A Classification Model for the Leiden Proteomics Competition
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Huub C. J. Hoefsloot
A strategy is presented to build a discrimination model in proteomics studies. The model is built using cross-validation. This cross-validation step can simply be combined with a variable selection method, called rank products. The strategy is especially suitable for the low-samples-to-variables-ratio (undersampling) case, as is often encountered in proteomics and metabolomics studies. As a classification method, Principal Component Discriminant Analysis is used; however, the methodology can be used with any classifier. A data set containing serum samples from breast cancer patients and healthy controls is analysed. Double cross-validation shows that the sensitivity of the model is 82% and the specificity 86%. Potential putative biomarkers are identified using the variable selection method. In each cross-validation loop a classification model is built. The final classification uses a majority voting scheme from the ensemble classifier.
©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
