Pages that link to "Item:Q2864016"
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The following pages link to Classification of breast cancer versus normal samples from mass spectrometry profiles using linear discriminant analysis of important features selected by random forest (Q2864016):
Displaying 12 items.
- Binary Markov random fields and interpretable mass spectra discrimination (Q523937) (← links)
- Feature selection and machine learning with mass spectrometry data for distinguishing cancer and non-cancer samples (Q713690) (← links)
- A new genetic algorithm in proteomics: feature selection for SELDI-TOF data (Q1023781) (← links)
- Bayesian nonparametric classification for spectroscopy data (Q1623622) (← links)
- Prototype based fuzzy classification in clinical proteomics (Q2270370) (← links)
- Case-control breast cancer study of MALDI-TOF proteomic mass spectrometry data on serum samples (Q2864011) (← links)
- Developing a discrimination rule between breast cancer patients and controls using proteomics mass spectrometric data: a three-step approach (Q2864014) (← links)
- Principal component discriminant analysis (Q2864015) (← links)
- A classification model for the Leiden proteomics competition (Q2864018) (← links)
- Support vector machine approach to separate control and breast cancer serum samples (Q2864022) (← links)
- A cross-validation study to select a classification procedure for clinical diagnosis based on proteomic mass spectrometry (Q2864025) (← links)
- Breast cancer diagnosis from proteomic mass spectrometry data: a comparative evaluation (Q2864029) (← links)
