@article{DahlkeKutyniokMaassetal.2006, author = {Dahlke, S. and Kutyniok, Gitta and Maass, Peter and Sagiv, C. and Stark, H.-G. and Teschke, G.}, title = {The Uncertainty Principle Associated with the Continuous Shearlet Transform}, series = {to appear}, journal = {to appear}, year = {2006}, language = {en} } @article{DahlkeKutyniokSteidletal.2007, author = {Dahlke, S. and Kutyniok, Gitta and Steidl, G. and Teschke, G.}, title = {Shearlet coorbit spaces and associated Banach frames}, series = {Bericht Nr. 2007-5, Philipps-Universit{\"a}t Marburg}, journal = {Bericht Nr. 2007-5, Philipps-Universit{\"a}t Marburg}, year = {2007}, language = {en} } @misc{WernerAurzadaBleyetal., author = {Werner, Axel and Aurzada, Frank and Bley, Andreas and Eisenbl{\"a}tter, Andreas and Geerdes, Hans-Florian and Guillemard, Mijail and Kutyniok, Gitta and Philipp, Friedrich and Raack, Christian and Scheutzow, Michael}, title = {Mathematics for telecommunications}, series = {MATHEON - Mathematics for Key Technologies}, volume = {1}, journal = {MATHEON - Mathematics for Key Technologies}, editor = {Deuflhard, Peter and Gr{\"o}tschel, Martin and H{\"o}mberg, Dietmar and Horst, Ulrich and Kramer, J{\"u}rg and Mehrmann, Volker and Polthier, Konrad and Schmidt, Frank and Sch{\"u}tte, Christof and Skutella, Martin and Sprekels, J{\"u}rgen}, edition = {EMS Series in Industrial and Applied Mathematics}, publisher = {European Mathematical Society}, doi = {10.4171/137}, pages = {75 -- 89}, language = {en} } @article{ConradGenzelCvetkovicetal., author = {Conrad, Tim and Genzel, Martin and Cvetkovic, Nada and Wulkow, Niklas and Vybiral, Jan and Kutyniok, Gitta and Sch{\"u}tte, Christof}, title = {Sparse Proteomics Analysis - a compressed sensing-based approach for feature selection and classification of high-dimensional proteomics mass spectrometry data}, series = {BMC Bioinformatics}, volume = {18}, journal = {BMC Bioinformatics}, number = {160}, doi = {10.1186/s12859-017-1565-4}, pages = {1 -- 20}, abstract = {Motivation: 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 how MS spectra dier 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 to noise and outliers, and the identied 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 to identify a minimal discriminating set of features from mass spectrometry data-sets. We show how our method performs on artificial and real-world data-sets.}, language = {en} }