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 Experiment Video

Updated: May 13, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

A neural network algorithm for semi-supervised node label learning from unbalanced data.

Marco Frasca1, Alberto Bertoni, Matteo Re

  • 1Dipartimento di Informatica, Università degli Studi di Milano, Via Comelico 39, 20135 Milano, Italy. frasca@di.unimi.it

Neural Networks : the Official Journal of the International Neural Network Society
|March 19, 2013
PubMed
Summary
This summary is machine-generated.

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

MiRInter-Trans: a transformer-based framework for microRNA interaction prediction.

Bioinformatics advances·2026
Same author

On the state of protein function prediction: a report on the fourth CAFA challenge.

bioRxiv : the preprint server for biology·2026
Same author

Computational understanding of non-coding RNA pairwise interactions.

Frontiers in artificial intelligence·2026
Same author

Systematic benchmarking demonstrates large language models have not reached the diagnostic accuracy of traditional rare-disease decision support tools.

European journal of human genetics : EJHG·2026
Same author

Leveraging generative AI to assist biocuration of medical actions for rare disease.

Bioinformatics advances·2025
Same author

RNA knowledge-graph analysis through homogeneous embedding methods.

Bioinformatics advances·2025
Same journal

Exploiting audio-visual modalities in videos: Object detection via multi-stage bilateral coupling network.

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

Reliability-aware modality completion with cross-modal distillation for federated learning with missing modalities.

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

IGFD-Net: Illumination-guided frequency decoupling for polarization image fusion.

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

Multiple-Strategies dung beetle optimizer and its applications in engineering optimization and bankruptcy prediction.

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

Aggregating global-scale pixel-wise forgery cues within a graph.

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

Finite-Time intermittent control for secure synchronization of Neutral-Type stochastic delayed neural networks under aperiodic DoS attacks.

Neural networks : the official journal of the International Neural Network Society·2026
See all related articles

COSNet (Cost Sensitive neural Network) effectively predicts node labels in unbalanced graphs using a novel Hopfield network approach. This method improves accuracy and scalability for large networks in applications like gene function prediction.

Area of Science:

  • Graph theory
  • Machine learning
  • Bioinformatics

Background:

  • Graph classification involves predicting node labels in weighted graphs.
  • Unbalanced labeling, where some labels are rare, is common in real-world data like gene networks.
  • Existing methods may struggle with imbalanced datasets.

Purpose of the Study:

  • To introduce COSNet (Cost Sensitive neural Network), a novel neural network algorithm for graph classification with unbalanced node labels.
  • To address the challenge of predicting rare labels in large-scale networks.

Main Methods:

  • COSNet utilizes a 2-parameter family of Hopfield networks.
  • It employs a cost-sensitive optimization for learning network parameters.
  • A restricted Hopfield network simulation on unlabeled nodes determines their classification.

Related Experiment Videos

Last Updated: May 13, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Main Results:

  • The COSNet algorithm demonstrates effectiveness on real-world unbalanced data.
  • Experimental analysis in genome-wide gene function prediction shows significant improvements.
  • The restricted dynamics lead to reduced time complexity and better scalability.

Conclusions:

  • COSNet offers an effective solution for graph classification with imbalanced labels.
  • The approach scales well for large networks, making it suitable for complex biological data.
  • This method enhances the accuracy of node label prediction in challenging datasets.