Pages that link to "Item:Q713690"
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The following pages link to Feature selection and machine learning with mass spectrometry data for distinguishing cancer and non-cancer samples (Q713690):
Displaying 12 items.
- Feature extraction for proteomics imaging mass spectrometry data (Q262375) (← links)
- An OLS-based predictor test for a single-index model for predicting transcription rate from histone acetylation level (Q734691) (← links)
- Simultaneous classification and relevant feature identification in high-dimensional spaces: Application to molecular profiling data (Q947408) (← links)
- A new genetic algorithm in proteomics: feature selection for SELDI-TOF data (Q1023781) (← links)
- Machine learning techniques combined with dose profiles indicate radiation response biomarkers (Q2314538) (← links)
- Reproducibility of biomarker identifications from mass spectrometry proteomic data in cancer studies (Q2324977) (← links)
- Developing a discrimination rule between breast cancer patients and controls using proteomics mass spectrometric data: a three-step approach (Q2864014) (← links)
- Classification of breast cancer versus normal samples from mass spectrometry profiles using linear discriminant analysis of important features selected by random forest (Q2864016) (← links)
- Support vector machine approach to separate control and breast cancer serum samples (Q2864022) (← links)
- Artificial Intelligence and Soft Computing - ICAISC 2004 (Q4666430) (← links)
- Predicting Patient Survival from Proteomic Profile using Mass Spectrometry Data: An Empirical Study (Q4921574) (← links)
- Detection of cancer-specific markers amid massive mass spectral data (Q5460820) (← links)
