Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-9272
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Main Title: Sparse Proteomics Analysis – a compressed sensing-based approach for feature selection and classification of high-dimensional proteomics mass spectrometry data
Author(s): Conrad, Tim O. F.
Genzel, Martin
Cvetkovic, Nada
Wulkow, Niklas
Leichtle, Alexander
Vybiral, Jan
Kutyniok, Gitta
Schütte, Christof
Type: Article
URI: https://depositonce.tu-berlin.de/handle/11303/10310
http://dx.doi.org/10.14279/depositonce-9272
License: https://creativecommons.org/licenses/by/4.0/
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.
Subject(s): machine learning
feature selection
classification
compressed sensing
sparsity
proteomics
mass spectrometry
clinical data
biomarker
Issue Date: 2017
Date Available: 14-Nov-2019
Language Code: en
DDC Class: 004 Datenverarbeitung; Informatik
570 Biowissenschaften; Biologie
610 Medizin und Gesundheit
Journal Title: BMC Bioinformatics
Publisher: Springer Nature
Volume: 18
Issue: 1
Article Number: 160
Publisher DOI: 10.1186/s12859-017-1565-4
EISSN: 1471-2105
TU Affiliation(s): Fak. 2 Mathematik und Naturwissenschaften » Inst. Mathematik » FG Angewandte Funktionalanalysis
Appears in Collections:Technische Universität Berlin » Publications

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