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：
暂无
中图分类号：
学科分类号：
摘要：
引用
收藏
相关论文
共 226 条
[1]  
Aebersold R(2003)Mass spectrometry-based proteomics Nature 422 198-207
[2]  
Mann M(2006)The blood peptidome: a higher dimension of information content for cancer biomarker discovery Nat Rev Cancer 6 961-7
[3]  
Petricoin EF(2004)Cancer proteomics: serum diagnostics for tumor marker discovery Ann N Y Acad Sci 1022 286-94
[4]  
Belluco C(2005)Serum proteomics profiling–a young technology begins to mature Nat Biotechnol 23 291-2
[5]  
Araujo RP(2003)Clinical proteomics: written in blood Nature 425 905-215
[6]  
Liotta LA(2003)Protein analysis on a proteomic scale Nature 422 208-20
[7]  
Rai AJ(2007)Serum and plasma proteomics Chem Rev 107 3601-48
[8]  
Chan DW(2004)MALDI: more than peptide mass fingerprints Curr Opin Mol Ther 6 239-8
[9]  
Coombes KR(2012)Application of label-free proteomics for differential analysis of lung carcinoma cell line A549 Methods Mol Biol 893 241-9
[10]  
Morris JS(2009)Serum peptidome profiling revealed platelet factor 4 as a potential discriminating peptide associated with pancreatic cancer Clin Cancer Res 15 3812-7
12345678910