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

Sub-Terahertz Memristor Switches Using MoS<sub>2</sub> by Liquid-Liquid Interface Assembly.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Surgical Treatment of Peri-Implant Defects with L-PRF-Xenograft Bone Blocks: A Prospective Case Series.

Bioengineering (Basel, Switzerland)·2026
Same author

Multi-head CRF classifier for biomedical multi-class named entity recognition on Spanish clinical notes.

Database : the journal of biological databases and curation·2024
Same author

Plasma Brain-Derived Tau in Prognosis of Large Vessel Occlusion Ischemic Stroke.

Stroke·2024
Same author

Towards discovery: an end-to-end system for uncovering novel biomedical relations.

Database : the journal of biological databases and curation·2024
Same author

Using inertial measurement units for quantifying the most intense jumping movements occurring in professional male volleyball players.

Scientific reports·2023

Related Experiment Video

Updated: Feb 17, 2026

Author Spotlight: Unraveling the Molecular Mechanisms of Brown and Beige Adipocyte Regulation
07:16

Author Spotlight: Unraveling the Molecular Mechanisms of Brown and Beige Adipocyte Regulation

Published on: January 5, 2024

1.7K

Protein-Protein Interaction Article Classification Using a Convolutional Recurrent Neural Network with Pre-trained

Sérgio Matos1, Rui Antunes1

  • 1.

Journal of Integrative Bioinformatics
|December 14, 2017
PubMed
Summary

This study introduces a deep learning model to efficiently identify scientific articles containing protein interaction data. The convolutional recurrent neural network improves the curation of crucial biological interaction networks.

Keywords:
Literature retrievalmachine learningprotein-protein interactionsrecurrent neural networksword embeddings

More Related Videos

TurboID-Based Proximity Labeling for In Planta Identification of Protein-Protein Interaction Networks
07:02

TurboID-Based Proximity Labeling for In Planta Identification of Protein-Protein Interaction Networks

Published on: May 17, 2020

26.2K
Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
06:50

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

2.6K

Related Experiment Videos

Last Updated: Feb 17, 2026

Author Spotlight: Unraveling the Molecular Mechanisms of Brown and Beige Adipocyte Regulation
07:16

Author Spotlight: Unraveling the Molecular Mechanisms of Brown and Beige Adipocyte Regulation

Published on: January 5, 2024

1.7K
TurboID-Based Proximity Labeling for In Planta Identification of Protein-Protein Interaction Networks
07:02

TurboID-Based Proximity Labeling for In Planta Identification of Protein-Protein Interaction Networks

Published on: May 17, 2020

26.2K
Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
06:50

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

2.6K

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Biomedical Informatics

Background:

  • Protein interactions are vital for understanding biological processes, diseases, and drug actions.
  • The exponential growth of scientific literature makes manual curation of protein interaction data challenging and costly.

Purpose of the Study:

  • To develop an automated method for identifying relevant scientific articles containing protein interaction information.
  • To improve the efficiency and scalability of protein interaction data curation.

Main Methods:

  • Utilized a convolutional recurrent neural network (CRNN) architecture.
  • Applied the model to the BioCreative III Article Classification Task dataset.
  • Evaluated model performance using standard metrics for classification tasks.

Main Results:

  • Achieved an area under the precision-recall curve (AUPRC) of 0.715.
  • Obtained a Matthew's correlation coefficient (MCC) of 0.600.
  • Demonstrated superior performance compared to previous methods in article classification for protein interaction extraction.

Conclusions:

  • The developed CRNN model offers an effective solution for automated identification of protein interaction literature.
  • This approach enhances the curation of protein interaction networks, facilitating biological research.
  • The findings represent a significant advancement in computational approaches for biomedical text mining.