ArticleNano letters2018
QuipuNet: Convolutional Neural Network for Single-Molecule Nanopore Sensing.
Article in Nano letters, 2018. 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.
- A universal deep learning framework for empowering nanopore identification by reinforcing temporal signals.Nature communications · 2026Article
- Quantification of disease-associated RNA tandem repeats by nanopore sensing.Nature communications · 2026Article
- NanoSSL: attention mechanism-based self-supervised learning method for protein identification using nanopores.Bioinformatics (Oxford, England) · 2026Article
- Preparation and Detection of Designed DNA Carriers for Nanopore Sensing.Methods in molecular biology (Clifton, N.J.) · 2026Article
- DemuxTrans: Transformer and temporal convolution network for accurate barcode demultiplexing in nanopore sequencing.Bioinformatics (Oxford, England) · 2025Article
- Brownian motion data augmentation: a method to push neural network performance on nanopore sensors.Bioinformatics (Oxford, England) · 2025Article
- Detection of protein oligomers with nanopores.Nature reviews. Chemistry · 2025Review
- Amplification-Free Quantification of Endogenous Mitochondrial DNA Copy Number Using Solid-State Nanopores.ACS nano · 2025Article
- 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
- Deep learning algorithms applied to computational chemistry.Molecular diversity · 2024Review
- Nanopore analysis of salvianolic acids in herbal medicines.Nature communications · 2024Article
- T-S2Inet: Transformer-based sequence-to-image network for accurate nanopore sequence recognition.Bioinformatics (Oxford, England) · 2024Article
- Analysis of Nanopore Data: Classification Strategies for an Unbiased Curation of Single-Molecule Events from DNA Nanostructures.ACS sensors · 2023Article
- Active learning for efficient analysis of high-throughput nanopore data.Bioinformatics (Oxford, England) · 2023Article
- Review
- Machine learning approaches for biomolecular, biophysical, and biomaterials research.Biophysics reviews · 2022Review
- Biological nanopores for single-molecule sensing.iScience · 2022Review
- Nanopore sensors for viral particle quantification: current progress and future prospects.Bioengineered · 2021Review
- A Guide to Signal Processing Algorithms for Nanopore Sensors.ACS sensors · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Nanopore sensing is a versatile technique for the analysis of molecules on the single-molecule level. However, extracting information from data with established algorithms usually requires time-consuming checks by an experienced researcher due to inherent variability of solid-state nanopores. Here, we develop a convolutional neural network (CNN) for the fully automated extraction of information from the time-series signals obtained by nanopore sensors. In our demonstration, we use a previously published data set on multiplexed single-molecule protein sensing. The neural network learns to classify translocation events with greater accuracy than previously possible, while also increasing the number of analyzable events by a factor of 5. Our results demonstrate that deep learning can achieve significant improvements in single molecule nanopore detection with potential applications in rapid diagnostics.
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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 OpenQuestion graph.