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

Improvement of SVM Algorithm for Microarray Analysis Using Intelligent Parameter Selection.

John Phan1, Richard Moffitt, Jennifer Dale

  • 1Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, 313 Ferst Drive Atlanta, GA, USA 30324.

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
|February 7, 2007
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

Remote sensing data and machine learning models estimate sorghum grain yield in a plant breeding program.

Plant phenomics (Washington, D.C.)·2026
Same author

Receptor basis of unusual tissue tropism of avian influenza H5N1 clade 2.3.4.4b virus in cattle.

Science advances·2026
Same author

Safety, immunogenicity, and dose ranging of a bivalent respiratory syncytial virus and human metapneumovirus glycoprotein F-molecular clamp vaccine candidate in older adults: 1-month interim analysis of an ongoing randomised, observer-blind, placebo- and active-controlled, phase 1 trial.

Vaccine·2026
Same author

Stepwise loss of complexity in hagfish eyes prior to deep sea colonization.

Biology letters·2026
Same author

Viral mimicry redirects immunosuppressed colorectal tumour landscapes towards a proinflammatory and CMS1-like regenerative state.

Communications biology·2026
Same author

Physiological and Yield Responses of Peanut (<i>Arachis hypogaea</i> L.) Genotypes Under Well-Watered and Water-Stressed Conditions.

Plants (Basel, Switzerland)·2026

Support vector machines (SVM) effectively identify genetic markers in high-dimensional microarray data for diseases like renal cell carcinoma (RCC). This study optimizes SVM parameters and kernels for accurate disease diagnosis and prognosis.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Genetic marker identification is vital for disease diagnosis, prognosis, and treatment.
  • High-dimensional microarray data presents challenges for traditional marker identification methods.

Purpose of the Study:

  • To apply and evaluate supervised classification, specifically Support Vector Machines (SVM), for genetic marker identification in high-dimensional microarray data.
  • To identify optimal SVM parameters and kernel functions for accurate classification of disease subtypes.

Main Methods:

  • Utilized Support Vector Machines (SVM), a supervised classification technique, on high-dimensional microarray data.
  • Compared various linear and nonlinear SVM kernel functions.
  • Tested SVM performance across a range of parameters and normalization schemes.

Related Experiment Videos

  • Applied the methodology to a case study of renal cell carcinoma (RCC).

    • Main Results:

    • Demonstrated SVM's capability to identify biologically relevant markers in high-dimensional data.
    • Identified optimal SVM parameters and kernel functions for differentiating RCC subtypes.
    • Empirical results confirmed the effectiveness of the optimized SVM classifier.

    Conclusions:

    • SVM is a powerful tool for genetic marker identification in complex biological datasets.
    • Optimized SVM classifiers can significantly aid in the diagnosis and prognosis of diseases like RCC.
    • This approach facilitates the discovery of predictive markers for improved patient outcomes.