Relevant bibliographies by topics / Mass spectrometry data

Academic literature on the topic 'Mass spectrometry data'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Mass spectrometry data"

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Deng, H. "Questions About Mass Spectrometry Data." Science 313, no. 5786 (July 28, 2006): 440b. http://dx.doi.org/10.1126/science.313.5786.440b.

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Burlaka, I. A., M. A. Maiboroda, and I. V. Startseva. "Data Interpretation in Mass Spectrometry." Journal of Analytical Chemistry 60, no. 8 (August 2005): 698–701. http://dx.doi.org/10.1007/s10809-005-0164-0.

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Bellard, S., M. Corkhill, R. Reid, and C. Seeley. "The mass spectrometry data centre." Rapid Communications in Mass Spectrometry 4, no. 6 (June 1990): 234–36. http://dx.doi.org/10.1002/rcm.1290040614.

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Gao, Yin Han, Jing Zhou, Wei Wang, and Bao Jun Wu. "Data Acquisition and High Speed Storage by FPGA Implementation in the Quadrupole Mass Spectrometry." Applied Mechanics and Materials 239-240 (December 2012): 901–4. http://dx.doi.org/10.4028/www.scientific.net/amm.239-240.901.

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A design method of data acquisition and data storage buffer control logic in the quadrupole mass spectrometry was proposed. The control logic builds a high-speed built-in FIFO memory on FPGA to buffer of mass spectrometry data. FIFO storage capacity of 16K bytes and simultaneous reading and writing speed of 60Mbps were realized by control logic system. The data acquisition and storage buffer system had been used on the Quadrupole Mass Spectrometry and Quadrupole Ion Trap Mass Spectrometry to reduce the single scanning time of MS analysis. A higher sensitivity had been obtained by increasing the scanning rate of mass spectrometer.
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Laude, David A., Carolyn L. Johlman, John R. Cooper, and Charles L. Wilkins. "Postsearch accurate mass measurement filter for gas chromatography/infrared spectrometry/mass spectrometry and gas chromatography/mass spectrometry data." Analytical Chemistry 57, no. 6 (May 1985): 1044–49. http://dx.doi.org/10.1021/ac00283a019.

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Bielow, Chris, Stephan Aiche, Sandro Andreotti, and Knut Reinert. "MSSimulator: Simulation of Mass Spectrometry Data." Journal of Proteome Research 10, no. 7 (July 2011): 2922–29. http://dx.doi.org/10.1021/pr200155f.

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Amidan, Brett G., Daniel J. Orton, Brian L. LaMarche, Matthew E. Monroe, Ronald J. Moore, Alexander M. Venzin, Richard D. Smith, Landon H. Sego, Mark F. Tardiff, and Samuel H. Payne. "Signatures for Mass Spectrometry Data Quality." Journal of Proteome Research 13, no. 4 (March 24, 2014): 2215–22. http://dx.doi.org/10.1021/pr401143e.

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Murie, Carl, Brian Sandri, Ann-Sofi Sandberg, Timothy J. Griffin, Janne Lehtiö, Christine Wendt, and Ola Larsson. "Normalization of mass spectrometry data (NOMAD)." Advances in Biological Regulation 67 (January 2018): 128–33. http://dx.doi.org/10.1016/j.jbior.2017.11.005.

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Randolph, T. W., and Y. Yasui. "Multiscale Processing of Mass Spectrometry Data." Biometrics 62, no. 2 (January 6, 2006): 589–97. http://dx.doi.org/10.1111/j.1541-0420.2005.00504.x.

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Thomas, Asha, Georgia D. Tourassi, Adel S. Elmaghraby, Roland Valdes, and Saeed A. Jortani. "Data mining in proteomic mass spectrometry." Clinical Proteomics 2, no. 1-2 (March 2006): 13–32. http://dx.doi.org/10.1385/cp:2:1:13.

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Dissertations / Theses on the topic "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.

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Protein identification using tandem mass spectrometry (MS/MS) has proven to be an effective way to identify proteins in a biological sample. An observed spectrum is constructed from the data produced by the tandem mass spectrometer. A protein can be identified if the observed spectrum aligns with the theoretical spectrum. However, data generated by the tandem mass spectrometer are affected by errors thus making protein identification challenging in the field of proteomics. Some of these errors include wrong calibration of the instrument, instrument distortion and noise. In this thesis, we present a pre-processing method, which focuses on the removal of noisy data with the hope of aiding in better identification of proteins. We employ the method of binning to reduce the number of noise peaks in the data without sacrificing the alignment of the observed spectrum with the theoretical spectrum. In some cases, the alignment of the two spectra improved.
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Ben-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.

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The research described in this thesis can be broadly described by term "statistical analysis of mass spectrometry data". Bioinformatics is a new science which attempts to amalgamate statistical methodology with bring statistical thinking and the biological understanding to area which have previously been void of such. Mass spectrometry which is used to study proteins and their functions, is a relatively new field of bioinformatics research. In this thesis we explore three main themes, all of which tackle a different statistical learning method which arises in mass spectrometry. The main focus of the first theme of the research is on using statistical methods to study fragmentation patterns of mass spectrometry experiments. The analysis contained in this theme has been loosely split into parts: firstly, we calculate a probability of a process called cleavage as part of our preliminary analysis to determine which combination of fragmentation site residues were likely to break. In part two, we apply statistical models to investigate factors influencing the relative intensity of fragment ions formed in tandem mass spectrometry experiments. Separate models were formulated for different types of ions as it was thought that different factors may influence the formation of each type of fragment ion. Statistical regression methods are applied to two types of datasets of mass spectra data: tryptic and nontryptic peptide sequences. We find that several factors have a highly significant influence on the relative intensity of fragment ions formed in the experiment.
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Handley, Kelly. "Statistical analysis of proteomic mass spectrometry data." Thesis, University of Nottingham, 2007. http://eprints.nottingham.ac.uk/10287/.

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This thesis considers the statistical modelling and analysis of proteomic mass spectrometry data. Proteomics is a relatively new field of study and tried and tested methods of analysis do not yet exist. Mass spectrometry output is high-dimensional and so we firstly develop an algorithm to identify peaks in the spectra in order to reduce the dimensionality of the datasets. We use the results along with a variety of classification methods to examine the classification of new spectra based on a training set. Another method to reduce the complexity of the problem is to fit a parametric model to the data. We model the data as a mixture of Gaussian peaks with parameters representing the peak locations, heights and variances, and apply a Bayesian Markov chain Monte Carlo (MCMC) algorithm to obtain their estimates. These resulting estimates are used to identify m/z values where differences are apparent between groups, where the m/z value of an ion is its mass divided by its charge. A multilevel modelling framework is also considered to incorporate the structure in the data and locations exhibiting differences are again obtained. We consider two mass spectrometry datasets in detail. The first consists of mass spectra from breast cancer cells which either have or have not been treated with the chemotherapeutic agent Taxol. The second consists of mass spectra from melanoma cells classified as stage I or stage IV using the TNM system. Using the MCMC and multilevel techniques described above we show that, in both datasets, small subsets of the available m/z values can be identified which exhibit significant differences in protein expression between groups. Also we see that good classification of new data can also be achieved using a small number of m/z values and that the classification rate does not fall greatly when compared with results from the complete spectra. For both datasets we compare our results with those in the literature which use other techniques on the same data. We conclude by discussing potential areas for further research.
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Wandy, Joe. "Unsupervised Bayesian explorations of mass spectrometry data." Thesis, University of Glasgow, 2017. http://theses.gla.ac.uk/7928/.

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In recent years, the large-scale, untargeted studies of the compounds that serve as workers in the cell (proteins) and the small molecules involved in essential life-sustaining chemical processes (metabolites) have provided insights into a wide array of fields, such as medical diagnostics, drug discovery, personalised medicine and many others. Measurements in such studies are routinely performed using liquid chromatography mass spectrometry (LC-MS) instruments. From these measurements, we obtain a set of peaks having mass-to-charge, retention time (RT) and intensity values. Before further analysis is possible, the raw LC-MS data has to be processed in a data pre-preprocessing pipeline. In the alignment step of the pipeline, peaks from multiple LC-MS measurements have to be matched. In the identification step, the identity of unknown compounds in the sample that generate the observed peaks have to be assigned. Using tandem mass spectrometry, fragmentation peaks characteristic to a compound can be obtained and used to help establish the identity of the compound. Alignment and identification are challenging because the true identities of the entire set of compounds in the sample are unknown, and a single compound can produce many observed peaks, each with a potential drift in its retention time value. These observed peaks are not independent as they can be explained as being generated by the same compound. The aim of this thesis is to introduce methods to group these related peaks and to use these groupings to improve alignment and assist in identification during data pre-processing. Firstly, we introduce a generative model to group related peaks by their retention time. This information is used to influence direct-matching alignment, bringing related peak groups closer during matching. Investigations using benchmark datasets reveal that improved alignment performance is obtained from this approach. Next, we also consider mass information in the grouping process, resulting in PrecursorCluster, a model that performs the grouping of related peaks in metabolomics by their explainable mass relationships, RT and intensity values. Through a second-stage process that matches these related peak groups, peak alignment is produced. Experiments on benchmark datasets show that an improved alignment performance is obtained, while uncertainties in matched peaksets can also be extracted from the method. In the next section, we expand upon this two-stage method and introduce HDPAlign, a model that performs the clustering of related peaks within and across multiple LC-MS runs at once. This allows for matched peaksets and their respective uncertainties to be naturally extracted from the model. Finally, we look at fragmentation peaks used for identification and introduce MS2LDA, a topic model to group related fragmentation features. These groups of related fragmentation features potentially correspond to substructures shared by metabolites and can be used to assist data interpretation during identification. This final section corresponds to a work in progress and points to many interesting avenues for future research.
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Tengstrand, 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.

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Data processing tools are valuable to the analytical chemist as they can speed up the analysis, and sometimes solve problems that are not feasible to solve in a traditional manner. However, the complexity of many data processing tools can make their use daunting for the inexperienced user. This thesis includes two applications and two tools for data processing. The first application focuses on minimizing the manual input, reducing the time required for a simple task. The second application required more manual input, in the form of parameter selection, but process far more data.  The data processing tools both include features that simplify the manual work required. The first by including visual diagnostics tools that helps in setting the parameters. The second via internal validation that makes the tool’s process more robust and reliable, and thereby less sensitive to small changes in the parameters. No matter how good or precise a data processing tool is, if it is so cumbersome that it is not used by the analytical chemists that need it, it is useless. Therefore, the main focus of this thesis is to make data processing easier.

At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 4: Submitted.

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Lee, Wooram. "Protein Set for Normalization of Quantitative Mass Spectrometry Data." Thesis, Virginia Tech, 2014. http://hdl.handle.net/10919/54554.

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Mass spectrometry has been recognized as a prominent analytical technique for peptide and protein identification and quantitation. With the advent of soft ionization methods, such as electrospray ionization and matrix assisted laser desorption/ionization, mass spectrometry has opened a new era for protein and proteome analysis. Due to its high-throughput and high-resolution character, along with the development of powerful data analysis software tools, mass spectrometry has become the most popular method for quantitative proteomics. Stable isotope labeling and label-free quantitation methods are widely used in quantitative mass spectrometry experiments. Proteins with stable expression level and key roles in basic cellular functions such as actin, tubulin and glyceraldehyde-3-phosphate dehydrogenase, are frequently utilized as internal controls in biological experiments. However, recent studies have shown that the expression level of such commonly used housekeeping proteins is dependent on cell type, cell cycle or disease status, and that it can change as a result of a biochemical stimulation. Such phenomena can, therefore, substantially compromise the use of these proteins for data validation. In this work, we propose a novel set of proteins for quantitative mass spectrometry that can be used either for data normalization or validation purposes. The protein set was generated from cell cycle experiments performed with MCF-7, an estrogen receptor positive breast cancer cell line, and MCF-10A, a non-tumorigenic immortalized breast cell line. The protein set was selected from a list of 3700 proteins identified in the different cellular sub-fractions and cell cycle stages of MCF-7/MCF-10A cells, based on the stability of spectral count data (CV<30 %) generated with an LTQ ion trap mass spectrometer. A total of 34 proteins qualified as endogenous standards for the nuclear, and 75 for the cytoplasmic cell fractions, respectively. The validation of these proteins was performed with a complementary, Her2+, SKBR-3 cell line. Based on the outcome of these experiments, it is anticipated that the proposed protein set will find applicability for data normalization/validation in a broader range of mechanistic biological studies that involve the use of cell lines.
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NGUYEN, DAI HAI. "Machine Learning for Metabolite Identification with Mass Spectrometry Data." Kyoto University, 2020. http://hdl.handle.net/2433/259022.

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He, Ping. "Classification methods and applications to mass spectral data." HKBU Institutional Repository, 2005. http://repository.hkbu.edu.hk/etd_ra/593.

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Aiche, Stephan [Verfasser]. "Inferring Proteolytic Processes from Mass Spectrometry Time Series Data / Stephan Aiche." Berlin : Freie Universität Berlin, 2013. http://d-nb.info/1043480870/34.

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Bielow, Chris [Verfasser]. "Quantification and simulation of liquid chromatography-mass spectrometry data / Chris Bielow." Berlin : Freie Universität Berlin, 2012. http://d-nb.info/1030382883/34.

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Books on the topic "Mass spectrometry data"

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Rune, Matthiesen, ed. Mass spectrometry data analysis in proteomics. Totowa, N.J: Humana Press, 2007.

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Rune, Matthiesen. Mass Spectrometry Data Analysis in Proteomics. New Jersey: Humana Press, 2006. http://dx.doi.org/10.1385/1597452750.

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Matthiesen, Rune, ed. Mass Spectrometry Data Analysis in Proteomics. Totowa, NJ: Humana Press, 2013. http://dx.doi.org/10.1007/978-1-62703-392-3.

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Matthiesen, Rune, ed. Mass Spectrometry Data Analysis in Proteomics. New York, NY: Springer New York, 2020. http://dx.doi.org/10.1007/978-1-4939-9744-2.

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Matthiesen, Rune. Mass spectrometry data analysis in proteomics. New York: Humana Press, 2013.

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O, Raznikova M., ed. Informat͡s︡ionno-analiticheskai͡a︡ mass-spektrometrii͡a︡. Moskva: "Nauka", 1992.

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McLafferty, F. W. TheW iley/NBS registry of mass spectral data. New York: Wiley, 1989.

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McLafferty, Fred W. The Wiley/NBS registry of mass spectral data. New York: Wiley, 1989.

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Watson, J. Throck. Introduction to mass spectrometry: Instrumentation, applications, and strategies for data interpretation. 4th ed. Hoboken, N.J: John Wiley, 2007.

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K, Lang J., ed. Handbook on mass spectrometry instrumentation, data and analysis, and applications. Hauppauge, NY: Nova Science Publishers, 2009.

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Book chapters on the topic "Mass spectrometry data"

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Sugiura, Yuki, and Mitsutoshi Setou. "Statistical Procedure for IMS Data Analysis." In Imaging Mass Spectrometry, 127–42. Tokyo: Springer Japan, 2010. http://dx.doi.org/10.1007/978-4-431-09425-8_10.

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Neumann, Steffen, Oscar Yanes, Roland Mumm, and Pietro Franceschi. "Mass Spectrometry Data Processing." In Metabolomics, 73–99. Boca Raton, Florida : CRC Press, [2019]: Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9781315370583-4.

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Colombini, Maria Perla, Francesca Modugno, and Erika Ribechini. "Archaeometric Data from Mass Spectrometric Analysis of Organic Materials: Proteins, Lipids, Terpenoid Resins, Lignocellulosic Polymers, and Dyestuff." In Mass Spectrometry Handbook, 797–828. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118180730.ch36.

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Saeed, Fahad, and Muhammad Haseeb. "Introduction to Mass Spectrometry Data." In Computational Biology, 7–19. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-01960-9_2.

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Cross, K. P., P. T. Palmer, C. F. Beckner, A. B. Giordani, H. G. Gregg, P. A. Hoffman, and C. G. Enke. "Automation of Structure Elucidation from Mass Spectrometry-Mass Spectrometry Data." In ACS Symposium Series, 321–36. Washington, DC: American Chemical Society, 1986. http://dx.doi.org/10.1021/bk-1986-0306.ch025.

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Peralbo-Molina, Ángela, Pol Solà-Santos, Alexandre Perera-Lluna, and Eduardo Chicano-Gálvez. "Data Processing and Analysis in Mass Spectrometry-Based Metabolomics." In Mass Spectrometry for Metabolomics, 207–39. New York, NY: Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-2699-3_20.

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Ball, Graham, and Ali Al-Shahib. "Data Mining for Predictive Proteomics." In Mass Spectrometry for Microbial Proteomics, 409–22. Chichester, UK: John Wiley & Sons, Ltd, 2010. http://dx.doi.org/10.1002/9780470665497.ch17.

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Cragnolini, T., and K. Thalassinos. "CHAPTER 7. Computational Approaches for Processing Native Ion Mobility–Mass Spectrometry Data." In Ion Mobility-Mass Spectrometry, 163–82. Cambridge: Royal Society of Chemistry, 2021. http://dx.doi.org/10.1039/9781839162886-00163.

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Sugimoto, Masahiro, Yumi Aizawa, and Atsumi Tomita. "Data Processing and Analysis in Liquid Chromatography–Mass Spectrometry-Based Targeted Metabolomics." In Mass Spectrometry for Metabolomics, 241–55. New York, NY: Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-2699-3_21.

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Karp, Natasha A. "Mass Spectrometry for Microbial Proteomics: Issues in Data Analysis with Electrophoretic or Mass Spectrometric Expression Proteomic Data." In Mass Spectrometry for Microbial Proteomics, 423–40. Chichester, UK: John Wiley & Sons, Ltd, 2010. http://dx.doi.org/10.1002/9780470665497.ch18.

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Conference papers on the topic "Mass spectrometry data"

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Sarycheva, Anastasia, Anton Grigoryev, Evgeny N. Nikolaev, and Yury Kostyukevich. "Robust Simulation Of Imaging Mass Spectrometry Data." In 35th ECMS International Conference on Modelling and Simulation. ECMS, 2021. http://dx.doi.org/10.7148/2021-0192.

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Mass spectrometry imaging (MSI) with high resolution in mass and space is an analytical method that produces distributions of ions on a sample surface. The algorithms for preprocessing and analysis of the raw data acquired from a mass spectrometer should be evaluated. To do that, the ion composition at every point of the sample should be known. This is possible via the employment of a simulated MSI dataset. In this work, we suggest a pipeline for a robust simulation of MSI datasets that resemble real data with an option to simulate the spectra acquired from any mass spectrometry instrument through the use of the experimental MSI datasets to extract simulation parameters.
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Muir, E. R., I. J. Ndiour, N. A. Le Goasduff, R. A. Moffitt, Y. Liu, M. C. Sullards, A. H. Merrill, Y. Chen, and M. D. Wang. "Multivariate Analysis of Imaging Mass Spectrometry Data." In 7th IEEE International Conference on Bioinformatics and Bioengineering. IEEE, 2007. http://dx.doi.org/10.1109/bibe.2007.4375603.

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Mukosaka, Shinichi, Kanae Teramoto, and Hideki Koike. "mzRepeat: Visual analysis of lipids in mass spectrometry." In 2012 IEEE Symposium on Biological Data Visualization (BioVis). IEEE, 2012. http://dx.doi.org/10.1109/biovis.2012.6378594.

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Awedat, Khalfalla, Ikhlas Abdel-Qader, and James R. Springstead. "Mass spectrometry sensing data for robust cancer classification." In 2016 IEEE International Conference on Electro Information Technology (EIT). IEEE, 2016. http://dx.doi.org/10.1109/eit.2016.7535250.

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Li, Yifeng, Yihui Liu, and Li Bai. "Genetic algorithm based feature selection for mass spectrometry data." In 2008 8th IEEE International Conference on Bioinformatics and BioEngineering (BIBE). IEEE, 2008. http://dx.doi.org/10.1109/bibe.2008.4696664.

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Gullo, Francesco, Giovanni Ponti, Andrea Tagarelli, Giuseppe Tradigo, and Pierangelo Veltri. "MSPtool: A Versatile Tool for Mass Spectrometry Data Preprocessing." In 2008 21st International Symposium on Computer-Based Medical Systems (CBMS). IEEE, 2008. http://dx.doi.org/10.1109/cbms.2008.53.

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Nakamoto, Takamichi, and Tomomasa Nakama. "Odor Recorder Using Mass Spectrometry and Large-scale Data." In 2007 IEEE Sensors. IEEE, 2007. http://dx.doi.org/10.1109/icsens.2007.4388364.

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Ressom, Habtom W., Rency S. Varghese, Lenka Goldman, Christopher A. Loffredo, Mohamed Abdel-Hamid, Zuzana Kyselova, Yehia Mechref, Milos Novotny, and Radoslav Goldman. "Analysis of mass spectrometry data for serum biomarker discovery." In 2007 IEEE/NIH Life Science Systems and Applications Workshop. IEEE, 2007. http://dx.doi.org/10.1109/lssa.2007.4400912.

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Murad, William, Rahul Singh, and Ten-Yang Yen. "Polynomial-time disulfide bond determination using mass spectrometry data." In 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshop, BIBMW. IEEE, 2009. http://dx.doi.org/10.1109/bibmw.2009.5332141.

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Ke, Jiqing, Lei Zhu, Bin Han, Qi Dai, Yaojia Wang, Lihua Li, Shenhua Xu, Hanzhou Mou, and Zhiguo Zheng. "Sparse representation based feature selection for mass spectrometry data." In 2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW). IEEE, 2010. http://dx.doi.org/10.1109/bibmw.2010.5703773.

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Reports on the topic "Mass spectrometry data"

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Sklarew, Debbie S., and Alexandre V. Mitroshkov. Review of Mass Spectrometry Data from Waste Tank Headspace Analyses. Office of Scientific and Technical Information (OSTI), February 2006. http://dx.doi.org/10.2172/878137.

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Gutjahr, A., F. Phillips, P. W. Kubik, and D. Elmore. An improved method for statistical analysis of raw accelerator mass spectrometry data. Office of Scientific and Technical Information (OSTI), January 1987. http://dx.doi.org/10.2172/6329593.

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Fowler, M. G., and M. Obermajer. Gas chromatography - mass spectrometry data of Jeanne d'Arc Basin source rock saturate fractions. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2002. http://dx.doi.org/10.4095/213487.

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Stewart, B. J. Mass Spectrometry Data Set for Renal Cell Carcinoma and Polycystic Kidney Disease Cell Models. Office of Scientific and Technical Information (OSTI), January 2017. http://dx.doi.org/10.2172/1342001.

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Fowler, M. G., and M. Obermajer. Gas chromatography - mass spectrometry data of Jeanne d'Arc Basin (offshore Newfoundland) oil saturate fractions. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2002. http://dx.doi.org/10.4095/213488.

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Trimble, D. J. DATA ANALYSIS K-WEST BASIN CANISTER LIQUID AND GAS SAMPLES AND GAMMA ENERGY ANALYSIS AND MASS SPECTROMETRY DATA. Office of Scientific and Technical Information (OSTI), February 1996. http://dx.doi.org/10.2172/16098.

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Fowler, M. G., P. W. Brooks, and L. R. Snowdon. Gas Chromatography and Gas Chromatography - Mass Spectrometry Data of Some Jeanne D'arc Basin Oil Saturate Fractions. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1989. http://dx.doi.org/10.4095/130688.

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Obermajer, M., K. Dewing, and M. G. Fowler. Geological and geochemical data from the Canadian Arctic Islands. Part VIII: Saturate fraction gas chromatography-mass spectrometry data for organic extracts. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2008. http://dx.doi.org/10.4095/224969.

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Obermajer, M., K. Dewing, and M. G. Fowler. Geological and geochemical data from the Canadian Arctic Islands. Part IX: Saturate fraction gas chromatography-mass spectrometry data for hydrocarbon samples. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2008. http://dx.doi.org/10.4095/226238.

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10

Fokin, Vladimir. Mathematical Modeling and Analysis of Mass Spectrometry Data in Workflows for the Discovery of Biomarkets in Breast Cancer. Fort Belvoir, VA: Defense Technical Information Center, July 2008. http://dx.doi.org/10.21236/ada513471.

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