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Journal articles on the topic 'Mass spectrometry data'

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Published: 10 December 2022
Last updated: 28 January 2023

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Dutta, Mainak, Elavarasan Subramani, Khushman Taunk, Akshada Gajbhiye, Shubhendu Seal, Namita Pendharkar, Snigdha Dhali, et al. "Mass spectrometry and bioinformatics analysis data." Data in Brief 2 (March 2015): 21–25. http://dx.doi.org/10.1016/j.dib.2014.11.002.

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12

Yates, John R. "Database searching using mass spectrometry data." Electrophoresis 19, no. 6 (May 1998): 893–900. http://dx.doi.org/10.1002/elps.1150190604.

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13

Courcelle, E., F. Pratbernou, and J. C. Prome. "A simple mass spectrometry data system dedicated to fast atom bombardment mass spectrometry." Review of Scientific Instruments 60, no. 10 (October 1989): 3181–87. http://dx.doi.org/10.1063/1.1140549.

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14

Hart, Kevin J., and Chris G. Enke. "An Automated Chemical structure Elucidation System (ACES) for mass spectrometry/mass spectrometry data." Chemometrics and Intelligent Laboratory Systems 8, no. 3 (July 1990): 293–302. http://dx.doi.org/10.1016/0169-7439(90)80018-2.

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15

Ryu, So Young, and George A. Wendt. "MetaMSD: meta analysis for mass spectrometry data." PeerJ 7 (April 10, 2019): e6699. http://dx.doi.org/10.7717/peerj.6699.

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Mass spectrometry-based proteomics facilitate disease understanding by providing protein abundance information about disease progression. For the same type of disease studies, multiple mass spectrometry datasets may be generated. Integrating multiple mass spectrometry datasets can provide valuable information that a single dataset analysis cannot provide. In this article, we introduce a meta-analysis software, MetaMSD (Meta Analysis for Mass Spectrometry Data) that is specifically designed for mass spectrometry data. Using Stouffer’s or Pearson’s test, MetaMSD detects significantly more differential proteins than the analysis based on the single best experiment. We demonstrate the performance of MetaMSD using simulated data, urinary proteomic data of kidney transplant patients, and breast cancer proteomic data. Noting the common practice of performing a pilot study prior to a main study, this software will help proteomics researchers fully utilize the benefit of multiple studies (or datasets), thus optimizing biomarker discovery. MetaMSD is a command line tool that automatically outputs various graphs and differential proteins with confidence scores. It is implemented in R and is freely available for public use at https://github.com/soyoungryu/MetaMSD. The user manual and data are available at the site. The user manual is written in such a way that scientists who are not familiar with R software can use MetaMSD.
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16

Kermani, Nazanin Zounemat, Xian Yang, Yike Guo, James McKenzie, and Zoltan Takats. "A Bi-directional Hierarchical Clustering (BHC) for Peak Matching of Large Mass Spectrometry Data Sets." International Journal of Machine Learning and Computing 11, no. 6 (November 2021): 373–79. http://dx.doi.org/10.18178/ijmlc.2021.11.6.1064.

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17

Dunn, W. J., and Dorothy Swain. "Computer-assisted interpretation of mass spectrometry—mass spectrometry data of potentially hazardous environmental compounds." Chemometrics and Intelligent Laboratory Systems 19, no. 2 (June 1993): 175–79. http://dx.doi.org/10.1016/0169-7439(93)80101-m.

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18

Tsugawa, Hiroshi. "Predicting Molecular Formula from Mass Spectrometry Data." Journal of the Mass Spectrometry Society of Japan 70, no. 2 (June 1, 2022): 133–34. http://dx.doi.org/10.5702/massspec.s22-28.

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19

Gambin, Anna, and Bogusław Kluge. "Modeling Proteolysis from Mass Spectrometry Proteomic Data." Fundamenta Informaticae 103, no. 1-4 (2010): 89–104. http://dx.doi.org/10.3233/fi-2010-320.

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20

Satoh, Takaya, Kentaro Takahara, Tomoaki Kondo, Ryo Ogasawara, and Chika Nogami. "Recent data analysis technique in mass spectrometry." Japanese Journal of Pesticide Science 42, no. 1 (2017): 203–15. http://dx.doi.org/10.1584/jpestics.w17-54.

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21

Sharma, Vagisha, Jimmy K. Eng, Michael J. MacCoss, and Michael Riffle. "A Mass Spectrometry Proteomics Data Management Platform." Molecular & Cellular Proteomics 11, no. 9 (May 18, 2012): 824–31. http://dx.doi.org/10.1074/mcp.o111.015149.

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22

Gaida, Antje, and Steffen Neumann. "MetHouse: Raw and Preprocessed Mass Spectrometry Data." Journal of Integrative Bioinformatics 4, no. 1 (March 1, 2007): 107–14. http://dx.doi.org/10.1515/jib-2007-56.

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Abstract We are developing a vendor-independent archive and on top of that a data warehouse for mass spectrometry metabolomics data. The archive schema resembles the communitydeveloped object model, the Java implementation of the model classes, and an editor (for both mzData XML files and the database) have been generated using the Eclipse Modeling Framework. Persistence is handled by the JDO2 -compliant framework JPOX. The main content of the Data Warehouse are the results of the signal processing and peak-picking tasks, carried out using the XCMS package from Bioconductor, putative identification and mass decomposition are added to the warehouse afterwards. We present the system architecture, current content, performance observations and describe the analysis tools on top of the warehouse. Availability: http://msbi.ipb-halle.de/
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23

Renard, Bernhard Y., Marc Kirchner, Hanno Steen, Judith AJ Steen, and Fred A. Hamprecht. "NITPICK: peak identification for mass spectrometry data." BMC Bioinformatics 9, no. 1 (2008): 355. http://dx.doi.org/10.1186/1471-2105-9-355.

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24

Ao Kong, Chinmaya Gupta, Mauro Ferrari, Marco Agostini, Chiara Bedin, Ali Bouamrani, Ennio Tasciotti, and Robert Azencott. "Biomarker Signature Discovery from Mass Spectrometry Data." IEEE/ACM Transactions on Computational Biology and Bioinformatics 11, no. 4 (July 1, 2014): 766–72. http://dx.doi.org/10.1109/tcbb.2014.2318718.

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25

Smilde, Age K., Mariët J. van der Werf, Sabina Bijlsma, Bianca J. C. van der Werff-van der Vat, and Renger H. Jellema. "Fusion of Mass Spectrometry-Based Metabolomics Data." Analytical Chemistry 77, no. 20 (October 2005): 6729–36. http://dx.doi.org/10.1021/ac051080y.

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26

Banks, David, and Emmanuel Petricoin. "Finding Cancer Signals in Mass Spectrometry Data." CHANCE 16, no. 4 (September 2003): 8–57. http://dx.doi.org/10.1080/09332480.2003.10554868.

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27

Fonville, Judith M., Claire L. Carter, Luis Pizarro, Rory T. Steven, Andrew D. Palmer, Rian L. Griffiths, Patricia F. Lalor, et al. "Hyperspectral Visualization of Mass Spectrometry Imaging Data." Analytical Chemistry 85, no. 3 (January 15, 2013): 1415–23. http://dx.doi.org/10.1021/ac302330a.

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28

Salmi, J., R. Moulder, J. J. Filen, O. S. Nevalainen, T. A. Nyman, R. Lahesmaa, and T. Aittokallio. "Quality classification of tandem mass spectrometry data." Bioinformatics 22, no. 4 (December 13, 2005): 400–406. http://dx.doi.org/10.1093/bioinformatics/bti829.

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29

Katajamaa, Mikko, and Matej Orešič. "Data processing for mass spectrometry-based metabolomics." Journal of Chromatography A 1158, no. 1-2 (July 2007): 318–28. http://dx.doi.org/10.1016/j.chroma.2007.04.021.

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30

Rajapakse, Jagath C., Kai-Bo Duan, and Wee Kiang Yeo. "Proteomic Cancer Classification with Mass Spectrometry Data." American Journal of PharmacoGenomics 5, no. 5 (2005): 281–92. http://dx.doi.org/10.2165/00129785-200505050-00001.

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31

Slotta, Douglas J., Lenwood S. Heath, Naren Ramakrishnan, Rich Helm, and Malcolm Potts. "Clustering mass spectrometry data using order statistics." PROTEOMICS 3, no. 9 (September 2003): 1687–91. http://dx.doi.org/10.1002/pmic.200300517.

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32

Xiong, Xingchuang, Wei Xu, Livia S. Eberlin, Justin M. Wiseman, Xiang Fang, You Jiang, Zejian Huang, Yukui Zhang, R. Graham Cooks, and Zheng Ouyang. "Data Processing for 3D Mass Spectrometry Imaging." Journal of The American Society for Mass Spectrometry 23, no. 6 (March 3, 2012): 1147–56. http://dx.doi.org/10.1007/s13361-012-0361-7.

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33

McLafferty, Fred W. "Billionfold data increase from mass spectrometry instrumentation." Journal of the American Society for Mass Spectrometry 8, no. 1 (January 1997): 1–7. http://dx.doi.org/10.1016/s1044-0305(96)00203-6.

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34

Sonderegger, Martin, Kristin Staniszewski, Andrew Meyers, and Gary Siuzdak. "A bioinformatics approach for mass spectrometry data processing: Applications to proteomics and small molecule analysis." Spectroscopy 16, no. 2 (2002): 81–87. http://dx.doi.org/10.1155/2002/529268.

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We have developed a web‒based software system,JULIAN, that simplifies the process of relaying mass spectral information for chemists, protein chemists, biochemists and all others performing mass spectrometry experiments through a centralized mass spectrometry laboratory.JULIANallows for relative ease in submitting compound information as well as instant access to analysis results from any networked computer equipped with a web browser. Compound information is centralized in a Microsoft Access database and results are available in Adobe's portable document format (PDF) from an NT4 server. This gives researchers the ability to easily obtain data and allows the analysts in the mass spectrometry lab to browse analysis results when assisting researchers with their inquiries. Due to this web‒based designJULIANis independent of the mass spectrometers' hardware and operating system. Approximately seven hundred on‒site and off‒site users have utilizedJULIANtransmitting over 40,000 analyses. The conversion from paper to electronic mass spectrometry data processing has enabled our Center to receive compound information, perform analysis, and relay the results four times faster than required previously.
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35

Nicolotti, Luca, Jeremy Hack, Markus Herderich, and Natoiya Lloyd. "MStractor: R Workflow Package for Enhancing Metabolomics Data Pre-Processing and Visualization." Metabolites 11, no. 8 (July 29, 2021): 492. http://dx.doi.org/10.3390/metabo11080492.

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Untargeted metabolomics experiments for characterizing complex biological samples, conducted with chromatography/mass spectrometry technology, generate large datasets containing very complex and highly variable information. Many data-processing options are available, however, both commercial and open-source solutions for data processing have limitations, such as vendor platform exclusivity and/or requiring familiarity with diverse programming languages. Data processing of untargeted metabolite data is a particular problem for laboratories that specialize in non-routine mass spectrometry analysis of diverse sample types across humans, animals, plants, fungi, and microorganisms. Here, we present MStractor, an R workflow package developed to streamline and enhance pre-processing of metabolomics mass spectrometry data and visualization. MStractor combines functions for molecular feature extraction with user-friendly dedicated GUIs for chromatographic and mass spectromerty (MS) parameter input, graphical quality-control outputs, and descriptive statistics. MStractor performance was evaluated through a detailed comparison with XCMS Online. The MStractor package is freely available on GitHub at the MetabolomicsSA repository.
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36

Honour, John W. "Benchtop mass spectrometry in clinical biochemistry." Annals of Clinical Biochemistry: International Journal of Laboratory Medicine 40, no. 6 (November 1, 2003): 628–38. http://dx.doi.org/10.1258/000456303770367216.

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Mass spectrometry has for many years enabled us to rapidly identify and quantify many different compounds in biological samples. The equipment is currently available in specialist centres investigating metabolic disorders and in toxicology laboratories. Improvements in sample introduction and refinements in the mass spectrometry hardware now allow higher sample throughput without extensive sample purification. Many mass spectrometers are compact and operated by computers that also assist data handling. Mass spectrometry has the potential to change hospital laboratory operations generally. Consideration of the practical and financial aspects of its application may reveal cost-effective means of improving the specificity of analyte assay. Considerable advantages are expected in the analysis of metabolites and drugs and in proteomics.
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37

Bravo-Rodriguez, Kenny, Birte Hagemeier, Lea Drescher, Marian Lorenz, Juliana Rey, Michael Meltzer, Farnusch Kaschani, Markus Kaiser, and Michael Ehrmann. "Utilities for Mass Spectrometry Analysis of Proteins (UMSAP): Fast post-processing of mass spectrometry data." Rapid Communications in Mass Spectrometry 32, no. 19 (September 5, 2018): 1659–67. http://dx.doi.org/10.1002/rcm.8243.

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38

Lu, Yang Young, Jeff Bilmes, Ricard A. Rodriguez-Mias, Judit Villén, and William Stafford Noble. "DIAmeter: matching peptides to data-independent acquisition mass spectrometry data." Bioinformatics 37, Supplement_1 (July 1, 2021): i434—i442. http://dx.doi.org/10.1093/bioinformatics/btab284.

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Abstract Motivation Tandem mass spectrometry data acquired using data independent acquisition (DIA) is challenging to interpret because the data exhibits complex structure along both the mass-to-charge (m/z) and time axes. The most common approach to analyzing this type of data makes use of a library of previously observed DIA data patterns (a ‘spectral library’), but this approach is expensive because the libraries do not typically generalize well across laboratories. Results Here, we propose DIAmeter, a search engine that detects peptides in DIA data using only a peptide sequence database. Although some existing library-free DIA analysis methods (i) support data generated using both wide and narrow isolation windows, (ii) detect peptides containing post-translational modifications, (iii) analyze data from a variety of instrument platforms and (iv) are capable of detecting peptides even in the absence of detectable signal in the survey (MS1) scan, DIAmeter is the only method that offers all four capabilities in a single tool. Availability and implementation The open source, Apache licensed source code is available as part of the Crux mass spectrometry analysis toolkit (http://crux.ms). Supplementary information Supplementary data are available at Bioinformatics online.
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39

Balamurugan, A. K., S. Dash, and A. K. Tyagi. "Mass spectral analysis and quantification of Secondary Ion Mass Spectrometry data." International Journal of Mass Spectrometry 386 (July 2015): 56–60. http://dx.doi.org/10.1016/j.ijms.2015.06.004.

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40

Le, Vu Anh, Cam Quyen Thi Phan, and Thuy Huong Nguyen. "Data mining in mass spectrometry-based proteomics studies." Science & Technology Development Journal - Engineering and Technology 2, no. 4 (March 24, 2020): 258–76. http://dx.doi.org/10.32508/stdjet.v2i4.483.

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The post-genomic era consists of experimental and computational efforts to meet the challenge of clarifying and understanding the function of genes and their products. Proteomic studies play a key role in this endeavour by complementing other functional genomics approaches, encompasses the large-scale analysis of complex mixtures, including the identification and quantification of proteins expressed under different conditions, the determination of their properties, modifications and functions. Understanding how biological processes are regulated at the protein level is crucial to understanding the molecular basis of diseases and often highlights the prevention, diagnosis and treatment of diseases. High-throughput technologies are widely used in proteomics to perform the analysis of thousands of proteins. Specifically, mass spectrometry (MS) is an analytical technique for characterizing biological samples and is increasingly used in protein studies because of its targeted, nontargeted, and high performance abilities. However, as large data sets are created, computational methods such as data mining techniques are required to analyze and interpret the relevant data. More specifically, the application of data mining techniques in large proteomic data sets can assist in many interpretations of data; it can reveal protein-protein interactions, improve protein identification, evaluate the experimental methods used and facilitate the diagnosis and biomarker discovery. With the rapid advances in mass spectrometry devices and experimental methodologies, MS-based proteomics has become a reliable and necessary tool for elucidating biological processes at the protein level. Over the past decade, we have witnessed a great expansion of our knowledge of human diseases with the adoption of proteomic technologies based on MS, which leads to many interesting discoveries. Here, we review recent advances of data mining in MS-based proteomics in biomedical research. Recent research in many fields shows that proteomics goes beyond the simple classification of proteins in biological systems and finally reaches its initial potential – as an essential tool to aid related disciplines, notably biomedical research. From here, there is great potential for data mining in MS-based proteomics to move beyond basic research, into clinical research and diagnostics.
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41

Forner, Francesca, Leonard Foster, and Stefano Toppo. "Mass Spectrometry Data Analysis in the Proteomics Era." Current Bioinformatics 2, no. 1 (January 1, 2007): 63–93. http://dx.doi.org/10.2174/157489307779314285.

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42

Kostelic, Marius M., Ciara K. Zak, Yang Liu, Victor Shugui Chen, Zhuchun Wu, Jared Sivinski, Eli Chapman, and Michael T. Marty. "UniDecCD: Deconvolution of Charge Detection-Mass Spectrometry Data." Analytical Chemistry 93, no. 44 (October 27, 2021): 14722–29. http://dx.doi.org/10.1021/acs.analchem.1c03181.

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43

Teleman, Johan, Andrew W. Dowsey, Faviel F. Gonzalez-Galarza, Simon Perkins, Brian Pratt, Hannes L. Röst, Lars Malmström, et al. "Numerical Compression Schemes for Proteomics Mass Spectrometry Data." Molecular & Cellular Proteomics 13, no. 6 (March 27, 2014): 1537–42. http://dx.doi.org/10.1074/mcp.o114.037879.

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44

Martens, Lennart, Matthew Chambers, Marc Sturm, Darren Kessner, Fredrik Levander, Jim Shofstahl, Wilfred H. Tang, et al. "mzML—a Community Standard for Mass Spectrometry Data." Molecular & Cellular Proteomics 10, no. 1 (August 17, 2010): R110.000133. http://dx.doi.org/10.1074/mcp.r110.000133.

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45

McLerran, Dale F., Ziding Feng, O. John Semmes, Lisa Cazares, and Timothy W. Randolph. "Signal Detection in High-Resolution Mass Spectrometry Data." Journal of Proteome Research 7, no. 1 (January 2008): 276–85. http://dx.doi.org/10.1021/pr700640a.

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46

Rasche, Florian, Aleš Svatoš, Ravi Kumar Maddula, Christoph Böttcher, and Sebastian Böcker. "Computing Fragmentation Trees from Tandem Mass Spectrometry Data." Analytical Chemistry 83, no. 4 (February 15, 2011): 1243–51. http://dx.doi.org/10.1021/ac101825k.

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47

Magee, J. T. "Pyrolysis Mass Spectrometry: Data Processing in Classification Studies." Zentralblatt für Bakteriologie 285, no. 2 (January 1997): 182–94. http://dx.doi.org/10.1016/s0934-8840(97)80026-6.

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48

Bonner, Ron, and Gérard Hopfgartner. "SWATH data independent acquisition mass spectrometry for metabolomics." TrAC Trends in Analytical Chemistry 120 (November 2019): 115278. http://dx.doi.org/10.1016/j.trac.2018.10.014.

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49

Hebert, Alexander S., Christian Thöing, Nicholas M. Riley, Nicholas W. Kwiecien, Evgenia Shiskova, Romain Huguet, Helene L. Cardasis, et al. "Improved Precursor Characterization for Data-Dependent Mass Spectrometry." Analytical Chemistry 90, no. 3 (January 11, 2018): 2333–40. http://dx.doi.org/10.1021/acs.analchem.7b04808.

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50

Saksena, Anshu, Dennis Lucarelli, and I.-Jeng Wang. "Bayesian model selection for mining mass spectrometry data." Neural Networks 18, no. 5-6 (July 2005): 843–49. http://dx.doi.org/10.1016/j.neunet.2005.06.046.

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