Pitfalls in the Use of DNA Microarray Data for Diagnostic and Prognostic Classification

Clicks: 3
ID: 298898
2002
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #175 of 335 articles by views in JNCI Journal of the National Cancer Institute

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 335 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
DNA microarrays have made it possible to estimate the level of expression of thousands of genes for a sample of cells. Although biomedical investigators have been quick to adopt this powerful new research tool, accurate analysis and interpretation of the data have provided unique challenges. Indeed, many investigators are not experienced in the analytical steps needed to convert tens of thousands of noisy data points into reliable and interpretable biologic information. Although some investigators recognize the importance of collaborating with experienced biostatisticians to analyze microarray data, the number and availability of experienced biostatisticians is inadequate. Consequently, investigators are using available software to analyze their data, many seemingly without knowledge of potential pitfalls. Because of serious problems associated with the analysis and reporting of some DNA microarray studies, there is great interest in guidance on valid and effective methods for analysis of DNA microarray data. The design and analysis strategy for a DNA microarray experiment should be determined in light of the overall objectives of the study. Because DNA microarrays are used for a wide variety of objectives, it is not feasible to address the entire range of design and analysis issues in this commentary. Here, we address statistical issues that arise from the use of DNA microarrays for an important group of objectives that has been called “class prediction” (1). Class prediction includes derivation of predictors of prognosis, response to therapy, or any phenotype or genotype defined independently of the gene expression profile.
Reference Key
openalex_W2043235003 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Richard Simon, Michael D. Radmacher, Kevin K. Dobbin, Lisa M. McShane
Journal JNCI Journal of the National Cancer Institute
Year 2002
DOI
10.1093/jnci/95.1.14
URL
https://academic.oup.com/jnci/article-pdf/95/1/14/9849348/14.pdf
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.