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

Exploiting scale-free information from expression data for cancer classification.

Alexey V Antonov1, Igor V Tetko, Denis Kosykh

  • 1GSF National Research Center for Environment and Health, Institute for Bioinformatics, Ingolstädter Landstrasse 1, D-85764 Neuherberg, Germany. antonov@gsf.de

Computational Biology and Chemistry
|July 26, 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

SangsterLogP - the largest publicly available dataset of logP values.

Scientific data·2026
Same author

2nd EUOS/SLAS joint challenge: Prediction of spectral properties of compounds.

SLAS technology·2025
Same author

Introducing the Inaugural Early Career Board for <i>Chemical Research in Toxicology</i>.

Chemical research in toxicology·2025
Same author

Advanced machine learning for innovative drug discovery.

Journal of cheminformatics·2025
Same author

Advancing Human and Environmental Safety Science Using <i>In Silico</i> Methods.

Chemical research in toxicology·2025
Same author

Which Modern AI Methods Provide Accurate Predictions of Toxicological End Points? Analysis of Tox24 Challenge Results.

Chemical research in toxicology·2025
Same journal

An interpretable framework for cancer drug response prediction using integrated drug and multi-omics data with a hybrid Bi-LSTM-GRU network.

Computational biology and chemistry·2026
Same journal

SegMWB: A lightweight deep learning framework for microscopic image classification.

Computational biology and chemistry·2026
Same journal

Protein dynamic simulations: From early inception to clinical translation over half a century.

Computational biology and chemistry·2026
Same journal

Integrated omics and virtual screening predict Tabularin as a dual inhibitor of the prognostic microRNAs mir-19a and mir-32 in colorectal cancer.

Computational biology and chemistry·2026
Same journal

In silico characterization of acetyl-CoA carboxylase from Staphylococcus aureus and Escherichia coli: A comparative analysis.

Computational biology and chemistry·2026
Same journal

An optimized cascaded transformer with progressive attention for lung and colon cancer diagnosis from histopathological images.

Computational biology and chemistry·2026
See all related articles

This study introduces a new method for gene expression data analysis using pairwise ratios, which are more stable than absolute intensities. This approach improves classification accuracy and is less affected by data processing variations.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Gene expression data analysis often relies on gene intensity variability.
  • This variability is sensitive to data preprocessing, impacting analytical conclusions.
  • Existing methods may not be robust to variations in data processing pipelines.

Purpose of the Study:

  • To propose a novel approach for gene expression data classification.
  • To leverage scale-free, relative gene expression information for improved analysis.
  • To develop a method robust to variations in data processing.

Main Methods:

  • Utilizing pairwise ratios of gene expression values instead of absolute intensities.
  • Focusing on relative gene expression levels within samples.

Related Experiment Videos

  • Testing the proposed method on publicly available gene expression datasets.

    • Main Results:

    • The proposed method demonstrates superior classification results compared to traditional approaches.
    • Pairwise ratios proved to be a stable and effective feature for classification.
    • The approach showed robustness against variations in data processing.

    Conclusions:

    • Relative gene expression analysis using pairwise ratios offers a more stable and accurate classification method.
    • This approach enhances the reliability of gene expression data analysis.
    • The findings suggest a significant improvement in bioinformatics classification strategies.