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

Labeling DNA Probes

Labeling DNA Probes

DNA probes are fragments of DNA labeled with a reporter tag to enable their detection or purification. The resulting labeled DNA probes can then hybridize to target nucleic acid sequences through complementary base-pairing, and may be used to recover or identify these regions.
Radioisotopes, fluorophores, or small molecule binding partners like biotin or digoxigenin, are the most widely used reporter tags for labeling DNA probes. These labels can be attached to the probe DNA molecule via...

Intracellular Signaling Affects Focal Adhesions

Intracellular Signaling Affects Focal Adhesions

Integrins act both as extracellular input receivers and as intracellular processing activators. As their name suggests, integrins are entirely integrated into the membrane structure. Their hydrophobic membrane-spanning regions interact with the phospholipid bilayer's hydrophobic region. These membrane receptors provide extracellular attachment sites for effectors like hormones and growth factors. They activate intracellular response cascades when their effectors are bound and active.
Some...

You might also read

Related Articles

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

Sort by

Same author

Hierarchical feature based dual contrastive multiview clustering.

Neural networks : the official journal of the International Neural Network Society·2026

Same author

Unsupervised feature selection via row-sparse local preserving projection.

Neural networks : the official journal of the International Neural Network Society·2026

Same author

A Unified Framework for Pseudo-Supervised Clustering via Weighted Sample Aggregation.

IEEE transactions on pattern analysis and machine intelligence·2026

Same author

Projection with mixed-size anchor graphs.

Neural networks : the official journal of the International Neural Network Society·2026

Same author

SimMTC: Simple Multi-View Tensor Clustering.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same author

Unsupervised fine-tuning of vision-language models by fusing classifier tuning and visual prompt tuning.

Neural networks : the official journal of the International Neural Network Society·2026

Search research articles

Related Experiment Video

Updated: May 29, 2025

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

Published on: December 12, 2019

Fast Co-clustering via Anchor-guided Label Spreading.

Fangyuan Xie¹, Feiping Nie¹, Weizhong Yu¹

¹School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University, Xi'an 710072, PR China.

Neural Networks : the Official Journal of the International Neural Network Society

|February 1, 2025

Summary

This study introduces Fast Co-clustering via Anchor-guided Label Spreading (FCALS), a robust method for data clustering. FCALS effectively handles noisy data by simultaneously determining sample and anchor labels, improving clustering accuracy.

Keywords:

Bipartite graph Co-clustering Label spreading Size constraints

More Related Videos

Spatial Separation of Molecular Conformers and Clusters

Spatial Separation of Molecular Conformers and Clusters

Published on: January 9, 2014

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

Related Experiment Videos

Last Updated: May 29, 2025

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

Published on: December 12, 2019

Spatial Separation of Molecular Conformers and Clusters

Spatial Separation of Molecular Conformers and Clusters

Published on: January 9, 2014

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

Area of Science:

Data Science
Machine Learning
Artificial Intelligence

Background:

Anchor graph-based clustering is effective but sensitive to noisy data.
Anchor points link sample and label spaces, crucial for label spreading.

Purpose of the Study:

To propose a robust co-clustering method resilient to noisy data.
To simultaneously determine sample and anchor labels for improved clustering.

Main Methods:

Developed Fast Co-clustering via Anchor-guided Label Spreading (FCALS).
Incorporated a size constraint to prevent trivial solutions and ensure cluster validity.
Proposed continuous (FCALS-C) and discrete (FCALS-D) models for fuzzy or discrete label matrices.

Main Results:

FCALS maximizes intra-cluster similarity among anchors while preserving anchor-data relationships.
The size constraint is robust across a broad range of values.
Experimental results on synthetic and real-world datasets demonstrate superiority.

Conclusions:

FCALS offers an effective and efficient solution for co-clustering, particularly in the presence of noisy data.
The method's ability to directly obtain anchor labels makes it suitable for out-of-sample problems.