Autocorrelated Logistic Ridge Regression for Prediction Based on Proteomics Spectra

Articles in the same Issue

1. Editorial
2. International Competition on Mass Spectrometry Proteomic Diagnosis
3. Introduction Paper
4. Case-Control Breast Cancer Study of MALDI-TOF Proteomic Mass Spectrometry Data on Serum Samples
5. Organizing a Competition on Clinical Mass Spectrometry Based Proteomic Diagnosis
6. Competition Paper
7. Application of the Random Forest Classification Method to Peaks Detected from Mass Spectrometric Proteomic Profiles of Cancer Patients and Controls
8. Developing a Discrimination Rule between Breast Cancer Patients and Controls Using Proteomics Mass Spectrometric Data: A Three-Step Approach
9. Principal Component Discriminant Analysis
10. Classification of Breast Cancer versus Normal Samples from Mass Spectrometry Profiles Using Linear Discriminant Analysis of Important Features Selected by Random Forest
11. A Classification Model for the Leiden Proteomics Competition
12. Empirical Bayes Logistic Regression
13. Autocorrelated Logistic Ridge Regression for Prediction Based on Proteomics Spectra
14. Support Vector Machine Approach to Separate Control and Breast Cancer Serum Samples
15. A Cross-Validation Study to Select a Classification Procedure for Clinical Diagnosis Based on Proteomic Mass Spectrometry
16. Clinical Mass Spectrometry Proteomic Diagnosis by Conformal Predictors
17. Article
18. Assessing the Validity Domains of Graphical Gaussian Models in Order to Infer Relationships among Components of Complex Biological Systems
19. Assessment
20. Breast Cancer Diagnosis from Proteomic Mass Spectrometry Data: A Comparative Evaluation