Sparse Proteomics Analysis - a compressed sensing-based approach for feature selection and classification of high-dimensional proteomics mass spectrometry data.
Tim O. F. ConradMartin GenzelNada CvetkovicNiklas WulkowAlexander B. LeichtleJan VybíralGitta KutyniokChristof SchüttePublished in: BMC Bioinform. (2017)
Keyphrases
- compressed sensing
- high dimensional
- mass spectrometry data
- image reconstruction
- high throughput
- feature selection and classification
- biomarker discovery
- mass spectrometry
- sparse representation
- random projections
- natural images
- data analysis
- data sets
- similarity search
- pattern recognition
- support vector
- low dimensional
- signal processing
- microarray data
- dimensionality reduction
- compressive sensing
- training data
- feature extraction
- neural network