ArticleACS nano2021
Deep Learning of Nanopore Sensing Signals Using a Bi-Path Network.
Article in ACS nano, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed.
- Advanced deep learning strategies in nanopore RNA sequencing.RNA biology · 2026Review
- Denoise Stepwise Signals by Diffusion Model-Based Approach.Small methods · 2026Article
- Rational Engineering of the Anthrax Toxin Nanopore Interface for Orthogonal Peptide Classification.ACS omega · 2026Article
- A dynamical anthrax toxin nanopore biosensor for high-fidelity single-peptide classification.PLoS computational biology · 2026Article
- Brownian motion data augmentation: a method to push neural network performance on nanopore sensors.Bioinformatics (Oxford, England) · 2025Article
- Deep learning-based classification of peptide analytes from single-channel nanopore translocation events.PloS one · 2025Article
- A Robust Parallel Computing Data Extraction Framework for Nanopore Experiments.Small methods · 2024Article
- Brownian Motion Paving the Way for Molecular Translocation in Nanopores.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
- Review
- Portable nanopore-sequencing technology: Trends in development and applications.Frontiers in microbiology · 2023Review
- Article
- Electrical Conductance of Charged Nanopores.ACS omega · 2022Article
- A Generalized Transformer-Based Pulse Detection Algorithm.ACS sensors · 2022Article
- A Guide to Signal Processing Algorithms for Nanopore Sensors.ACS sensors · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Temporal changes in electrical resistance of a nanopore sensor caused by translocating target analytes are recorded as a sequence of pulses on current traces. Prevalent algorithms for feature extraction in pulse-like signals lack objectivity because empirical amplitude thresholds are user-defined to single out the pulses from the noisy background. Here, we use deep learning for feature extraction based on a bi-path network (B-Net). After training, the B-Net acquires the prototypical pulses and the ability of both pulse recognition and feature extraction without
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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.