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Books on the topic 'Metabolomic analyses'

Author: Grafiati
Published: 25 June 2022

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1

Xia, Yinglin, and Jun Sun. Microbiome and Metabolomics: Statistical Data Analyses. Washington, DC, USA: American Chemical Society, 2022. http://dx.doi.org/10.1021/acsinfocus.7e5003.

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2

Vaidyanathan, Seetharaman, George G. Harrigan, and Royston Goodacre, eds. Metabolome Analyses: Strategies for Systems Biology. Boston, MA: Springer US, 2005. http://dx.doi.org/10.1007/b106967.

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3

Li, Shuzhao, ed. Computational Methods and Data Analysis for Metabolomics. New York, NY: Springer US, 2020. http://dx.doi.org/10.1007/978-1-0716-0239-3.

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4

Xia, Yinglin, Jun Sun, and Xiaotao Shen, Ph.D., Stanford University School of Medicine. Statistical Data Analysis of Microbiomes and Metabolomics. Washington, DC, USA: American Chemical Society, 2022. http://dx.doi.org/10.1021/acsinfocus.7e5035.

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5

Datta, Susmita, and Bart J. A. Mertens, eds. Statistical Analysis of Proteomics, Metabolomics, and Lipidomics Data Using Mass Spectrometry. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-45809-0.

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6

Roessner, Ute, and Daniel Anthony Dias. Metabolomics tools for natural product discovery: Methods and protocols. New York: Humana Press, 2013.

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7

Bagchi, Debasis. Genomics, proteomics, and metabolomics in nutraceuticals and functional foods. Ames, Iowa: Wiley-Blackwell, 2010.

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8

Bagchi, Debasis, Anand Swaroop, and Manashi Bagchi. Genomics, proteomics and metabolomics in nutraceuticals and functional foods. Chichester, West Sussex: John Wiley & Sons, Inc., 2015.

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9

Bjerrum, Jacob T. Metabonomics: Methods and protocols. New York: Humana Press, 2015.

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10

Armitage, Emily G. Correlation-based network analysis of cancer metabolism: A new systems biology approach in metabolomics. New York: Springer, 2014.

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11

Molecular analysis and genome discovery. 2nd ed. Chichester, West Sussex: John Wiley & Sons, 2011.

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12

Metabolic flux analysis: Methods and protocols. New York: Humana Press, 2014.

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13

K, Saito, Dixon Richard A. 1951-, and Willmitzer Lothar, eds. Plant metabolomics. Berlin: Springer, 2006.

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14

Villas-Boas, Silas G., Jorn Smedsgaard, Michael A. E. Hansen, Ute Roessner-Tunali, and Jens Nielsen. Metabolome Analysis: An Introduction. Wiley & Sons, Incorporated, John, 2007.

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15

Villas-Boas, Silas G., Jorn Smedsgaard, Michael A. E. Hansen, Ute Roessner-Tunali, and Jens Nielsen. Metabolome Analysis: An Introduction. Wiley & Sons, Incorporated, John, 2009.

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16

Wolfram, Weckwerth, ed. Metabolomics: Methods and protocols. Totowa, N.J: Humana Press, 2007.

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17

E, Burczynski Michael, and Rockett John C, eds. Surrogate tissue analysis: Genomic, proteomic and metabolomic approaches. Boca Raton: Taylor & Francis, 2005.

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18

(Editor), Michael E. Burczynski, and John C. Rockett (Editor), eds. Surrogate Tissue Analysis: Genomic, Proteomic, and Metabolomic Approaches. CRC, 2005.

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19

Surrogate tissue analysis: Genomic, proteomic, and metabolomic approaches. Boca Raton, FL: CRC/Taylor & Francis, 2004.

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20

Metabolome Analyses:: Strategies for Systems Biology. Springer, 2006.

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21

Vaidyanathan, Seetharaman, George G. Harrigan, and Royston Goodacre. Metabolome Analyses : : Strategies for Systems Biology. Springer, 2010.

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22

(Editor), Seetharaman Vaidyanathan, George G. Harrigan (Editor), and Royston Goodacre (Editor), eds. Metabolome Analyses:: Strategies for Systems Biology. Springer, 2005.

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23

Plant Metabolomics (Biotechnology in Agriculture and Forestry). Springer, 2006.

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24

Clay, Elizabeth. Chapter 11 Metabolomics in the Analysis of Inflammatory Diseases. InTechOpen, 2012.

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25

Metabolomics Data Processing and Data Analysis—Current Best Practices. MDPI, 2021. http://dx.doi.org/10.3390/books978-3-0365-1195-5.

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26

Weckwerth, Wolfram. Metabolomics: Methods and Protocols (Methods in Molecular Biology). Humana Press, 2006.

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27

Datta, Susmita, and Bart J. A. Mertens. Statistical Analysis of Proteomics, Metabolomics, and Lipidomics Data Using Mass Spectrometry. Springer, 2018.

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28

Metabolome Analysis: An Introduction (Wiley - Interscience Series on Mass Spectrometry). Wiley-Interscience, 2007.

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29

Bagchi, Debasis, Manashi Bagchi, and Francis Lau. Genomics, Proteomics and Metabolomics in Nutraceuticals and Functional Foods. Wiley & Sons, Incorporated, John, 2010.

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30

Debasis, Bagchi, Lau Francis, and Bagchi Manashi, eds. Genomics, proteomics, and metabolomics in nutraceuticals and functional foods. Ames, Iowa: Wiley-Blackwell, 2010.

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31

Gamete and Embryo Selection: Genomics, Metabolomics and Morphological Assessment. Springer, 2014.

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32

Bagchi, Debasis, Anand Swaroop, and Manashi Bagchi. Genomics, Proteomics and Metabolomics in Nutraceuticals and Functional Foods. Wiley & Sons, Incorporated, John, 2015.

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33

Munro, Carol A., and Duncan Wilson. Fungal genomics and transcriptomics. Edited by Christopher C. Kibbler, Richard Barton, Neil A. R. Gow, Susan Howell, Donna M. MacCallum, and Rohini J. Manuel. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780198755388.003.0006.

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Abstract:
The advent of whole-genome sequencing has resulted in a range of platforms for large-scale analysis of the DNA (genomics), RNA (transcriptomics), protein (proteomics), and metabolite (metabolomics) content of cells. These inclusive ‘omics’ approaches have allowed for unparalleled insights into fungal biology. In this chapter we will discuss how genomics and transcriptomics have been used to broaden our understanding of the biology of human pathogenic fungi and their interactions with their hosts.
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34

Vermeulen, Roel, Douglas A. Bell, Dean P. Jones, Montserrat Garcia-Closas, Avrum Spira, Teresa W. Wang, Martyn T. Smith, Qing Lan, and Nathaniel Rothman. Application of Biomarkers in Cancer Epidemiology. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190238667.003.0006.

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Advancements in OMICs are now enabling investigators to explore comprehensively the biological consequences of exogenous and endogenous exposures by detecting molecular signatures of exposure, early signs of adverse biological effects, preclinical disease, and molecularly defined cancer subtypes. These new technologies have proven invaluable for assembling a comprehensive portrait of human exposure, health, and disease. This includes hypothesis-driven biomarkers, as well as platforms that can agnostically analyze entire biologic processes and “compartments,” including the measurement of small molecules (metabolomics), DNA polymorphisms and rarer inherited variants (genomics), methylation and microRNA (epigenomics), chromosome-wide alterations, mRNA (transcriptomics), proteins (proteomics), and the microbiome (microbiomics). Although the implementation of these technologies in epidemiologic studies has already shown great promise, some challenges of particular importance must be addressed. Non-genetic OMIC markers vary over time due to both random variation and physiologic changes. Therefore, there is an urgent need for cohorts to collect repeat biological samples over time.
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35

Suffredini, Anthony F., and J. Perren Cobb. Genetic and molecular expression patterns in critical illness. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199600830.003.0031.

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Investigators who study RNA, proteins, or metabolites use analytic platforms that simultaneously measure changes in the relative abundance of thousands of molecules in a single biological sample. Over the last decade, the application of these high-throughput, genome-wide platforms to study critical illness and injury has generated huge quantities of data that require specialized computational skills for analysis. These investigations hold promise for improving our understanding of the host response, thereby transforming the practice of intensive care. This chapter summarizes recent technological and computational approaches used in genomics, proteomics, and metabolomics. While major advances have been made with these approaches when applied to chronic diseases, the acute nature of critical illness and injury has unique challenges. The rapidity of initiating events, the trajectory of inflammation that follows injury or infection and the interplay of host responses to a replicating infection, all have major effects on changes in gene and molecular expression. This complexity is further accentuated by measurement that may vary with the timing and type of tissue sampled after the critical event. In addition, the hunt for novel molecular markers holds promise for identifying patients at risk for severe illness and for enabling more individualized therapy.
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