# Deep learning of nanopore sensing signals using a bi-path network

arXiv: https://arxiv.org/abs/2105.03660 · HTML: https://mlsanity.com/paper/2105.03660/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 72 references that are in the map.
- First posted on arXiv 8 May 2021.

## Builds on (most cited first, 6 of 6)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [Speech Enhancement In Multiple-Noise Conditions using Deep Neural Networks](https://mlsanity.com/paper/1605.02427/) (2016)
- [1D Convolutional Neural Network Models for Sleep Arousal Detection](https://mlsanity.com/paper/1903.01552/) (2019)
- [Identity Connections in Residual Nets Improve Noise Stability](https://mlsanity.com/paper/1905.10944/) (2019)

## Built on by (most cited first, 2 of 2)

- [Denoise Stepwise Signals by Diffusion Model Based Approach](https://mlsanity.com/paper/2602.08904/) (2026)
- [Latent space mapping of interpretable structural coordinates from stochastic single-molecule signals](https://mlsanity.com/paper/2606.16950/) (2026)

## Abstract (arXiv)

Temporary 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 a priori assigned parameters. The B-Net performance is evaluated on generated datasets and further applied to experimental data of DNA and protein translocation. The B-Net results show remarkably small relative errors and stable trends. The B-Net is further shown capable of processing data with a signal-to-noise ratio equal to one, an impossibility for threshold-based algorithms. The developed B-Net is generic for pulse-like signals beyond pulsed nanopore currents.