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

Pre-validation and inference in microarrays.

Robert J Tibshirani1, Brad Efron

  • 1Stanford University, USA. tibs@stat.stanford.edu

Statistical Applications in Genetics and Molecular Biology
|May 2, 2006
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

Post-infusion CAR T<sub>Reg</sub> cells identify patients resistant to CD19-CAR therapy.

Nature medicine·2022
Same author

Testing for a Sweet Spot in Randomized Trials.

Medical decision making : an international journal of the Society for Medical Decision Making·2021
Same author

An approach to explore for a sweet spot in randomized trials.

Journal of clinical epidemiology·2019
Same author

Increased T Cell Differentiation and Cytolytic Function in Bangladeshi Compared to American Children.

Frontiers in immunology·2019
Same author

Early detection of unilateral ureteral obstruction by desorption electrospray ionization mass spectrometry.

Scientific reports·2019
Same author

Found In Translation: a machine learning model for mouse-to-human inference.

Nature methods·2018

Comparing gene expression predictors to clinical predictors in microarray studies requires careful validation. We introduce "pre-validation" to provide a fairer comparison, reducing bias in disease outcome prediction.

Area of Science:

  • Bioinformatics
  • Genomics
  • Biostatistics

Background:

  • Microarray studies are crucial for identifying disease outcome predictors.
  • Comparing gene expression-derived predictors with clinical predictors presents validation challenges.
  • Using the same dataset for predictor derivation and comparison introduces significant bias.

Purpose of the Study:

  • To introduce and evaluate a novel technique called "pre-validation".
  • To enable a fairer comparison between gene expression and clinical predictors.
  • To address the bias inherent in comparing predictors derived from the same dataset.

Main Methods:

  • Analytical study of the proposed "pre-validation" method.
  • Application and exploration of the technique in a breast cancer study.

Related Experiment Videos

  • Statistical analysis to assess predictor performance and bias reduction.

    • Main Results:

    • The "pre-validation" technique offers a more equitable assessment of predictor performance.
    • Demonstrated reduction of bias in comparing microarray-derived and clinical predictors.
    • Successful application in a real-world breast cancer dataset.

    Conclusions:

    • "Pre-validation" is a valuable method for validating gene expression predictors against clinical standards.
    • The technique enhances the reliability of comparing different types of predictive models.
    • This approach is essential for accurate disease outcome prediction in clinical genomics.