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 Videos

Information integration in molecular bioscience.

Alexander Garcia Castro1, Yi-Ping Phoebe Chen, Mark A Ragan

  • 1ARC Centre in Bioinformatics, and Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia.

Applied Bioinformatics
|October 20, 2005
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

MULGONET: An interpretable neural network framework to integrate multi-omics data for cancer recurrence prediction and biomarker discovery.

Fundamental research·2026
Same author

Establishing the Asia & Pacific Bioinformatics Joint Congress: a historic milestone in regional bioinformatics collaboration.

Briefings in bioinformatics·2025
Same author

Utilizing Neurons to Interrogate Cancer: Integrative Analysis of Cancer Omics Data with Deep Learning Models.

IEEE reviews in biomedical engineering·2025
Same author

Editorial: The 21st Asian Pacific Bioinformatics Conference 2023.

Computational biology and chemistry·2024
Same author

Dual attention based fusion network for MCI Conversion Prediction.

Computers in biology and medicine·2024
Same author

Role of TAP1 in the identification of immune-hot tumor microenvironment and its prognostic significance for immunotherapeutic efficacy in gastric carcinoma.

Journal of gastrointestinal oncology·2024
Same journal

Statistically consistent identification of differentially expressed genes in DNA chip data over the whole expression range: relative variance method.

Applied bioinformatics·2006
Same journal

A nonparametric likelihood ratio test to identify differentially expressed genes from microarray data.

Applied bioinformatics·2006
Same journal

Simulation study of ratio calculation formulae of two-colour cDNA microarray data.

Applied bioinformatics·2006
Same journal

Alternative mRNA polyadenylation can potentially affect detection of gene expression by affymetrix genechip arrays.

Applied bioinformatics·2006
Same journal

Comparisons of annotation predictions for affymetrix GeneChips.

Applied bioinformatics·2006
Same journal

Ontology annotation treebrowser : an interactive tool where the complementarity of medical subject headings and gene ontology improves the interpretation of gene lists.

Applied bioinformatics·2006
See all related articles

Integrating molecular biosciences data requires more than sequence linking. This review explores methods for combining diverse experimental and computational information, highlighting challenges and solutions like ontologies for better data integration.

Area of Science:

  • Molecular Biosciences
  • Bioinformatics
  • Computational Biology

Background:

  • Information integration in molecular biosciences extends beyond sequence and structure.
  • Experimental protocols, computational results, annotations, and literature links are crucial for data meaning.
  • Heterogeneous data sources and semantic implications pose challenges.

Purpose of the Study:

  • To review existing approaches for integrating information in molecular biosciences.
  • To analyze technical and semantic issues in data integration.
  • To identify strengths and limitations of current platforms and developments.

Main Methods:

  • Examination of current platforms and developments in data integration.
  • Analysis of technical challenges in integrating heterogeneous data sources.

Related Experiment Videos

  • Discussion of semantic integration strategies, including ontologies and XML.

    • Main Results:

    • Two platforms and six developments in molecular bioscience data integration were assessed.
    • Strengths and limitations of various integration strategies were identified.
    • Key unsolved problems in information integration were highlighted.

    Conclusions:

    • Semantic integration using ontologies, XML, and graphical user interfaces are potential solutions.
    • Addressing technical and semantic challenges is key for effective information integration.
    • Further development is needed to overcome existing problems in molecular bioscience data integration.