Skip to main content
Article thumbnail
Location of Repository

Bias, Randomization, and Ovarian Proteomic Data: A Reply to “Producers and Consumers”

By Keith A. Baggerly, Kevin R. Coombes and Jeffrey S. Morris

Abstract

Proteomic patterns derived from mass spectrometry have recently been put forth as potential biomarkers for the early diagnosis of cancer. This approach has generated much excitement, particularly as initial results reported on SELDI profiling of serum suggested that near perfect sensitivity and specificity could be achieved in diagnosing ovarian cancer. However, more recent reports have suggested that much of the observed structure could be due to the presence of experimental bias. A rebuttal to the findings of bias, subtitled “Producers and Consumers”, lists several objections. In this paper, we attempt to address these objections. While we continue to find evidence of experimental bias, we emphasize that the problems found are associated with experimental design and processing, and can be avoided in future studies

Topics: Point/Counter-Point
Publisher: Libertas Academica
OAI identifier: oai:pubmedcentral.nih.gov:2657654
Provided by: PubMed Central
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://www.pubmedcentral.nih.g... (external link)
  • Suggested articles

    Citations

    1. (2003). A data review and re-assessment of ovarian cancer serum proteomic profi ling”. BMC Bioinformatics,
    2. (2003). Cancer diagnosis using proteomic patterns”, Expert Rev.
    3. (2002). Design and implementation of microarray gene expression markup language (MAGE-ML)”. Genome Biol.,
    4. (2004). High-resolution serum proteomic patterns for ovarian cancer detection”,
    5. (2004). High-resolution serum proteomic patterns for ovarian cancer detection”. Endocrine-Related Cancer,
    6. (2004). Highresolution serum proteomic features for ovarian cancer detection”,
    7. (2001). Minimum information about a microarray experiment (MIAME)-toward standards for microarray data”.
    8. (2004). Ovarian cancer detection by logical analysis of proteomic data”.
    9. (2005). Signal in Noise: Evaluating Reported Reproducibility of Serum Proteomic Tests for Ovarian Cancer”.

    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.