Next Article in Journal
Development of a Novel ssDNA Sequence for a Glycated Human Serum Albumin and Construction of a Simple Aptasensor System Based on Reduced Graphene Oxide (rGO)
Next Article in Special Issue
Effect of Electrolyte Concentration on Cell Sensing by Measuring Ionic Current Waveform through Micropores
Previous Article in Journal
Screen-Printed Electrode-Based Sensors for Food Spoilage Control: Bacteria and Biogenic Amines Detection
Article

Machine Learning to Improve the Sensing of Biomolecules by Conical Track-Etched Nanopore

1
Institut Européen des Membranes, UMR5635, UM, ENSCM, CNRS, 34095 Montpellier, France
2
Mécanismes Moléculaires dans les Démences Neurodégénératives, U1198, UM, EPHE, INSERM, 34095 Montpellier, France
3
Institut des Matériaux Poreux de Paris UMR8004, CNRS, ENS, ESPCI, 75005 Paris, France
4
Institut des Biomolécules Max Mousseron, Université de Montpellier, CNRS, ENSCM, 34095 Montpellier, France
*
Author to whom correspondence should be addressed.
Biosensors 2020, 10(10), 140; https://doi.org/10.3390/bios10100140
Received: 7 September 2020 / Revised: 26 September 2020 / Accepted: 30 September 2020 / Published: 5 October 2020
(This article belongs to the Special Issue Micro- and Nanopore Biosensors)
Single nanopore is a powerful platform to detect, discriminate and identify biomacromolecules. Among the different devices, the conical nanopores obtained by the track-etched technique on a polymer film are stable and easy to functionalize. However, these advantages are hampered by their high aspect ratio that avoids the discrimination of similar samples. Using machine learning, we demonstrate an improved resolution so that it can identify short single- and double-stranded DNA (10- and 40-mers). We have characterized each current blockade event by the relative intensity, dwell time, surface area and both the right and left slope. We show an overlap of the relative current blockade amplitudes and dwell time distributions that prevents their identification. We define the different parameters that characterize the events as features and the type of DNA sample as the target. By applying support-vector machines to discriminate each sample, we show accuracy between 50% and 72% by using two features that distinctly classify the data points. Finally, we achieved an increased accuracy (up to 82%) when five features were implemented. View Full-Text
Keywords: nanopore; machine learning; DNA sensing nanopore; machine learning; DNA sensing
Show Figures

Figure 1

MDPI and ACS Style

Meyer, N.; Janot, J.-M.; Lepoitevin, M.; Smietana, M.; Vasseur, J.-J.; Torrent, J.; Balme, S. Machine Learning to Improve the Sensing of Biomolecules by Conical Track-Etched Nanopore. Biosensors 2020, 10, 140. https://doi.org/10.3390/bios10100140

AMA Style

Meyer N, Janot J-M, Lepoitevin M, Smietana M, Vasseur J-J, Torrent J, Balme S. Machine Learning to Improve the Sensing of Biomolecules by Conical Track-Etched Nanopore. Biosensors. 2020; 10(10):140. https://doi.org/10.3390/bios10100140

Chicago/Turabian Style

Meyer, Nathan, Jean-Marc Janot, Mathilde Lepoitevin, Michaël Smietana, Jean-Jacques Vasseur, Joan Torrent, and Sébastien Balme. 2020. "Machine Learning to Improve the Sensing of Biomolecules by Conical Track-Etched Nanopore" Biosensors 10, no. 10: 140. https://doi.org/10.3390/bios10100140

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop