Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Nonlinear quantum light source with van der Waals ferroelectric NbOX<sub>2</sub> (X = Br, I).

Nature communications·2026
Same author

In Situ Raman Study on Thermal Decomposition of Exfoliated Two-Dimensional α-MoO<sub>3</sub>.

ACS applied materials & interfaces·2026
Same author

High-performance nanoscale polarizers enabled by transparent birefringence of layered α-MoO<sub>3</sub>.

Optics letters·2026
Same author

Spatiotemporal non-stationarity and heterogeneity in atmospheric aerosol pollution regulation by subtropical urban green space: A GTNNWR-Based study in Shenzhen, China.

Environmental pollution (Barking, Essex : 1987)·2026
Same author

Reply to "Comment on 'In-Plane Optical Anisotropy and Linear Dichroism in Low-Symmetry Layered TlSe"'.

ACS nano·2025
Same author

Crosstalk-Enabled High In-Plane Anisotropy of Monolayer MoS<sub>2</sub> Nanoribbons.

Advanced materials (Deerfield Beach, Fla.)·2025

Related Experiment Video

Updated: Nov 9, 2025

Force Spectroscopy of Single Protein Molecules Using an Atomic Force Microscope
06:45

Force Spectroscopy of Single Protein Molecules Using an Atomic Force Microscope

Published on: February 28, 2019

9.1K

Learning-based event locating for single-molecule force spectroscopy.

Zuzeng Lin1, Xiaoqing Gao1, Shuai Li1

  • 1Key Lab of Precision Measuring Technology and Instrument, Tianjin University, China.

Biochemical and Biophysical Research Communications
|April 11, 2021
PubMed
Summary
This summary is machine-generated.

Automating event detection in single-molecule force spectroscopy (SMFS) is now possible using a novel deep learning approach. This method accurately identifies events in force spectrum data, reducing manual labor and enabling high-throughput biophysical analysis.

Keywords:
Computational methodsDeep learningSingle-molecule spectroscopy

More Related Videos

Covalent Immobilization of Proteins for the Single Molecule Force Spectroscopy
11:13

Covalent Immobilization of Proteins for the Single Molecule Force Spectroscopy

Published on: August 20, 2018

11.4K
Investigating Single Molecule Adhesion by Atomic Force Spectroscopy
09:48

Investigating Single Molecule Adhesion by Atomic Force Spectroscopy

Published on: February 27, 2015

10.6K

Related Experiment Videos

Last Updated: Nov 9, 2025

Force Spectroscopy of Single Protein Molecules Using an Atomic Force Microscope
06:45

Force Spectroscopy of Single Protein Molecules Using an Atomic Force Microscope

Published on: February 28, 2019

9.1K
Covalent Immobilization of Proteins for the Single Molecule Force Spectroscopy
11:13

Covalent Immobilization of Proteins for the Single Molecule Force Spectroscopy

Published on: August 20, 2018

11.4K
Investigating Single Molecule Adhesion by Atomic Force Spectroscopy
09:48

Investigating Single Molecule Adhesion by Atomic Force Spectroscopy

Published on: February 27, 2015

10.6K

Area of Science:

  • Biophysics
  • Computational Biology
  • Machine Learning

Background:

  • Single-molecule force spectroscopy (SMFS) experiments are vital for biophysical insights.
  • Manual event identification in SMFS data is labor-intensive and limits throughput.
  • Current methods hinder full automation and scalability of SMFS experiments.

Purpose of the Study:

  • To develop an automated method for event detection in SMFS data.
  • To reduce the reliance on manual analysis and complex algorithms.
  • To enable scalable and high-throughput analysis of SMFS experiments.

Main Methods:

  • A deep neural network model was developed for event inference.
  • The model was trained using user-provided data from SMFS experiments.
  • The approach was tested on data from both optical tweezers and atomic force microscopes (AFMs).

Main Results:

  • The deep learning model achieved high accuracy in detecting events across various samples.
  • The method successfully reduced the need for manual algorithm design and parameter tuning.
  • The trained model demonstrated transferability to different optical tweezer setups.

Conclusions:

  • Deep learning offers an effective solution for automated event detection in SMFS.
  • This approach significantly enhances the efficiency and scalability of biophysical data analysis.
  • The developed model shows potential for integration into more advanced deep learning frameworks for complex analyses.