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Support Vector Machine Approach to Separate Control and Breast Cancer Serum Samples
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Thang V Pham
Veröffentlicht/Copyright:
21. Februar 2008
The paper presents two analyzes of the MALDI-TOF mass spectrometry dataset. Both analyzes use the support vector machine as a tool to build a prediction model. The first analysis which is our contribution to the competition uses the given spectra data without further processing. In the second analysis, we employed an additional preprocessing step consisting of peak detection, peak alignment and feature selection based on statistical tests. The experimental results suggest that the preprocessing step with feature selection improves prediction accuracy.
Published Online: 2008-2-21
©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
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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
