>>Published online before print December 1, 2003
Proc. Natl. Acad. Sci. USA, 10.1073/pnas.2532248100
Statistics
Detection of cancer-specific markers amid massive mass spectral data
( discriminant analysis | random field | resampling | statgram )
Wei Zhu *, Xuena Wang *, Yeming Ma , Manlong Rao *, James Glimm *, and John S. Kovach ¶
*Department of Applied Mathematics and Statistics, and ¶Long Island Cancer Center, State University of New York, Stony Brook, NY 11794; and Medical Department, and Center for Data Intensive Computing, Brookhaven National Laboratory, Upton, NY 11973
Edited by Richard V. Kadison, University of Pennsylvania, Philadelphia, PA, and approved October 8, 2003 (received for review April 16, 2003)
We propose a comprehensive pattern recognition procedure that will achieve best discrimination between two or more sets of subjects with data in the same coordinate system. Applying the procedure to MS data of proteomic analysis of serum from ovarian cancer patients and serum from cancer-free individuals in the Food and Drug Administration/National Cancer Institute Clinical Proteomics Database, we have achieved perfect discrimination (100% sensitivity, 100% specificity) of patients with ovarian cancer, including early-stage disease, from normal controls for two independent sets of data. Our procedure identifies the best subset of proteomic biomarkers for optimal discrimination between the groups and appears to have higher discriminatory power than other methods reported to date. For large-scale screening for diseases of relatively low prevalence such as ovarian cancer, almost perfect specificity and sensitivity of the detection system is critical to avoid unmanageably high numbers of false-positive cases.<<
It'll be interesting to see if these folks have a unique algorithm that would be usable (licensable?) by the Ciphergens and Correologics of the world. I would guess phones are ringing in the authors' offices.
Cheers, Tuck |
