Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Insensitive Nuclei Enhanced by Polarization Transfer (INEPT)

Insensitive Nuclei Enhanced by Polarization Transfer (INEPT)

Insensitive Nuclei Enhanced by Polarization Transfer (INEPT) is an advanced Nuclear Magnetic Resonance (NMR) technique specifically designed to detect and enhance the signals of low-abundance nuclei, such as carbon-13 and nitrogen-15, in small molecules. The fundamental principle behind INEPT is the transfer of polarization from a more abundant and highly polarizable nucleus, typically hydrogen-1, to the low-abundance nucleus of interest. This process effectively boosts the NMR signal of the...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Programmable Lipid Functionalization of Nucleic Acid Nanoparticles Modulates Liver Cell-Type Targeting.

ACS applied materials & interfaces·2026

Same author

Enabling global-scale nucleic acid repositories through versatile, scalable biochemical selection from room-temperature archives.

Nature communications·2026

Same author

AI to Identify Strain-Sensitive Regions of the Optic Nerve Head Linked to Functional Loss in Glaucoma.

Investigative ophthalmology & visual science·2026

Same author

MicroRNA spatial profiling for assessing drug efficacy in <i>BRCA1</i> -related triple-negative breast tumors.

bioRxiv : the preprint server for biology·2026

Same author

DNA origami vaccines program antigen-focused germinal centers.

Science (New York, N.Y.)·2026

Same author

High-Concentration Antibody Formulation via Solvent-Based Dehydration.

Advanced materials (Deerfield Beach, Fla.)·2025

Same journal

TiO<sub>2</sub>/SnO<sub>2</sub>-Pt Janus-Type Nanofibers with Gradient Band Distribution for Enhanced Room-Temperature NO<sub>2</sub> Detection via Cascade Photo-Generated Charge Transfer.

ACS sensors·2026

Same journal

Extended-Gate Indium-Tin-Zinc-Oxide Thin-Film Transistor Biosensor for Multiplexed Detection of Liver Cancer Biomarkers.

ACS sensors·2026

Same journal

Loofah Fiber-Reinforced Eutectogel for Motion-Robust and High-Fidelity Surface Electromyography.

ACS sensors·2026

Same journal

Water-Reactive Printable Ga-Al-Mg Liquid Metal Alloy for Rapid Wound Hemorrhage Sensing.

ACS sensors·2026

Same journal

Tailoring Asymmetric Sites to Promote Sensitive Detection of Pneumonia-Related Gases.

ACS sensors·2026

Same journal

Secretory Bioluminescent Reporter Sensor for Functional Assessment of Glucocorticoids In Vitro and In Vivo.

ACS sensors·2026

See all related articles

Search research articles

Home
Interpretable Deep Learning For Single-molecule Nanopore Fingerprinting Using Physics-guided Preprocessing.

Home
Interpretable Deep Learning For Single-molecule Nanopore Fingerprinting Using Physics-guided Preprocessing.

Related Experiment Video

Fine-tuning the Size and Minimizing the Noise of Solid-state Nanopores

Fine-tuning the Size and Minimizing the Noise of Solid-state Nanopores

Published on: October 31, 2013

Interpretable Deep Learning for Single-Molecule Nanopore Fingerprinting Using Physics-Guided Preprocessing.

Arjav Shah^1,2, Xin Kai Lee^3,4, Kun Li^2,5

¹Department of Chemical Engineering, Massachusetts Institute of Technology , Cambridge, Massachusetts 02139, United States.

|February 20, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

We developed an interpretable machine learning framework for nanopore sensing, improving molecular fingerprinting accuracy. This approach analyzes raw ionic current pulses, offering physically consistent attributions for enhanced biosensing applications.

Keywords:

DNA nanostructures convolutional neural networks explainable AI machine learning nanopore sensing signal processing single-molecule fingerprinting wavelet transform

More Related Videos

A Closed-Type Wireless Nanopore Electrode for Analyzing Single Nanoparticles

A Closed-Type Wireless Nanopore Electrode for Analyzing Single Nanoparticles

Published on: March 20, 2019

Sequencing of mRNA from Whole Blood using Nanopore Sequencing

Sequencing of mRNA from Whole Blood using Nanopore Sequencing

Published on: June 3, 2019

Related Experiment Videos

Fine-tuning the Size and Minimizing the Noise of Solid-state Nanopores

Fine-tuning the Size and Minimizing the Noise of Solid-state Nanopores

Published on: October 31, 2013

A Closed-Type Wireless Nanopore Electrode for Analyzing Single Nanoparticles

A Closed-Type Wireless Nanopore Electrode for Analyzing Single Nanoparticles

Published on: March 20, 2019

Sequencing of mRNA from Whole Blood using Nanopore Sequencing

Sequencing of mRNA from Whole Blood using Nanopore Sequencing

Published on: June 3, 2019

Area of Science:

Biotechnology
Data Science
Analytical Chemistry

Background:

Nanopore sensing offers single-molecule analysis via ionic current pulses.
Current methods rely on handcrafted features, potentially missing structural information.
Need for robust and interpretable molecular fingerprinting in various applications.

Purpose of the Study:

To develop an interpretable machine learning framework for analyzing raw nanopore sensing data.
To improve accuracy and provide physically consistent attributions for molecular identification.
To establish a practical design principle for custom filters in pulse-based sensing.

Main Methods:

Developed a physics-guided time-frequency transform paired with a compact neural classifier.

Operated directly on raw ionic current pulses, bypassing conventional feature extraction.

Included baseline models: Support Vector Machines (SVMs) and a 1D classifier on raw pulses.

Main Results:

Achieved high accuracy in distinguishing DNA nanostructures with overlapping standard features.
Generated physically consistent feature attributions, highlighting discriminative signal motifs.
Demonstrated the benefit of physics-guided preprocessing for improved reliability.

Conclusions:

The interpretable machine learning framework enhances nanopore sensing accuracy and transparency.
The approach is modular, lightweight, and applicable to diverse pulse-based sensing platforms.
This work enables deployable sensing solutions for regulated and mission-critical environments.