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

Adding semantics to gene expression profiles: new tools for drug discovery.

V Manganaro1, S Paratore, E Alessi

  • 1Institute of Neurological Sciences, Italian National Research Council, 95123 Catania, Italy.

Current Medicinal Chemistry
|May 17, 2005
PubMed
Summary

Semantic clustering of gene expression data offers a novel approach to identify effective drug targets. This method enhances knowledge extraction from genomic data for improved drug discovery and development processes.

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

The novel Mechanical Ventilator Milano for the COVID-19 pandemic.

Physics of fluids (Woodbury, N.Y. : 1994)·2021
Same author

Endothelin-1 Induces Degeneration of Cultured Motor Neurons Through a Mechanism Mediated by Nitric Oxide and PI3K/Akt Pathway.

Neurotoxicity research·2017
Same author

Advances in the FTU collective Thomson scattering system.

The Review of scientific instruments·2016
Same author

Transcriptional landscapes at the intersection of neuronal apoptosis and substance P-induced survival: exploring pathways and drug targets.

Cell death discovery·2016
Same author

Cracking the code of neuronal apoptosis and survival.

Cell death & disease·2015
Same author

Note: fast neutron efficiency in CR-39 nuclear track detectors.

The Review of scientific instruments·2015

Area of Science:

  • Genomics
  • Bioinformatics
  • Drug Discovery

Background:

  • Gene expression profiling reveals numerous potential drug targets for various diseases.
  • Selecting optimal targets with therapeutic utility from vast datasets presents a significant challenge.
  • Traditional numerical clustering of gene expression data often falls short in inferring gene/protein roles for drug development.

Purpose of the Study:

  • To explore the application of semantic clustering for enhanced knowledge extraction from genomic data.
  • To improve the process of identifying and selecting viable drug targets.
  • To demonstrate the utility of gene ontologies in drug discovery.

Main Methods:

  • Reviewing and illustrating clustering methods based on semantic characteristics.

Related Experiment Videos

  • Utilizing gene ontologies for data interpretation.
  • Comparing semantic clustering with traditional numerical clustering approaches.

    • Main Results:

    • Semantic clustering methods can extract more meaningful biological insights compared to numerical methods.
    • This approach aids in a more effective selection of drug targets with higher therapeutic potential.
    • Gene ontologies provide a valuable framework for understanding gene function and relationships.

    Conclusions:

    • Clustering based on semantic features, particularly gene ontologies, offers a powerful strategy for drug target identification.
    • This approach enhances the drug discovery pipeline by leveraging genomic data more effectively.
    • Future drug development can benefit from integrating semantic analysis into gene expression data interpretation.