Proteomics in Health and Disease Part IView this Special Issue
Research article | Open Access
Yutaka Yasui, Dale McLerran, Bao-Ling Adam, Marcy Winget, Mark Thornquist, Ziding Feng, "An Automated Peak Identification/Calibration Procedure for High-Dimensional Protein Measures From Mass Spectrometers", BioMed Research International, vol. 2003, Article ID 957834, 7 pages, 2003. https://doi.org/10.1155/S111072430320927X
An Automated Peak Identification/Calibration Procedure for High-Dimensional Protein Measures From Mass Spectrometers
Discovery of “signature” protein profiles that distinguish disease states (eg, malignant, benign, and normal) is a key step towards translating recent advancements in proteomic technologies into clinical utilities. Protein data generated from mass spectrometers are, however, large in size and have complex features due to complexities in both biological specimens and interfering biochemical/physical processes of the measurement procedure. Making sense out of such high-dimensional complex data is challenging and necessitates the use of a systematic data analytic strategy. We propose here a data processing strategy for two major issues in the analysis of such mass-spectrometry-generated proteomic data: (1) separation of protein “signals” from background “noise” in protein intensity measurements and (2) calibration of protein mass/charge measurements across samples. We illustrate the two issues and the utility of the proposed strategy using data from a prostate cancer biomarker discovery project as an example.
Copyright © 2003 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.