 2025Review
- Single-Molecule Protein Profiling Using Nanopores and Dimeric Aptamer-Modified DNA Carriers.Angewandte Chemie (International ed. in English) · 2025Article
- Nanopore-Based Neurotransmitter Detection: Advances, Challenges, and Future Perspectives.ACS nano · 2025Review
- Brownian motion data augmentation: a method to push neural network performance on nanopore sensors.Bioinformatics (Oxford, England) · 2025Article
- Solid-State Nanopore Sensors: Analyte Quantification by Event Frequency Analysis at High Voltages.Analytical chemistry · 2025Article
- A Robust Parallel Computing Data Extraction Framework for Nanopore Experiments.Small methods · 2024Article
- Single-Molecule Bioelectronic Sensors with AI-Aided Data Analysis: Convergence and Challenges.Precision chemistry · 2024Review
- Machine learning empowered next generation DNA sequencing: perspective and prospectus.Chemical science · 2024Review
- Electrochemical Visualization of Single-Molecule Thiol Substitution with Nanopore Measurement.ACS measurement science au · 2024Article
- Review
- Identification of tagged glycans with a protein nanopore.Nature communications · 2023Article
- The New Era of High-Throughput Nanoelectrochemistry.Analytical chemistry · 2023Review
- Portable nanopore-sequencing technology: Trends in development and applications.Frontiers in microbiology · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.