Silicon Investor (SI) -- The First Internet Community

STOCKTALK

We've detected that you're using an ad content blocking browser plug-in or feature. Ads provide a critical source of revenue to the continued operation of Silicon Investor. We ask that you disable ad blocking while on Silicon Investor in the best interests of our community. If you are not using an ad blocker but are still receiving this message, make sure your browser's tracking protection is set to the 'standard' level.

Biotech / Medical : Ciphergen Biosystems(CIPH): -- Ignore unavailable to you. Want to Upgrade?

To: tuck who wrote (119)	5/27/2003 10:36:25 AM
From: tuck	Respond to of 510

>>Biometrics 2003 Mar;59(1):143-51 Data reduction using a discrete wavelet transform in discriminant analysis of very high dimensionality data. Qu Y, Adam BL, Thornquist M, Potter JD, Thompson ML, Yasui Y, Davis J, Schellhammer PF, Cazares L, Clements M, Wright GL Jr, Feng Z. Cancer Prevention Research Program, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave. N. MP-702 Seattle, Washington, USA. yqu@fhcrc.org We present a method of data reduction using a wavelet transform in discriminant analysis when the number of variables is much greater than the number of observations. The method is illustrated with a prostate cancer study, where the sample size is 248, and the number of variables is 48,538 (generated using the ProteinChip technology). Using a discrete wavelet transform, the 48,538 data points are represented by 1271 wavelet coefficients. Information criteria identified 11 of the 1271 wavelet coefficients with the highest discriminatory power. The linear classifier with the 11 wavelet coefficients detected prostate cancer in a separate test set with a sensitivity of 97% and specificity of 100%.<< Cheers, Tuck

To: tuck who wrote (119)	5/27/2003 11:13:45 AM
From: tuck	Read Replies (1) \| Respond to of 510

Found the abstract, but still no specific mention of the type of MS used, or whether or not ProteinChips were used (they could be in conjunction with the MS interface available for ABI mass specs): >>Multiple high-resolution serum proteomic patterns for ovarian cancer detection. Timothy Daniel Veenstra, Thomas Conrads, Emmanuel Petricoin, Vincent Fusaro, Sally Ross, Lance Liotta, Elise Kohn, Seth Steinberg, Gordon Whiteley, J. Carl Barrett, David Fishman, Peter Levine, Tzong-Hao Chen, Ben Hitt, Wesley Wiggins, Biomedical Proteomics Program, Frederick, MD; Food and Drug Administration-National Cancer Institute Clinical Proteomics Program, Bethesda, MD; National Cancer Institute, Center for Cancer Research, Bethesda, MD; National Cancer Institute, Clinical Proteomic Reference Laboratory, Bethesda, MD; Northwestern University Medical School, Chicago, IL; Correlogics Systems Inc., Bethesda, MD. Serum proteomic pattern diagnostics is an emerging clinical paradigm that typically utilizes low-resolution mass spectrometry and generates a single set of biomarker classifiers. In the present study we utilized a well-controlled serum study set (n = 248) from women being followed and evaluated for the presence of ovarian cancer to extend serum proteomic pattern analysis to a higher resolution platform to explore the existence of multiple distinct highly accurate diagnostic patterns present in the same mass spectrum. Here we report that multiple highly accurate diagnostic proteomic patterns exist within human sera mass spectra. Using high-resolution mass spectral data, at least 59 different patterns were discovered that achieve greater than 85% sensitivity and specificity in testing and validation. Four of the 59 patterns exhibit 100% sensitivity and specificity in validation. Only 40 models with a sensitivity and specificity greater than 80% are found in data acquired from the same samples analyzed with a more common low-resolution mass spectrometer and no patterns were validated as 100% sensitive and specific. The sensitivity and specificity of diagnostic patterns generated from high-resolution mass spectral data were significantly greater (P < 0.00001) than those generated from low-resolution mass spectral data. << Cheers, Tuck