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Case-Control Breast Cancer Study of MALDI-TOF Proteomic Mass Spectrometry Data on Serum Samples
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Martijn P. J. van der Werff
Published/Copyright:
January 25, 2008
We introduce mass spectrometry proteomic research for diagnosis from a clinical perspective, with special reference to early-stage breast cancer detection. The nature of SELDI and MALDI mass spectrometric measurement is discussed. We explain how the mass spectral data arising from this technology may be viewed as a new data type. Some of the properties of the data are discussed and we show how such spectra may be interpreted. Sample preprocessing for mass spectrometry is introduced and a literature review of research in clinical proteomics is presented. Finally, we provide a detailed description of the study design on the breast cancer case-control study which is investigated in this special issue.
Published Online: 2008-1-25
©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
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Articles in the same Issue
- Editorial
- International Competition on Mass Spectrometry Proteomic Diagnosis
- Introduction Paper
- Case-Control Breast Cancer Study of MALDI-TOF Proteomic Mass Spectrometry Data on Serum Samples
- Organizing a Competition on Clinical Mass Spectrometry Based Proteomic Diagnosis
- Competition Paper
- Application of the Random Forest Classification Method to Peaks Detected from Mass Spectrometric Proteomic Profiles of Cancer Patients and Controls
- Developing a Discrimination Rule between Breast Cancer Patients and Controls Using Proteomics Mass Spectrometric Data: A Three-Step Approach
- Principal Component Discriminant Analysis
- Classification of Breast Cancer versus Normal Samples from Mass Spectrometry Profiles Using Linear Discriminant Analysis of Important Features Selected by Random Forest
- A Classification Model for the Leiden Proteomics Competition
- Empirical Bayes Logistic Regression
- Autocorrelated Logistic Ridge Regression for Prediction Based on Proteomics Spectra
- Support Vector Machine Approach to Separate Control and Breast Cancer Serum Samples
- A Cross-Validation Study to Select a Classification Procedure for Clinical Diagnosis Based on Proteomic Mass Spectrometry
- Clinical Mass Spectrometry Proteomic Diagnosis by Conformal Predictors
- Article
- Assessing the Validity Domains of Graphical Gaussian Models in Order to Infer Relationships among Components of Complex Biological Systems
- Assessment
- Breast Cancer Diagnosis from Proteomic Mass Spectrometry Data: A Comparative Evaluation
Articles in the same Issue
- Editorial
- International Competition on Mass Spectrometry Proteomic Diagnosis
- Introduction Paper
- Case-Control Breast Cancer Study of MALDI-TOF Proteomic Mass Spectrometry Data on Serum Samples
- Organizing a Competition on Clinical Mass Spectrometry Based Proteomic Diagnosis
- Competition Paper
- Application of the Random Forest Classification Method to Peaks Detected from Mass Spectrometric Proteomic Profiles of Cancer Patients and Controls
- Developing a Discrimination Rule between Breast Cancer Patients and Controls Using Proteomics Mass Spectrometric Data: A Three-Step Approach
- Principal Component Discriminant Analysis
- Classification of Breast Cancer versus Normal Samples from Mass Spectrometry Profiles Using Linear Discriminant Analysis of Important Features Selected by Random Forest
- A Classification Model for the Leiden Proteomics Competition
- Empirical Bayes Logistic Regression
- Autocorrelated Logistic Ridge Regression for Prediction Based on Proteomics Spectra
- Support Vector Machine Approach to Separate Control and Breast Cancer Serum Samples
- A Cross-Validation Study to Select a Classification Procedure for Clinical Diagnosis Based on Proteomic Mass Spectrometry
- Clinical Mass Spectrometry Proteomic Diagnosis by Conformal Predictors
- Article
- Assessing the Validity Domains of Graphical Gaussian Models in Order to Infer Relationships among Components of Complex Biological Systems
- Assessment
- Breast Cancer Diagnosis from Proteomic Mass Spectrometry Data: A Comparative Evaluation
