Breast Cancer Diagnosis from Proteomic Mass Spectrometry Data: A Comparative Evaluation
-
David J Hand
The performance results of a wide range of different classifiers applied to proteomic mass spectra data, in a blind comparative assessment organised by Bart Mertens, are reviewed. The different approaches are summarised, issues of how to evaluate and compare the predictions are described, and the results of the different methods are examined. Although the different methods perform differently, their rank ordering varies according to how one measures performance, so that one cannot draw unequivocal conclusions about which is 'best.' Instead, it is clear that what matters is not the method by itself, but the interaction of method and user - the degree of sophistication of the user with a method. Nevertheless, such competitions do serve the useful role of setting (constantly improving) baselines against which new researchers can pit their wits and methods, as well as providing standards against which new methods should be assessed.
©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
Artikel in diesem Heft
- 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
Artikel in diesem Heft
- 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
