Sparse Proteomics Analysis – a compressed sensing-based approach for feature selection and classification of high-dimensional proteomics mass spectrometry data

被引：0
作者：
Tim O. F. Conrad
Martin Genzel
Nada Cvetkovic
Niklas Wulkow
Alexander Leichtle
Jan Vybiral
Gitta Kutyniok
Christof Schütte
机构：
[1] Freie Universität Berlin,Department of Mathematics
[2] Arnimallee 6,Department of Mathematics
[3] Technische Universität Berlin,Department of Mathematical Analysis
[4] Düsternbrooker Weg 20,undefined
[5] Center of Laboratory Medicine,undefined
[6] Inselspital - Bern University Hospital,undefined
[7] Düsternbrooker Weg 20,undefined
[8] Charles University,undefined
[9] Düsternbrooker Weg 20,undefined
[10] Zuse Institute Berlin,undefined
[11] Takustr. 7,undefined
来源：
BMC Bioinformatics | / 18卷
关键词：
Machine learning; Feature selection; Classification; Compressed sensing; Sparsity; Proteomics; Mass spectrometry; Clinical data; Biomarker;
D O I：
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摘要：
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