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Classification of Breast Cancer versus Normal Samples from Mass Spectrometry Profiles Using Linear Discriminant Analysis of Important Features Selected by Random Forest
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Somnath Datta
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February 19, 2008
We present our approach to classifying the processed proteomic data that were made available to the participants of the classification competition. Although classification of the spectra was the goal of the competition we feel that proteomic applications to cancer biomarker studies make certain additional demands. For example, one such requirement should be identification of certain features which collectively could differentiate the two groups of samples. Also ideally, the size of the feature set should be small. To that end we propose a linear discriminant classifier based on nine m/z intensity values. Construction and performance of this classifier are discussed.
Published Online: 2008-2-19
©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
