title: Sparse Proteomics Analysis - a compressed sensing-based approach for feature selection and classification of high-dimensional proteomics mass spectrometry data.
abstract: BACKGROUND
  High-throughput proteomics techniques, such as mass spectrometry (MS)-based approaches, produce very high-dimensional data-sets. In a clinical setting one is often interested in how mass spectra differ between patients of different classes, for example spectra from healthy patients vs. spectra from patients having a particular disease. Machine learning algorithms are needed to (a) identify these discriminating features and (b) classify unknown spectra based on this feature set. Since the acquired data is usually noisy, the algorithms should be robust against noise and outliers, while the identified feature set should be as small as possible.
  
  
  RESULTS
  We present a new algorithm, Sparse Proteomics Analysis (SPA), based on the theory of compressed sensing that allows us to identify a minimal discriminating set of features from mass spectrometry data-sets. We show (1) how our method performs on artificial and real-world data-sets, (2) that its performance is competitive with standard (and widely used) algorithms for analyzing proteomics data, and (3) that it is robust against random and systematic noise. We further demonstrate the applicability of our algorithm to two previously published clinical data-sets.
authors.affiliation: Department of Mathematics, Freie Universität Berlin, Arnimallee 6, Berlin, Germany. conrad@math.fu-berlin.de.|Zuse Institute Berlin, Takustr. 7, Berlin, Germany. conrad@math.fu-berlin.de.
authors.forename: Tim O F
authors.identifier: http://orcid.org/0000-0002-5590-5726
authors.initials: TO
authors.lastname: Conrad
authors.affiliation: Department of Mathematics, Technische Universität Berlin, Düsternbrooker Weg 20, Berlin, Germany.
authors.forename: Martin
authors.initials: M
authors.lastname: Genzel
authors.affiliation: Department of Mathematics, Freie Universität Berlin, Arnimallee 6, Berlin, Germany.
authors.forename: Nada
authors.initials: N
authors.lastname: Cvetkovic
authors.affiliation: Department of Mathematics, Freie Universität Berlin, Arnimallee 6, Berlin, Germany.
authors.forename: Niklas
authors.initials: N
authors.lastname: Wulkow
authors.affiliation: Center of Laboratory Medicine, Inselspital - Bern University Hospital, Düsternbrooker Weg 20, Bern, 24105, Switzerland.
authors.forename: Alexander
authors.initials: A
authors.lastname: Leichtle
authors.affiliation: Department of Mathematical Analysis, Charles University, Düsternbrooker Weg 20, Prague, Czech Republic.
authors.forename: Jan
authors.initials: J
authors.lastname: Vybiral
authors.affiliation: Department of Mathematics, Technische Universität Berlin, Düsternbrooker Weg 20, Berlin, Germany.
authors.forename: Gitta
authors.initials: G
authors.lastname: Kutyniok
authors.affiliation: Department of Mathematics, Freie Universität Berlin, Arnimallee 6, Berlin, Germany.|Zuse Institute Berlin, Takustr. 7, Berlin, Germany.
authors.forename: Christof
authors.initials: C
authors.lastname: Schütte
mesh_terms: D000465:Algorithms; D016022:Case-Control Studies; D003198:Computer Simulation; D016208:Databases, Factual; D006801:Humans; D000069550:Machine Learning; D008962:Models, Theoretical; D010190:Pancreatic Neoplasms / Q000175:diagnosis / Q000235:genetics; D040901:Proteomics / Q000379:methods*; D015203:Reproducibility of Results; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization / Q000379:methods*
keywords: Biomarker; Classification; Clinical data; Compressed sensing; Feature selection; Machine learning; Mass spectrometry; Proteomics; Sparsity