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The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.

Advances in proteomics data analysis and display using an accurate mass and time tag approach

Open access
Zimmer, Jennifer S.D., Monroe, Matthew E., Qian, Wei‐Jun, Smith, Richard D.·2006·Mass Spectrometry Reviews

Abstract

Proteomics has recently demonstrated utility for increasing the understanding of cellular processes on the molecular level as a component of systems biology approaches and for identifying potential biomarkers of various disease states. The large amount of data generated by utilizing high efficiency (e.g., chromatographic) separations coupled with high mass accuracy mass spectrometry for high‐throughput proteomics analyses presents challenges related to data processing, analysis, and display. This review focuses on recent advances in nanoLC‐FTICR‐MS‐based proteomics approaches and the accompanying data processing tools that have been developed to display and interpret the large volumes of data being produced. © 2006 Wiley Periodicals, Inc., Mass Spec Rev 25:450–482, 2006

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Metadata

Authors
Zimmer, J. S. D., Monroe, M. E., Qian, W., & Smith, R. D.
Venue
Mass Spectrometry Reviews
Publisher
Wiley
Published
2006-01-20
Volume
25
Issue
3
Pages
450-482
ISSN
  • 0277-7037 (ISSN-L)
  • 1098-2787
OpenAlex
W2101938218
Cited by
304
References
5
FWCI
14.05
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Advances in proteomics data analysis and display using an accurate mass and time tag approach. https://app.cochat.ai/papers/10.1002/mas.20071
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Zimmer, J. S. D., Monroe, M. E., Qian, W., & Smith, R. D. (2006). Advances in proteomics data analysis and display using an accurate mass and time tag approach. Mass Spectrometry Reviews. https://doi.org/10.1002/mas.20071

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.