A Solid-State Nanopore Signal Generator for Training Machine Learning Models

• URL: http://arxiv.org/abs/2504.05466v1
• Date: Mon, 07 Apr 2025 19:56:35 GMT
• Title: A Solid-State Nanopore Signal Generator for Training Machine Learning Models
• Authors: Jaise Johnson, Chinmayi R Galigekere, Manoj M Varma,
• Abstract summary: We introduce a nanopore signal generator capable of producing extensive synthetic datasets for machine learning applications.<n>We train deep learning models to detect translocation events directly from raw signals, achieving over 99% true event detection with minimal false positives.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Translocation event detection from raw nanopore current signals is a fundamental step in nanopore signal analysis. Traditional data analysis methods rely on user-defined parameters to extract event information, making the interpretation of experimental results sensitive to parameter choice. While Machine Learning (ML) has seen widespread adoption across various scientific fields, its potential remains underexplored in solid-state nanopore research. In this work, we introduce a nanopore signal generator capable of producing extensive synthetic datasets for machine learning applications and benchmarking nanopore signal analysis platforms. Using this generator, we train deep learning models to detect translocation events directly from raw signals, achieving over 99% true event detection with minimal false positives.

Related papers

• Rethinking Cross-Generator Image Forgery Detection through DINOv3 [62.80415066351157]
  Cross-generator detection has emerged as a new challenge forgenerative models.<n>We show that frozen visual foundation models, especially DINOv3, already exhibit strong cross-generator detection capability.<n>We introduce a training-free token-ranking strategy followed by a lightweight linear probe to select a small subset of authenticity-relevant tokens.
  arXiv  Detail & Related papers  (2025-11-27T14:01:50Z)
• Identification of Capture Phases in Nanopore Protein Sequencing Data Using a Deep Learning Model [0.0]
  We develop a lightweight one-dimensional convolutional neural network (1D CNN) to detect capture phases in down-sampled signal windows.<n>Our best model, CaptureNet-Deep, achieved an F1 score of 0.94 and precision of 93.39% on held-out test data.<n>These results show that efficient, real-time capture detection is possible using simple, interpretable architectures.
  arXiv  Detail & Related papers  (2025-11-03T06:51:53Z)
• Additive decomposition of one-dimensional signals using Transformers [48.7025991956527]
  One-dimensional signal decomposition is a well-established and widely used technique across various scientific fields.<n>Recent research suggests that applying the latest deep learning models to this problem presents an exciting, unexplored area with promising potential.<n>We leverage the Transformer architecture to decompose signals into their constituent components.
  arXiv  Detail & Related papers  (2025-06-06T10:09:40Z)
• Scintillation pulse characterization with spectrum-inspired temporal neural networks: case studies on particle detector signals [1.124958340749622]
  We propose a network architecture specially tailored for scintillation pulse characterization based on previous works on time series analysis.<n>We prove our idea in two case studies: (a) simulation data generated with the setting of the LUX dark matter detector, and (b) experimental electrical signals with fast electronics to emulate scintillation variations for the NICA/MPD calorimeter.
  arXiv  Detail & Related papers  (2024-10-09T02:44:53Z)
• Learning and Controlling Silicon Dopant Transitions in Graphene using Scanning Transmission Electron Microscopy [58.51812955462815]
  We introduce a machine learning approach to determine the transition dynamics of silicon atoms on a single layer of carbon atoms. The data samples are processed and filtered to produce symbolic representations, which we use to train a neural network to predict transition probabilities. These learned transition dynamics are then leveraged to guide a single silicon atom throughout the lattice to pre-determined target destinations.
  arXiv  Detail & Related papers  (2023-11-21T21:51:00Z)
• Analytical Modelling of Raw Data for Flow-Guided In-body Nanoscale Localization [5.188841610098436]
  In precision medicine, nanodevices show promise for disease diagnostics, treatment, and monitoring from within the bloodstreams. Current flow-guided localization approaches are constrained in their communication and energy-related capabilities. We propose an analytical model of raw data for flow-guided localization, where the raw data is modeled as a function of communication and energy-related capabilities of the nanodevices.
  arXiv  Detail & Related papers  (2023-09-27T21:26:01Z)
• Convolutional Neural Networks for the classification of glitches in gravitational-wave data streams [52.77024349608834]
  We classify transient noise signals (i.e.glitches) and gravitational waves in data from the Advanced LIGO detectors. We use models with a supervised learning approach, both trained from scratch using the Gravity Spy dataset. We also explore a self-supervised approach, pre-training models with automatically generated pseudo-labels.
  arXiv  Detail & Related papers  (2023-03-24T11:12:37Z)
• Signal Detection in MIMO Systems with Hardware Imperfections: Message Passing on Neural Networks [101.59367762974371]
  In this paper, we investigate signal detection in multiple-input-multiple-output (MIMO) communication systems with hardware impairments. It is difficult to train a deep neural network (DNN) with limited pilot signals, hindering its practical applications. We design an efficient message passing based Bayesian signal detector, leveraging the unitary approximate message passing (UAMP) algorithm.
  arXiv  Detail & Related papers  (2022-10-08T04:32:58Z)
• Time-to-Green predictions for fully-actuated signal control systems with supervised learning [56.66331540599836]
  This paper proposes a time series prediction framework using aggregated traffic signal and loop detector data. We utilize state-of-the-art machine learning models to predict future signal phases' duration. Results based on an empirical data set from a fully-actuated signal control system in Zurich, Switzerland, show that machine learning models outperform conventional prediction methods.
  arXiv  Detail & Related papers  (2022-08-24T07:50:43Z)
• Decision Forest Based EMG Signal Classification with Low Volume Dataset Augmented with Random Variance Gaussian Noise [51.76329821186873]
  We produce a model that can classify six different hand gestures with a limited number of samples that generalizes well to a wider audience. We appeal to a set of more elementary methods such as the use of random bounds on a signal, but desire to show the power these methods can carry in an online setting.
  arXiv  Detail & Related papers  (2022-06-29T23:22:18Z)
• Data-driven detector signal characterization with constrained bottleneck autoencoders [0.0]
  deep learning in the form of constrained bottleneck autoencoders can be used to learn the underlying unknown detector response model directly from data. The trained algorithm can be used simultaneously to perform estimations on the physical parameters of the model, simulate the detector response with high fidelity and to denoise detector signals.
  arXiv  Detail & Related papers  (2022-03-09T09:46:10Z)
• Grasp-and-Lift Detection from EEG Signal Using Convolutional Neural Network [1.869097450593631]
  This article has automated the hand movement activity viz GAL detection method from the 32-channel EEG signals. The proposed pipeline essentially combines preprocessing and end-to-end detection steps, eliminating the requirement of hand-crafted feature engineering.
  arXiv  Detail & Related papers  (2022-02-12T19:27:06Z)
• DriPP: Driven Point Processes to Model Stimuli Induced Patterns in M/EEG Signals [62.997667081978825]
  We develop a novel statistical point process model-called driven temporal point processes (DriPP) We derive a fast and principled expectation-maximization (EM) algorithm to estimate the parameters of this model. Results on standard MEG datasets demonstrate that our methodology reveals event-related neural responses.
  arXiv  Detail & Related papers  (2021-12-08T13:07:21Z)
• Signal Transformer: Complex-valued Attention and Meta-Learning for Signal Recognition [33.178794056273304]
  We propose a Complex-valued Attentional MEta Learner (CAMEL) for the problem few of general nonvalued problems with theoretical convergence guarantees. This paper shows the superiority of the proposed data recognition experiments when the state is abundant small data.
  arXiv  Detail & Related papers  (2021-06-05T03:57:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.