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

Protein-protein Interfaces02:04

Protein-protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a polypeptide...
Ligand Binding Sites02:40

Ligand Binding Sites

Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...

You might also read

Related Articles

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

Sort by
Same author

How can biological databases support the new UN mechanism for benefit-sharing from digital sequence information?

Scientific data·2026
Same author

Impact of a structured case report on self-reported responses to simulated emergency scenarios: a randomized survey-based study.

Scientific reports·2026
Same author

Comparison of Blood Culture Contamination Rates Between Initial Central Venous Puncture and Catheter Hub Sampling.

Critical care medicine·2026
Same authorSame journal

Evolving bioinformatics services - the journey of KPI metrics with Scorpion.

Journal of integrative bioinformatics·2026
Same author

Integrating plant phenotypic and genotypic data in the AGENT project: a BrAPI service implementation.

Bioinformatics (Oxford, England)·2026
Same author

Influence of odorants on preterm and term infants during neonatal intensive care: Overview and perspectives.

Zeitschrift fur Geburtshilfe und Neonatologie·2026
Same journal

Fusion of computational and experimental provenance in RO-Crate.

Journal of integrative bioinformatics·2026
Same journal

Updates and validation of the Compi RNA-seq pipeline with a case study in Alzheimer's disease.

Journal of integrative bioinformatics·2026
Same journal

Fragment-level FAIRness: annotating scientific data and its provenance using data fragment selectors.

Journal of integrative bioinformatics·2026
Same journal

Integrating cross-omics research through FAIR Digital Objects with DataPLANT.

Journal of integrative bioinformatics·2026
Same journal

Pheno-App 2.0 - a mobile app for collecting phenotypic data in plant research.

Journal of integrative bioinformatics·2026
See all related articles

Related Experiment Video

Updated: May 21, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

IDPredictor: predict database links in biomedical database.

Hendrik Mehlhorn1, Matthias Lange, Uwe Scholz

  • 1Leibniz Institute of Plant Genetics and Crop Plant Research, Gatersleben, Germany. mehlhorn@ipk-gatersleben.de

Journal of Integrative Bioinformatics
|June 28, 2012
PubMed
Summary
This summary is machine-generated.

This study presents a method to automatically build an integrated network of biomedical databases by predicting and extracting cross-references. This approach enhances data retrieval and functional annotation for bioinformatics research.

More Related Videos

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila
06:41

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

Published on: August 20, 2019

Related Experiment Videos

Last Updated: May 21, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila
06:41

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

Published on: August 20, 2019

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Information Science

Background:

  • Biomedical knowledge is distributed across numerous databases with varying formats and structures.
  • Existing search engines struggle to provide a comprehensive view due to fragmented and underexplored cross-references.
  • An integrated data view is crucial for functional annotation and semi-automated data exploration.

Purpose of the Study:

  • To investigate the feasibility of automated construction of an integrated biomedical database network.
  • To develop and evaluate a method for predicting and extracting cross-references between life science databases.
  • To enhance data retrieval and support functional annotation of biological data.

Main Methods:

  • Developed a novel method to predict and extract cross-references from multiple life science databases.
  • Focused on identifying explicit and implicit links between database entities.
  • Implemented the method as a freely available tool named IDPredictor.

Main Results:

  • Demonstrated the potential for automated construction of an integrated data network.
  • Achieved promising results in predicting and extracting cross-references.
  • The IDPredictor tool shows effectiveness in improving data integration.

Conclusions:

  • Automated cross-reference prediction is a viable approach to integrate distributed biomedical knowledge.
  • The developed method and tool (IDPredictor) offer a significant advancement for bioinformatics data management.
  • Further research can build upon these findings to create more comprehensive biomedical information systems.