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Alchikh M, Conrad TOF, Obermeier PE, Ma X, Schweiger B, Opota O, Rath BA. Disease Burden and Inpatient Management of Children with Acute Respiratory Viral Infections during the Pre-COVID Era in Germany: A Cost-of-Illness Study. Viruses 2024; 16:507. [PMID: 38675850 PMCID: PMC11054359 DOI: 10.3390/v16040507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 03/06/2024] [Accepted: 03/19/2024] [Indexed: 04/28/2024] Open
Abstract
Respiratory viral infections (RVIs) are common reasons for healthcare consultations. The inpatient management of RVIs consumes significant resources. From 2009 to 2014, we assessed the costs of RVI management in 4776 hospitalized children aged 0-18 years participating in a quality improvement program, where all ILI patients underwent virologic testing at the National Reference Centre followed by detailed recording of their clinical course. The direct (medical or non-medical) and indirect costs of inpatient management outside the ICU ('non-ICU') versus management requiring ICU care ('ICU') added up to EUR 2767.14 (non-ICU) vs. EUR 29,941.71 (ICU) for influenza, EUR 2713.14 (non-ICU) vs. EUR 16,951.06 (ICU) for RSV infections, and EUR 2767.33 (non-ICU) vs. EUR 14,394.02 (ICU) for human rhinovirus (hRV) infections, respectively. Non-ICU inpatient costs were similar for all eight RVIs studied: influenza, RSV, hRV, adenovirus (hAdV), metapneumovirus (hMPV), parainfluenza virus (hPIV), bocavirus (hBoV), and seasonal coronavirus (hCoV) infections. ICU costs for influenza, however, exceeded all other RVIs. At the time of the study, influenza was the only RVI with antiviral treatment options available for children, but only 9.8% of influenza patients (non-ICU) and 1.5% of ICU patients with influenza received antivirals; only 2.9% were vaccinated. Future studies should investigate the economic impact of treatment and prevention of influenza, COVID-19, and RSV post vaccine introduction.
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Affiliation(s)
- Maren Alchikh
- Vaccine Safety Initiative, 10437 Berlin, Germany; (M.A.); (P.E.O.)
- Laboratoire Chrono-Environnement, Université Bourgogne Franche-Comté, 25030 Besançon, France
- ESGREV (ESCMID Respiratory Virus Study Group), 4001 Basel, Switzerland;
| | | | - Patrick E. Obermeier
- Vaccine Safety Initiative, 10437 Berlin, Germany; (M.A.); (P.E.O.)
- ESGREV (ESCMID Respiratory Virus Study Group), 4001 Basel, Switzerland;
| | - Xiaolin Ma
- Department of Pulmonology, Capital Institute of Pediatrics, Beijing 100005, China;
| | - Brunhilde Schweiger
- Unit 17, Influenza and Other Respiratory Viruses, Department of Infectious Diseases, National Reference Centre for Influenza, Robert Koch-Institute, 13353 Berlin, Germany;
| | - Onya Opota
- ESGREV (ESCMID Respiratory Virus Study Group), 4001 Basel, Switzerland;
- Institute of Microbiology, University of Lausanne, 1011 Lausanne, Switzerland
| | - Barbara A. Rath
- Vaccine Safety Initiative, 10437 Berlin, Germany; (M.A.); (P.E.O.)
- Laboratoire Chrono-Environnement, Université Bourgogne Franche-Comté, 25030 Besançon, France
- ESGREV (ESCMID Respiratory Virus Study Group), 4001 Basel, Switzerland;
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Paltra S, Conrad TOF. Clinical Effectiveness of Ritonavir-Boosted Nirmatrelvir-A Literature Review. Adv Respir Med 2024; 92:66-76. [PMID: 38247553 PMCID: PMC10801539 DOI: 10.3390/arm92010009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 01/12/2024] [Accepted: 01/15/2024] [Indexed: 01/23/2024]
Abstract
Nirmatrelvir/Ritonavir is an oral treatment for mild to moderate COVID-19 cases with a high risk for a severe course of the disease. For this paper, a comprehensive literature review was performed, leading to a summary of currently available data on Nirmatrelvir/Ritonavir's ability to reduce the risk of progressing to a severe disease state. Herein, the focus lies on publications that include comparisons between patients receiving Nirmatrelvir/Ritonavir and a control group. The findings can be summarized as follows: Data from the time when the Delta-variant was dominant show that Nirmatrelvir/Ritonavir reduced the risk of hospitalization or death by 88.9% for unvaccinated, non-hospitalized high-risk individuals. Data from the time when the Omicron variant was dominant found decreased relative risk reductions for various vaccination statuses: between 26% and 65% for hospitalization. The presented papers that differentiate between unvaccinated and vaccinated individuals agree that unvaccinated patients benefit more from treatment with Nirmatrelvir/Ritonavir. However, when it comes to the dependency of potential on age and comorbidities, further studies are necessary. From the available data, one can conclude that Nirmatrelvir/Ritonavir cannot substitute vaccinations; however, its low manufacturing cost and easy administration make it a valuable tool in fighting COVID-19, especially for countries with low vaccination rates.
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Affiliation(s)
- Sydney Paltra
- FG Verkehrssystemplanung und Verkehrstelematik, Technische Universität Berlin, 10623 Berlin, Germany
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Melnyk K, Weimann K, Conrad TOF. Understanding microbiome dynamics via interpretable graph representation learning. Sci Rep 2023; 13:2058. [PMID: 36739319 PMCID: PMC9899240 DOI: 10.1038/s41598-023-29098-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 01/30/2023] [Indexed: 02/06/2023] Open
Abstract
Large-scale perturbations in the microbiome constitution are strongly correlated, whether as a driver or a consequence, with the health and functioning of human physiology. However, understanding the difference in the microbiome profiles of healthy and ill individuals can be complicated due to the large number of complex interactions among microbes. We propose to model these interactions as a time-evolving graph where nodes represent microbes and edges are interactions among them. Motivated by the need to analyse such complex interactions, we develop a method that can learn a low-dimensional representation of the time-evolving graph while maintaining the dynamics occurring in the high-dimensional space. Through our experiments, we show that we can extract graph features such as clusters of nodes or edges that have the highest impact on the model to learn the low-dimensional representation. This information is crucial for identifying microbes and interactions among them that are strongly correlated with clinical diseases. We conduct our experiments on both synthetic and real-world microbiome datasets.
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Affiliation(s)
- Kateryna Melnyk
- Department of Mathematics and Computer Science, Freie Universität Berlin, Arnimallee 6, 14195, Berlin, Germany.
| | - Kuba Weimann
- Zuse Institute Berlin, Takustraße 7, 14195, Berlin, Germany
| | - Tim O F Conrad
- Zuse Institute Berlin, Takustraße 7, 14195, Berlin, Germany
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Iravani S, Conrad TOF. An Interpretable Deep Learning Approach for Biomarker Detection in LC-MS Proteomics Data. IEEE/ACM Trans Comput Biol Bioinform 2023; 20:151-161. [PMID: 35007196 DOI: 10.1109/tcbb.2022.3141656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Analyzing mass spectrometry-based proteomics data with deep learning (DL) approaches poses several challenges due to the high dimensionality, low sample size, and high level of noise. Additionally, DL-based workflows are often hindered to be integrated into medical settings due to the lack of interpretable explanation. We present DLearnMS, a DL biomarker detection framework, to address these challenges on proteomics instances of liquid chromatography-mass spectrometry (LC-MS) - a well-established tool for quantifying complex protein mixtures. Our DLearnMS framework learns the clinical state of LC-MS data instances using convolutional neural networks. Based on the trained neural networks, we show how biomarkers can be identified using layer-wise relevance propagation. This enables detecting discriminating regions of the data and the design of more robust networks. One of the main advantages over other established methods is that no explicit preprocessing step is needed in our DLearnMS framework. Our evaluation shows that DLearnMS outperforms conventional LC-MS biomarker detection approaches in identifying fewer false positive peaks while maintaining a comparable amount of true positives peaks. Code availability: The code is available from the following GIT repository: https://github.com/SaharIravani/DlearnMS.
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Abstract
Background Pseudotime estimation from dynamic single-cell transcriptomic data enables characterisation and understanding of the underlying processes, for example developmental processes. Various pseudotime estimation methods have been proposed during the last years. Typically, these methods start with a dimension reduction step because the low-dimensional representation is usually easier to analyse. Approaches such as PCA, ICA or t-SNE belong to the most widely used methods for dimension reduction in pseudotime estimation methods. However, these methods usually make assumptions on the derived dimensions, which can result in important dataset properties being missed. In this paper, we suggest a new dictionary learning based approach, dynDLT, for dimension reduction and pseudotime estimation of dynamic transcriptomic data. Dictionary learning is a matrix factorisation approach that does not restrict the dependence of the derived dimensions. To evaluate the performance, we conduct a large simulation study and analyse 8 real-world datasets. Results The simulation studies reveal that firstly, dynDLT preserves the simulated patterns in low-dimension and the pseudotimes can be derived from the low-dimensional representation. Secondly, the results show that dynDLT is suitable for the detection of genes exhibiting the simulated dynamic patterns, thereby facilitating the interpretation of the compressed representation and thus the dynamic processes. For the real-world data analysis, we select datasets with samples that are taken at different time points throughout an experiment. The pseudotimes found by dynDLT have high correlations with the experimental times. We compare the results to other approaches used in pseudotime estimation, or those that are method-wise closely connected to dictionary learning: ICA, NMF, PCA, t-SNE, and UMAP. DynDLT has the best overall performance for the simulated and real-world datasets. Conclusions We introduce dynDLT, a method that is suitable for pseudotime estimation. Its main advantages are: (1) It presents a model-free approach, meaning that it does not restrict the dependence of the derived dimensions; (2) Genes that are relevant in the detected dynamic processes can be identified from the dictionary matrix; (3) By a restriction of the dictionary entries to positive values, the dictionary atoms are highly interpretable. Supplementary Information The online version contains supplementary material available at (10.1186/s12864-021-08276-9).
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Affiliation(s)
- Mona Rams
- Freie Universitaet Berlin, Arnimallee 6, Berlin, 14195, Germany.
| | - Tim O F Conrad
- Konrad-Zuse-Zentrum für Informationstechnik Berlin, Takustraße 7, Berlin, 14195, Germany
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Wulkow H, Conrad TOF, Djurdjevac Conrad N, Müller SA, Nagel K, Schütte C. Prediction of Covid-19 spreading and optimal coordination of counter-measures: From microscopic to macroscopic models to Pareto fronts. PLoS One 2021; 16:e0249676. [PMID: 33887760 PMCID: PMC8062158 DOI: 10.1371/journal.pone.0249676] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 03/22/2021] [Indexed: 11/23/2022] Open
Abstract
The Covid-19 disease has caused a world-wide pandemic with more than 60 million positive cases and more than 1.4 million deaths by the end of November 2020. As long as effective medical treatment and vaccination are not available, non-pharmaceutical interventions such as social distancing, self-isolation and quarantine as well as far-reaching shutdowns of economic activity and public life are the only available strategies to prevent the virus from spreading. These interventions must meet conflicting requirements where some objectives, like the minimization of disease-related deaths or the impact on health systems, demand for stronger counter-measures, while others, such as social and economic costs, call for weaker counter-measures. Therefore, finding the optimal compromise of counter-measures requires the solution of a multi-objective optimization problem that is based on accurate prediction of future infection spreading for all combinations of counter-measures under consideration. We present a strategy for construction and solution of such a multi-objective optimization problem with real-world applicability. The strategy is based on a micro-model allowing for accurate prediction via a realistic combination of person-centric data-driven human mobility and behavior, stochastic infection models and disease progression models including micro-level inclusion of governmental intervention strategies. For this micro-model, a surrogate macro-model is constructed and validated that is much less computationally expensive and can therefore be used in the core of a numerical solver for the multi-objective optimization problem. The resulting set of optimal compromises between counter-measures (Pareto front) is discussed and its meaning for policy decisions is outlined.
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Affiliation(s)
| | - Tim O F Conrad
- Zuse Institute Berlin, Berlin, Germany
- Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany
| | | | - Sebastian A Müller
- Transport Systems Planning and Transport Telematics, TU Berlin, Berlin, Germany
| | - Kai Nagel
- Transport Systems Planning and Transport Telematics, TU Berlin, Berlin, Germany
| | - Christof Schütte
- Zuse Institute Berlin, Berlin, Germany
- Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany
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Liang Y, Piao C, Beuschel CB, Toppe D, Kollipara L, Bogdanow B, Maglione M, Lützkendorf J, See JCK, Huang S, Conrad TOF, Kintscher U, Madeo F, Liu F, Sickmann A, Sigrist SJ. eIF5A hypusination, boosted by dietary spermidine, protects from premature brain aging and mitochondrial dysfunction. Cell Rep 2021; 35:108941. [PMID: 33852845 DOI: 10.1016/j.celrep.2021.108941] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2019] [Revised: 02/11/2021] [Accepted: 03/12/2021] [Indexed: 01/08/2023] Open
Abstract
Mitochondrial function declines during brain aging and is suspected to play a key role in age-induced cognitive decline and neurodegeneration. Supplementing levels of spermidine, a body-endogenous metabolite, has been shown to promote mitochondrial respiration and delay aspects of brain aging. Spermidine serves as the amino-butyl group donor for the synthesis of hypusine (Nε-[4-amino-2-hydroxybutyl]-lysine) at a specific lysine residue of the eukaryotic translation initiation factor 5A (eIF5A). Here, we show that in the Drosophila brain, hypusinated eIF5A levels decline with age but can be boosted by dietary spermidine. Several genetic regimes of attenuating eIF5A hypusination all similarly affect brain mitochondrial respiration resembling age-typical mitochondrial decay and also provoke a premature aging of locomotion and memory formation in adult Drosophilae. eIF5A hypusination, conserved through all eukaryotes as an obviously critical effector of spermidine, might thus be an important diagnostic and therapeutic avenue in aspects of brain aging provoked by mitochondrial decline.
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Affiliation(s)
- YongTian Liang
- Institute for Biology/Genetics, Freie Universität Berlin, Berlin 14195, Germany; NeuroCure Cluster of Excellence, Charité Universitätmedizin Berlin, Berlin 10117, Germany
| | - Chengji Piao
- Institute for Biology/Genetics, Freie Universität Berlin, Berlin 14195, Germany; NeuroCure Cluster of Excellence, Charité Universitätmedizin Berlin, Berlin 10117, Germany
| | - Christine B Beuschel
- Institute for Biology/Genetics, Freie Universität Berlin, Berlin 14195, Germany; NeuroCure Cluster of Excellence, Charité Universitätmedizin Berlin, Berlin 10117, Germany
| | - David Toppe
- Institute for Biology/Genetics, Freie Universität Berlin, Berlin 14195, Germany; NeuroCure Cluster of Excellence, Charité Universitätmedizin Berlin, Berlin 10117, Germany
| | - Laxmikanth Kollipara
- Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Dortmund 44139, Germany
| | - Boris Bogdanow
- Department of Chemical Biology, Leibniz-Forschungsinstitut für Molekulare Pharmakologie (FMP), 13125 Berlin, Germany
| | - Marta Maglione
- Institute for Biology/Genetics, Freie Universität Berlin, Berlin 14195, Germany; NeuroCure Cluster of Excellence, Charité Universitätmedizin Berlin, Berlin 10117, Germany
| | - Janine Lützkendorf
- Institute for Biology/Genetics, Freie Universität Berlin, Berlin 14195, Germany; NeuroCure Cluster of Excellence, Charité Universitätmedizin Berlin, Berlin 10117, Germany
| | - Jason Chun Kit See
- Institute for Biology/Genetics, Freie Universität Berlin, Berlin 14195, Germany; NeuroCure Cluster of Excellence, Charité Universitätmedizin Berlin, Berlin 10117, Germany
| | - Sheng Huang
- Institute for Biology/Genetics, Freie Universität Berlin, Berlin 14195, Germany; NeuroCure Cluster of Excellence, Charité Universitätmedizin Berlin, Berlin 10117, Germany
| | - Tim O F Conrad
- Institute for Mathematics and Computer Sciences, Freie Universität Berlin, Berlin 14195, Germany; Zuse Institute Berlin, Berlin 14195, Germany
| | - Ulrich Kintscher
- German Centre for Cardiovascular Research (DZHK), partner site Berlin, Berlin 10117, Germany; Institute of Pharmacology, Center for Cardiovascular Research, Charité Universitätmedizin Berlin, Berlin 10115, Germany
| | - Frank Madeo
- Institute of Molecular Biosciences, NAWI Graz, University of Graz, Graz, Austria; BioTechMed Graz, Graz, Austria
| | - Fan Liu
- Department of Chemical Biology, Leibniz-Forschungsinstitut für Molekulare Pharmakologie (FMP), 13125 Berlin, Germany
| | - Albert Sickmann
- Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Dortmund 44139, Germany; Department of Chemistry, College of Physical Sciences, University of Aberdeen, Aberdeen AB24 3FX, Scotland, UK; Medizinische Fakultät, Medizinische Proteom-Center (MPC), Ruhr-Universität Bochum, Bochum 44801, Germany
| | - Stephan J Sigrist
- Institute for Biology/Genetics, Freie Universität Berlin, Berlin 14195, Germany; NeuroCure Cluster of Excellence, Charité Universitätmedizin Berlin, Berlin 10117, Germany.
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Abstract
Remote monitoring devices, which can be worn or implanted, have enabled a more effective healthcare for patients with periodic heart arrhythmia due to their ability to constantly monitor heart activity. However, these devices record considerable amounts of electrocardiogram (ECG) data that needs to be interpreted by physicians. Therefore, there is a growing need to develop reliable methods for automatic ECG interpretation to assist the physicians. Here, we use deep convolutional neural networks (CNN) to classify raw ECG recordings. However, training CNNs for ECG classification often requires a large number of annotated samples, which are expensive to acquire. In this work, we tackle this problem by using transfer learning. First, we pretrain CNNs on the largest public data set of continuous raw ECG signals. Next, we finetune the networks on a small data set for classification of Atrial Fibrillation, which is the most common heart arrhythmia. We show that pretraining improves the performance of CNNs on the target task by up to \documentclass[12pt]{minimal}
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\begin{document}$$6.57\%$$\end{document}6.57%, effectively reducing the number of annotations required to achieve the same performance as CNNs that are not pretrained. We investigate both supervised as well as unsupervised pretraining approaches, which we believe will increase in relevance, since they do not rely on the expensive ECG annotations. The code is available on GitHub at https://github.com/kweimann/ecg-transfer-learning.
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Affiliation(s)
- Kuba Weimann
- Department of Visual and Data-Centric Computing, Zuse Institute Berlin, Takustrasse 7, 14195, Berlin, Germany.
| | - Tim O F Conrad
- Department of Visual and Data-Centric Computing, Zuse Institute Berlin, Takustrasse 7, 14195, Berlin, Germany.,Department of Mathematics, Free University of Berlin, Arnimallee 6, 14195, Berlin, Germany
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Abstract
One of the most widely recognized features of biological systems is their modularity. The modules that constitute biological systems are said to be redeployed and combined across several conditions, thus acting as building blocks. In this work, we analyse to what extent are these building blocks reusable as compared with those found in randomized versions of a system. We develop a notion of decompositions of systems into phenotypic building blocks, which allows them to overlap while maximizing the number of times a building block is reused across several conditions. Different biological systems present building blocks whose reusability ranges from single use (e.g. condition specific) to constitutive, although their average reusability is not always higher than random equivalents of the system. These decompositions reveal a distinct distribution of building block sizes in real biological systems. This distribution stems, in part, from the peculiar usage pattern of the elements of biological systems, and constitutes a new angle to study the evolution of modularity.
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Affiliation(s)
- Victor Mireles
- 1 Department of Mathematics and Computer Science, Freie Universität Berlin , Berlin, Germany.,2 International Max Planck Research School for Computational Biology and Scientific Computing, Max Planck Institute for Molecular Genetics , Berlin , Germany
| | - Tim O F Conrad
- 1 Department of Mathematics and Computer Science, Freie Universität Berlin , Berlin, Germany
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10
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Conrad TOF, Genzel M, Cvetkovic N, Wulkow N, Leichtle A, Vybiral J, Kutyniok G, Schütte C. Sparse Proteomics Analysis - a compressed sensing-based approach for feature selection and classification of high-dimensional proteomics mass spectrometry data. BMC Bioinformatics 2017; 18:160. [PMID: 28274197 PMCID: PMC5343371 DOI: 10.1186/s12859-017-1565-4] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2016] [Accepted: 02/24/2017] [Indexed: 01/10/2023] Open
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.
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Affiliation(s)
- Tim O F Conrad
- Department of Mathematics, Freie Universität Berlin, Arnimallee 6, Berlin, Germany. .,Zuse Institute Berlin, Takustr. 7, Berlin, Germany.
| | - Martin Genzel
- Department of Mathematics, Technische Universität Berlin, Düsternbrooker Weg 20, Berlin, Germany
| | - Nada Cvetkovic
- Department of Mathematics, Freie Universität Berlin, Arnimallee 6, Berlin, Germany
| | - Niklas Wulkow
- Department of Mathematics, Freie Universität Berlin, Arnimallee 6, Berlin, Germany
| | - Alexander Leichtle
- Center of Laboratory Medicine, Inselspital - Bern University Hospital, Düsternbrooker Weg 20, Bern, 24105, Switzerland
| | - Jan Vybiral
- Department of Mathematical Analysis, Charles University, Düsternbrooker Weg 20, Prague, Czech Republic
| | - Gitta Kutyniok
- Department of Mathematics, Technische Universität Berlin, Düsternbrooker Weg 20, Berlin, Germany
| | - Christof Schütte
- Department of Mathematics, Freie Universität Berlin, Arnimallee 6, Berlin, Germany.,Zuse Institute Berlin, Takustr. 7, Berlin, Germany
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Aiche S, Reinert K, Schütte C, Hildebrand D, Schlüter H, Conrad TOF. Inferring proteolytic processes from mass spectrometry time series data using degradation graphs. PLoS One 2012; 7:e40656. [PMID: 22815782 PMCID: PMC3398944 DOI: 10.1371/journal.pone.0040656] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2012] [Accepted: 06/11/2012] [Indexed: 11/24/2022] Open
Abstract
Background Proteases play an essential part in a variety of biological processes. Besides their importance under healthy conditions they are also known to have a crucial role in complex diseases like cancer. In recent years, it has been shown that not only the fragments produced by proteases but also their dynamics, especially ex vivo, can serve as biomarkers. But so far, only a few approaches were taken to explicitly model the dynamics of proteolysis in the context of mass spectrometry. Results We introduce a new concept to model proteolytic processes, the degradation graph. The degradation graph is an extension of the cleavage graph, a data structure to reconstruct and visualize the proteolytic process. In contrast to previous approaches we extended the model to incorporate endoproteolytic processes and present a method to construct a degradation graph from mass spectrometry time series data. Based on a degradation graph and the intensities extracted from the mass spectra it is possible to estimate reaction rates of the underlying processes. We further suggest a score to rate different degradation graphs in their ability to explain the observed data. This score is used in an iterative heuristic to improve the structure of the initially constructed degradation graph. Conclusion We show that the proposed method is able to recover all degraded and generated peptides, the underlying reactions, and the reaction rates of proteolytic processes based on mass spectrometry time series data. We use simulated and real data to demonstrate that a given process can be reconstructed even in the presence of extensive noise, isobaric signals and false identifications. While the model is currently only validated on peptide data it is also applicable to proteins, as long as the necessary time series data can be produced.
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Affiliation(s)
- Stephan Aiche
- Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany.
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