The Challenges in Blood Proteomic Biomarker Discovery

  • Guangxu Jin
  • Xiaobo ZhouEmail author
  • Honghui Wang
  • Stephen T. C. Wong
Part of the Applied Bioinformatics and Biostatistics in Cancer Research book series (ABB)


Although discovering proteomic biomarker by using mass spectrometry technique is promising, its rate of introducing proteomic biomarker approved by the US Food and Drug Administration is falling every year and nearly 1 per year on an average since 1998. Apparently, there is a big gap between biomarker discovery and biomarker validation. Here, we reviewed the challenges appearing in the three key stages for the pipeline of proteomic biomarker, that is, blood sample preparation, bioinformatics algorithms for biomarker candidate discovery, and validation and clinical application of proteomic biomarkers. To analyze and explain the reasons for the gap between biomarker discovery and validation, we covered areas ranging from the techniques/methods used in biomarker discovery and their related biological backgrounds to the existing problems in these techniques/methods.


Feature Selection Linear Discriminant Analysis Discrete Wavelet Transform Feature Subset Peak Detection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This research is funded by the Bioinformatics Core Research Grant at The Methodist Research Institute, Cornell University. Dr. Zhou is partially funded by The Methodist Hospital Scholarship Award. He and Dr. Wong are also partially funded by NIH grants R01LM08696, R01LM009161, and R01AG028928. The authors have declared no conflict of interest.


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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Guangxu Jin
  • Xiaobo Zhou
    • 1
    Email author
  • Honghui Wang
  • Stephen T. C. Wong
  1. 1.HoustonUSA

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