Дисертації з теми "Mass spectrometry data"
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Offei, Felix. "Denoising Tandem Mass Spectrometry Data." Digital Commons @ East Tennessee State University, 2017. https://dc.etsu.edu/etd/3218.
Повний текст джерелаBen-Farag, Suaad Omran S. "Statistical analysis of mass spectrometry data." Thesis, University of Leeds, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.659026.
Повний текст джерелаHandley, Kelly. "Statistical analysis of proteomic mass spectrometry data." Thesis, University of Nottingham, 2007. http://eprints.nottingham.ac.uk/10287/.
Повний текст джерелаWandy, Joe. "Unsupervised Bayesian explorations of mass spectrometry data." Thesis, University of Glasgow, 2017. http://theses.gla.ac.uk/7928/.
Повний текст джерелаTengstrand, Erik. "Data analysis of non-targeted mass spectrometry experiments." Doctoral thesis, Stockholms universitet, Institutionen för miljövetenskap och analytisk kemi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-116820.
Повний текст джерелаAt the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 4: Submitted.
Lee, Wooram. "Protein Set for Normalization of Quantitative Mass Spectrometry Data." Thesis, Virginia Tech, 2014. http://hdl.handle.net/10919/54554.
Повний текст джерелаMaster of Science
NGUYEN, DAI HAI. "Machine Learning for Metabolite Identification with Mass Spectrometry Data." Kyoto University, 2020. http://hdl.handle.net/2433/259022.
Повний текст джерелаHe, Ping. "Classification methods and applications to mass spectral data." HKBU Institutional Repository, 2005. http://repository.hkbu.edu.hk/etd_ra/593.
Повний текст джерелаAiche, Stephan [Verfasser]. "Inferring Proteolytic Processes from Mass Spectrometry Time Series Data / Stephan Aiche." Berlin : Freie Universität Berlin, 2013. http://d-nb.info/1043480870/34.
Повний текст джерелаBielow, Chris [Verfasser]. "Quantification and simulation of liquid chromatography-mass spectrometry data / Chris Bielow." Berlin : Freie Universität Berlin, 2012. http://d-nb.info/1030382883/34.
Повний текст джерелаBrian, Carrillo. "Optimization of data directed acquisition in tandem mass spectrometry for proteomics." Thesis, McGill University, 2004. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=80003.
Повний текст джерелаHitchcock, Jonathan James. "Automated processing and analysis of gas chromatography/mass spectrometry screening data." Thesis, University of Bedfordshire, 2009. http://hdl.handle.net/10547/134940.
Повний текст джерелаNagavaram, Ashish. "Cloud Based Dynamic Workflow with QOS For Mass Spectrometry Data Analysis." The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1322681210.
Повний текст джерелаBarton, Sheila Janet. "Statistical analysis of proteomic profile data generated by tandem mass spectrometry." Thesis, London School of Hygiene and Tropical Medicine (University of London), 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.536923.
Повний текст джерелаKuschner, Karl W. "A Bayesian network approach to feature selection in mass spectrometry data." W&M ScholarWorks, 2009. https://scholarworks.wm.edu/etd/1539623543.
Повний текст джерелаSniatynski, Matthew John. "Data analysis in proteomics novel computational strategies for modeling and interpreting complex mass spectrometry data." Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/1497.
Повний текст джерелаSong, Zhao Xu Dong. "Bioinformatics methods for protein identification using peptide mass fingerprinting data." Diss., Columbia, Mo. : University of Missouri--Columbia, 2009. http://hdl.handle.net/10355/6125.
Повний текст джерелаHornby, Sarah Elizabeth. "Characterisation of pyrolysis mass spectrometry for use in marine algal systematics." Thesis, University of Newcastle Upon Tyne, 2000. http://hdl.handle.net/10443/948.
Повний текст джерелаDomingo, Almenara Xavier. "Automated mass spectrometry-based metabolomics data processing by blind source separation methods." Doctoral thesis, Universitat Rovira i Virgili, 2016. http://hdl.handle.net/10803/397799.
Повний текст джерелаUna de las principales limitaciones de la metabolómica es la transformación de datos crudos en información biológica. Además, la metabolómica basada en espectrometría de masas genera grandes cantidades de datos complejos caracterizados por la co-elución de compuestos y artefactos experimentales. El objetivo de esta tesis es desarrollar estrategias automatizadas basadas en deconvolución ciega de la señal para mejorar las capacidades de los métodos existentes que tratan las limitaciones de los diferentes pasos del procesamiento de datos en metabolómica. El objetivo de esta tesis es también desarrollar herramientas capaces de ejecutar el flujo de trabajo del procesamiento de datos en metabolómica, que incluye el preprocessamiento de datos, deconvolución espectral, alineamiento e identificación. Como resultado, tres nuevos métodos automáticos para deconvolución espectral basados en deconvolución ciega de la señal fueron desarrollados. Estos métodos fueron incluidos en dos herramientas computacionales que permiten convertir automáticamente datos crudos en información biológica interpretable y por lo tanto, permiten resolver hipótesis biológicas y adquirir nuevos conocimientos biológicos.
One of the major bottlenecks in metabolomics is to convert raw data samples into biological interpretable information. Moreover, mass spectrometry-based metabolomics generates large and complex datasets characterized by co-eluting compounds and with experimental artifacts. This thesis main objective is to develop automated strategies based on blind source separation to improve the capabilities of the current methods that tackle the different metabolomics data processing workflow steps limitations. Also, the objective of this thesis is to develop tools capable of performing the entire metabolomics workflow for GC--MS, including pre-processing, spectral deconvolution, alignment and identification. As a result, three new automated methods for spectral deconvolution based on blind source separation were developed. These methods were embedded into two computation tools able to automatedly convert raw data into biological interpretable information and thus, allow resolving biological answers and discovering new biological insights.
Bäckström, Daniel. "Managing and Exploring Large Data Sets Generated by Liquid Separation - Mass Spectrometry." Doctoral thesis, Uppsala University, Analytical Chemistry, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-8223.
Повний текст джерелаA trend in natural science and especially in analytical chemistry is the increasing need for analysis of a large number of complex samples with low analyte concentrations. Biological samples (urine, blood, plasma, cerebral spinal fluid, tissue etc.) are often suitable for analysis with liquid separation mass spectrometry (LS-MS), resulting in two-way data tables (time vs. m/z). Such biological 'fingerprints' taken for all samples in a study correspond to a large amount of data. Detailed characterization requires a high sampling rate in combination with high mass resolution and wide mass range, which presents a challenge in data handling and exploration. This thesis describes methods for managing and exploring large data sets made up of such detailed 'fingerprints' (represented as data matrices).
The methods were implemented as scripts and functions in Matlab, a wide-spread environment for matrix manipulations. A single-file structure to hold the imported data facilitated both easy access and fast manipulation. Routines for baseline removal and noise reduction were intended to reduce the amount of data without loosing relevant information. A tool for visualizing and exploring single runs was also included. When comparing two or more 'fingerprints' they usually have to be aligned due to unintended shifts in analyte positions in time and m/z. A PCA-like multivariate method proved to be less sensitive to such shifts, and an ANOVA implementation made it easier to find systematic differences within the data sets.
The above strategies and methods were applied to complex samples such as plasma, protein digests, and urine. The field of application included urine profiling (paracetamole intake; beverage effects), peptide mapping (different digestion protocols) and search for potential biomarkers (appendicitis diagnosis) . The influence of the experimental factors was visualized by PCA score plots as well as clustering diagrams (dendrograms).
Fredriksson, Mattias. "Efficient algorithms for highly automated evaluation of liquid chromatography - mass spectrometry data." Doctoral thesis, Mittuniversitetet, Institutionen för naturvetenskap, teknik och matematik, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-12991.
Повний текст джерелаDe analysinstrument som används för att ta reda på vad ett prov innehåller(och till vilken mängd) måste vanligtvis ställas in för det specifika fallet, för attfungera optimalt. Det finns ofta en mängd olika variabler att undersöka som harmer eller mindre inverkan på resultatet och när provet är okänt kan man oftast inteförutspå de optimala inställningarna i förtid.En vätskekromatograf med en masspektrometer som detektor är ett sådantinstrument som är utvecklat för att separera och identifiera organiska ämnen lösta ivätska. Med detta mycket potenta system kan man ofta med rätt inställningar delaupp de ingående ämnena i provet var för sig och samtidigt erhålla mått som kanrelateras till dess massa och mängd. Detta system används flitigt av analytiskalaboratorer inom bl.a. läkemedelsindustrin för att undersöka stabilitet och renhethos potentiella läkemedel. För att optimera instrumentet för det okända provetkrävs dock att en hel del försök utförs där inställningarna varieras. Syftet är attmed en mindre mängd designade försök bygga en modell som klarar av att peka åtvilket håll de optimala inställningarna finns. Data som genereras från instrumentetför denna typ av applikation är i matrisform då instrumentet scannar och spararintensiteten av ett intervall av massor varje tidpunkt en mätning sker. Om enanalyt når detektorn vid aktuell tidpunkt återges det som en eller flera överlagdanormalfördelade toppar som ett specifikt mönster på en annars oregelbundenbakgrundssignal. Förutom att alla topparna i det färdiga datasetet helst ska varavälseparerade och ha den rätta formen, så ska tiden analysen pågår vara så kortsom möjlig. Det är ändå inte ovanligt att ett färdigt dataset består av tiotalsmiljoner uppmätta intensiteter och att det kan krävas runt 10 försök med olikabetingelser för att åstadkomma ett godtagbart resultat.Dataseten kan dock till mycket stor del innehålla brus och andra störandesignaler vilket gör de extra krångligt att tolka och utvärdera. Eftersom man ävenofta får att komponenterna byter plats i ett dataset när betingelserna ändras kan enmanuell utvärdering ta mycket lång tid.Syftet med denna avhandling har varit att hitta metoder som kan vara till nyttaför den som snabbt och automatiskt behöver jämföra dataset analyserade medolika kromatografiska betingelser, men med samma prov. Det slutgiltiga målet harfrämst varit att identifiera hur olika komponenter i provet har rört sig mellan deolika dataseten, men de steg som ingår kan även nyttjas till andra applikationer.
The, Matthew. "Statistical and machine learning methods to analyze large-scale mass spectrometry data." Licentiate thesis, KTH, Genteknologi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-185149.
Повний текст джерелаQC 20160412
Faull, Peter Allen. "Exploring gas-phase protein conformations by ion mobility-mass spectrometry." Thesis, University of Edinburgh, 2009. http://hdl.handle.net/1842/3851.
Повний текст джерелаKumar, Chanchal. "Bioinformatics methods and applications for functional analysis of mass spectrometry based proteomics data." Diss., lmu, 2008. http://nbn-resolving.de/urn:nbn:de:bvb:19-124512.
Повний текст джерелаAmmar, Constantin [Verfasser], and Ralf [Akademischer Betreuer] Zimmer. "Context-based analysis of mass spectrometry proteomics data / Constantin Ammar ; Betreuer: Ralf Zimmer." München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2020. http://d-nb.info/1221524488/34.
Повний текст джерелаHauschild, Jennifer M. "Fourier transform ion cyclotron resonance mass spectrometry for petroleomics." Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:8604a373-fb6b-4bc0-8dc1-464a191b1fac.
Повний текст джерелаHellner, Joakim. "Introducing quality assessment and efficient management of cellular thermal shift assay mass spectrometry data." Thesis, Uppsala universitet, Institutionen för biologisk grundutbildning, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-311792.
Повний текст джерелаLi, Fang Owens Kevin G. "Development of a genetic algorithm-correlation analysis (GA/CA) program for classification of chemical compounds using mass spectral data /." Philadelphia, Pa. : Drexel University, 2008. http://hdl.handle.net/1860/2803.
Повний текст джерелаSettelmeier, Jens. "Theoretical Fundamentals of Computational Proteomics and Deep Learning- Based Identification of Chimeric Mass Spectrometry Data." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-294322.
Повний текст джерелаEn komplicerande faktor för peptididentifiering genom MS / MS- experiment är närvaron av “chimära” spektra eller “chimera”, där åtminstone två föregångare med liknande retentionstid och massa sameluerar in i masspektrometern och resulterar i ett spektrum som är en superposition av individuella spektra. Eftersom dessa chimära spektra gör identifieringen av peptider mer utmanande behövs ett detekteringsverktyg för att förbättra identifieringsgraden för peptider. I detta arbete fokuserade vi på GLEAMS, en lärd inbäddning för effektiv gemensam analys av miljontals masspektrum. Först simulerade vi chimära spektra. Sedan presenterar vi en ensembleklassificering baserad på olika maskininlärnings- och djupinlärningsmetoder som lär sig att skilja på simulerad chimera och rena spektra. Resultatet visar att GLEAM fångar “chimärheten” i ett spektrum, vilket kan leda till högre identifieringsgrad av protein samt ge stöd till utvecklingsprocesser för biomarkörer.
Meier, Florian [Verfasser], and Matthias [Akademischer Betreuer] Mann. "Data acquisition methods for next-generation mass spectrometry-based proteomics / Florian Meier ; Betreuer: Matthias Mann." München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2018. http://d-nb.info/1175381322/34.
Повний текст джерелаDelabrière, Alexis. "New approaches for processing and annotations of high-throughput metabolomic data obtained by mass spectrometry." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLS359/document.
Повний текст джерелаMetabolomics is a phenotyping approach with promising prospects for the diagnosis and monitoring of several diseases. The most widely used observation technique in metabolomics is mass spectrometry (MS). Recent technological developments have significantly increased the size and complexity of data. This thesis focused on two bottlenecks in the processing of these data, the extraction of peaks from raw data and the annotation of MS/MS spectra. The first part of the thesis focused on the development of a new peak detection algorithm for Flow Injection Analysis (FIA) data, a high-throughput metabolomics technique. A model derived from the physics of the mass spectrometer taking into account the saturation of the instrument has been proposed. This model includes a peak common to all metabolites and a specific saturation phenomenon for each ion. This model has made it possible to create a workflow that estimates the common peak on well-behaved signals, then uses it to perform matched filtration on all signals. Its effectiveness on real data has been studied and it has been shown that proFIA is superior to existing algorithms, has good reproducibility and is very close to manual measurements made by an expert on several types of devices. The second part of this thesis focused on the development of a tool for detecting the structural similarities of a set of fragmentation spectra. To do this, a new graphical representation has been proposed, which does not require the metabolite formula. The graphs are also a natural representation of MS/MS spectra. Some properties of these graphs have then made it possible to create an efficient algorithm for detecting frequent subgraphs (FSM) based on the generation of trees covering graphs. This tool has been tested on two different data sets and has proven its speed and interpretability compared to state-of-the-art algorithms. These two algorithms have been implemented in R, proFIA and mineMS2 packages available to the community
Wang, Minkun. "Topic Model-based Mass Spectrometric Data Analysis in Cancer Biomarker Discovery Studies." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/78201.
Повний текст джерелаPh. D.
Conrad, Tim [Verfasser]. "New statistical algorithms for the analysis of mass spectrometry time-of-flight mass data with applications in clinical diagnostics / Tim Conrad." Berlin : Freie Universität Berlin, 2008. http://d-nb.info/1023262851/34.
Повний текст джерелаXu, Hua. "Novel data analysis methods and algorithms for identification of peptides and proteins by use of tandem mass spectrometry." Columbus, Ohio : Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1187113396.
Повний текст джерелаTanaka, Yuuki. "Deciphering the physiological codes of bone using elemental and isotopic data obtained by ICP-mass spectrometry." Kyoto University, 2017. http://hdl.handle.net/2433/228224.
Повний текст джерелаLee, Joanna L. S. "Time-of-flight secondary ion mass spectrometry - fundamental issues for quantitative measurements and multivariate data analysis." Thesis, University of Oxford, 2011. http://ora.ox.ac.uk/objects/uuid:f0e4b8ff-f563-429e-9e71-9c277a5139c4.
Повний текст джерелаLancashire, Lee James. "Multi-layer perceptron artificial neural network predictive modelling of genomic and mass spectrometry data in bioinformatics." Thesis, Nottingham Trent University, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.442340.
Повний текст джерелаMomo, Remi Ako-Mbianyor. "MALDI-ToF mass spectrometry biomarker profiling via multivariate data analysis application in the biopharmaceutical bioprocessing industry." Thesis, University of Newcastle upon Tyne, 2013. http://hdl.handle.net/10443/1939.
Повний текст джерелаGreen, Christopher Lee. "IP Algorithm Applied to Proteomics Data." Diss., CLICK HERE for online access, 2004. http://contentdm.lib.byu.edu/ETD/image/etd618.pdf.
Повний текст джерелаHarper, Robert T. "Determination of the proton affinities of gas phase peptides by mass spectrometry and computational chemistry." Scholarly Commons, 2007. https://scholarlycommons.pacific.edu/uop_etds/673.
Повний текст джерелаTaylor, John A. "Development of automated methods for analysis of mass spectrometric data and characterization of an active proteolytic fragment of CD45 /." Thesis, Connect to this title online; UW restricted, 1997. http://hdl.handle.net/1773/9233.
Повний текст джерелаTreutler, Hendrik [Verfasser]. "Bioinformatics tools for mass spectrometry, phylogenetic footprinting, and the integration of biological data : [kumulative Dissertation] / Hendrik Treutler." Halle, 2017. http://d-nb.info/1153401967/34.
Повний текст джерелаMueller, Michael. "Integrated analysis of proteomics data to assess and improve the scope of mass spectrometry based genome annotation." Thesis, University of Cambridge, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.611790.
Повний текст джерелаHope, Janiece L. "Comprehensive gas chromatography with chemometric data analysis for pattern recognition and signal deconvolution of complex samples /." Thesis, Connect to this title online; UW restricted, 2005. http://hdl.handle.net/1773/8542.
Повний текст джерелаVeit, Johannes [Verfasser], and Oliver [Akademischer Betreuer] Kohlbacher. "Efficient Workflows for Analyzing High-Performance Liquid Chromatography Mass Spectrometry-Based Proteomics Data / Johannes Veit ; Betreuer: Oliver Kohlbacher." Tübingen : Universitätsbibliothek Tübingen, 2019. http://d-nb.info/1193489288/34.
Повний текст джерелаYang, Li. "Functionalization, characterization, and applications of diamond particles, modification of planar silicon, and chemometrics analysis of mass spectrometry data /." Diss., CLICK HERE for online access, 2009. http://contentdm.lib.byu.edu/ETD/image/etd2855.pdf.
Повний текст джерелаMoberg, My. "Liquid Chromatography Coupled to Mass Spectrometry : Implementation of Chemometric Optimization and Selected Applications." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-7071.
Повний текст джерелаZhou, Bin. "Computational Analysis of LC-MS/MS Data for Metabolite Identification." Thesis, Virginia Tech, 2011. http://hdl.handle.net/10919/36109.
Повний текст джерелаMaster of Science
Christison, Krege Matthew. "Exploring the Molecular Origin of Jet Fuel Thermal Oxidative Deposition Through Statistical Analysis of Mass Spectral Data and Pyrolysis Gas Chromatography/Mass Spectrometry of Deposits." Scholarly Commons, 2019. https://scholarlycommons.pacific.edu/uop_etds/3639.
Повний текст джерелаRabe, Jan-Hinrich [Verfasser], and Carsten [Akademischer Betreuer] Hopf. "Multimodal FTIR Microscopy-guided Acquisition and Interpretation of MALDI Mass Spectrometry Imaging Data / Jan-Hinrich Rabe ; Betreuer: Carsten Hopf." Heidelberg : Universitätsbibliothek Heidelberg, 2018. http://d-nb.info/1177385341/34.
Повний текст джерела