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Books on the topic 'Protein and gene networks'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Protein networks and pathway analysis. Dordrecht: Humana Press, 2009.

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2

Frishman, Dmitrij. Modern genome annotation: The BioSapiens Network. New York: Springer, 2009.

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3

Modern genome annotation: The BioSapiens Network. New York: Springer, 2009.

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4

Nagai, Ryōzō. The biology of Krüppel-like factors. Tokyo: Springer, 2009.

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5

Canzar, Stefan, and Francisca Rojas Ringeling, eds. Protein-Protein Interaction Networks. New York, NY: Springer US, 2020. http://dx.doi.org/10.1007/978-1-4939-9873-9.

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6

Panchenko, Anna, and Teresa Przytycka, eds. Protein-protein Interactions and Networks. London: Springer London, 2008. http://dx.doi.org/10.1007/978-1-84800-125-1.

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7

Kasid, Usha, and Robert Clarke, eds. Cancer Gene Networks. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-6539-7.

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8

Deplancke, Bart, and Nele Gheldof, eds. Gene Regulatory Networks. Totowa, NJ: Humana Press, 2012. http://dx.doi.org/10.1007/978-1-61779-292-2.

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9

Weber, Wilfried, and Martin Fussenegger, eds. Synthetic Gene Networks. Totowa, NJ: Humana Press, 2012. http://dx.doi.org/10.1007/978-1-61779-412-4.

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10

Sanguinetti, Guido, and Vân Anh Huynh-Thu, eds. Gene Regulatory Networks. New York, NY: Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-8882-2.

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11

Castelli-Gair Hombría, James, and Paola Bovolenta, eds. Organogenetic Gene Networks. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42767-6.

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12

Xue, Jiaping. Molecular studies of myrosinase in Brassicaceae: From protein to gene and gene to protein. Uppsala, Sweden: Uppsala Genetic Center, Dept. of Cell Research, Swedish University of Agricultural Sciences, 1994.

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13

Kaufmann, Kerstin, and Bernd Mueller-Roeber, eds. Plant Gene Regulatory Networks. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-7125-1.

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14

F, Millot, and Francis J. L. 1952-, eds. Genetic biochemistry: From gene to protein. Chichester: Ellis Horwood, 1988.

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15

1933-, Thompson E. Brad, and Papaconstantinou John, eds. DNA: Protein interactions and gene regulation. Austin: University of Texas Press, 1987.

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16

Wilson, Joanne. DNA-protein interactions in Prolactin gene regulation. Manchester: University of Manchester, 1994.

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17

Spek, C. Arnold. Characterization of the human protein C gene promoter. Leiden: University of Leiden, 1998.

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18

Protein interaction networks: Computational analysis. Cambridge: Cambridge University Press, 2009.

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19

Nikolsky, Yuri, and Julie Bryant, eds. Protein Networks and Pathway Analysis. Totowa, NJ: Humana Press, 2009. http://dx.doi.org/10.1007/978-1-60761-175-2.

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20

Ionescu, Daniela. Protein-protein interactions between the breast cancer susceptibility gene product BRCA2 and replication protein A. Ottawa: National Library of Canada, 1999.

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21

Marchese, Adriano. Analysis of gene duplications in the G protein abundance and gene expression. Ottawa: National Library of Canada, 1993.

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22

Kennedy, Breandán Noel. Molecular characterisation of cellular retinaldehyde binding protein: Gene regulation and protein function. Dublin: University College Dublin, 1998.

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23

Bacterial Regulatory Networks. Norfolk, U.K: Caister Academic Press, 2012.

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24

Babu, M. Madan. Bacterial gene regulation and transcriptional networks. Norfolk, UK: Caister Academic Press, 2013.

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25

Gene regulatory networks: Methods and protocols. New York: Humana Press, 2012.

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26

Przytycka, Teresa, and Anna Panchenko. Protein-protein interactions and networks: Identification, computer analysis, and prediction. [New York]: Springer, 2010.

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27

1980-, Guzzi Pietro Hiram, ed. Data management of protein interaction networks. Hoboken, NY: Wiley, 2012.

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28

Cannataro, Mario, and Pietro Hiram Guzzi. Data Management of Protein Interaction Networks. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9781118103746.

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29

J, Kay, Ballard F. J, and Mayer R. J, eds. Gene expression: Regulation at the RNA and protein levels. London: Biochemical Society, 1989.

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30

Martin, Patricia E. M. Control of protein synthesis by the reovirus S4 gene. [s.l.]: typescript, 1991.

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31

You, Yongping. Targets in gene therapy. Rijeka: InTech, 2011.

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32

1969-, Li Xiao-Li, and Ng See-Kiong, eds. Biological data mining in protein interaction networks. Hershey, PA: Medical Information Science Reference, 2009.

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33

Shmulevich, Ilya. Probabilistic boolean networks: The modeling and control of gene regulatory networks. Philadelphia: Society for Industrial and Applied Mathematics, 2010.

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34

Shmulevich, Ilya. Probabilistic boolean networks: The modeling and control of gene regulatory networks. Philadelphia: Society for Industrial and Applied Mathematics, 2010.

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35

R, Dougherty Edward, and Society for Industrial and Applied Mathematics., eds. Probabilistic boolean networks: The modeling and control of gene regulatory networks. Philadelphia: Society for Industrial and Applied Mathematics, 2010.

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36

Shmulevich, Ilya. Probabilistic boolean networks: The modeling and control of gene regulatory networks. Philadelphia: Society for Industrial and Applied Mathematics, 2010.

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37

Nikolsky, Yuri, and Julie Bryant. Protein Networks and Pathway Analysis. Humana Press, 2011.

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38

Frishman, Dmitrij, and Alfonso Valencia. Modern Genome Annotation: The Biosapiens Network. Springer, 2011.

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39

Ryutaro, Utsumi, ed. Bacterial signal transduction: Networks and drug targets. New York: Springer Science+Business Media, 2008.

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40

Utsumi, Ryutaro. Bacterial Signal Transduction: Networks and Drug Targets. Springer London, Limited, 2008.

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41

Schadt, Eric E. Network Methods for Elucidating the Complexity of Common Human Diseases. Edited by Dennis S. Charney, Eric J. Nestler, Pamela Sklar, and Joseph D. Buxbaum. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190681425.003.0002.

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Abstract:

The life sciences are now a significant contributor to the ever expanding digital universe of data, and stand poised to lead in both the generation of big data and the realization of dramatic benefit from it. We can now score variations in DNA across whole genomes; RNA levels and alternative isoforms, metabolite levels, protein levels, and protein state information across the transcriptome, metabolome and proteome; methylation status across the methylome; and construct extensive protein–protein and protein–DNA interaction maps, all in a comprehensive fashion and at the scale of populations of individuals. This chapter describes a number of analytical approaches aimed at inferring causal relationships among variables in very large-scale datasets by leveraging DNA variation as a systematic perturbation source. The causal inference procedures are also demonstrated to enhance the ability to reconstruct truly predictive, probabilistic causal gene networks that reflect the biological processes underlying complex phenotypes like disease.

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42

Volff, J. N., ed. Gene and Protein Evolution. S. Karger AG, 2007. http://dx.doi.org/10.1159/isbn.978-3-8055-8341-1.

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43

Chromosomal Protein Gene Expr. Plenum Press, 1985.

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44

Jean-Nicolas, Volff, ed. Gene and protein evolution. Basel: Karger, 2007.

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45

Clarke, Robert, and Usha Kasid. Cancer Gene Networks. Springer New York, 2016.

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46

Gene Regulatory Networks. Elsevier, 2020. http://dx.doi.org/10.1016/s0070-2153(20)x0005-6.

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47

Clarke, Robert, and Usha Kasid. Cancer Gene Networks. Springer New York, 2018.

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48

Donev, Rossen. Protein Interaction Networks. Elsevier Science & Technology, 2022.

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49

Donev, Rossen. Protein Interaction Networks. Elsevier Science & Technology Books, 2022.

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50

Protein Interaction Networks. Elsevier, 2022. http://dx.doi.org/10.1016/s1876-1623(22)x0005-9.

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