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Books on the topic 'Computational analyses protein'

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Published: 6 September 2023

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1

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

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2

Nannan, Gao, ed. Lecture notes on computational mutation. New York: Nova Science Publishers, 2008.

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3

András, Aszódi, ed. Protein geometry, classification, topology and symmetry: A computational analysis of structure. Bristol: Institute of Physics Pub., 2005.

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4

Suhai, Sándor. Genomics and proteomics: Functional and computational aspects. New York: Kluwer Academic Publishers, 2002.

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5

D, Baxevanis Andreas, and Ouellette B. F. Francis, eds. Bioinformatics: A practical guide to the analysis of genes and proteins. 3rd ed. Hoboken, N.J: Wiley, 2005.

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6

A, Andrade Miguel, ed. Bioinformatics and genomes: Current perspectives. Wymondham, England: Horizon Scientific, 2003.

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7

D, Higgins, and Taylor W. R, eds. Bioinformatics: Sequence, structure, and databanks : a practical approach. Oxford: Oxford University Press, 2000.

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8

D, Baxevanis Andreas, and Ouellette B. F. Francis, eds. Bioinformatics: A practical guide to the analysis of genes and proteins. 2nd ed. New York, NY: Wiley-Interscience, 2001.

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9

C, Hoch Jeffrey, Poulsen Flemming M, Redfield Christina, North Atlantic Treaty Organization. Scientific Affairs Division., and NATO Advanced Research Workshop on Computational Aspects of the Study of Biological Macromolecules by Nuclear Magnetic Resonance Spectroscopy (1990 : Il Ciocco, Italy), eds. Computational aspects of the study of biological macromolecules by nuclear magnetic resonance spectroscopy. New York: Plenum Press, 1991.

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10

Mather A. R. Sadiq Al-Baghdadi. CFD models for analysis and design of PEM fuel cells CFD models for analysis & design of PEM fuel cells. New York: Nova Science Publishers, 2008.

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11

Proteome bioinformatics. New York, NY: Humana, 2010.

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12

Maher A. R. Sadiq Al-Baghdadi. CFD modeling and analysis of different novel designs of air-breathing PEM fuel cells. Hauppauge, N.Y: Nova Science Publishers, 2009.

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13

Maher A. R. Sadiq Al-Baghdadi. CFD modeling and analysis of different novel designs of air-breathing PEM fuel cells. New York: Nova Science Publishers, 2010.

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14

D, Baxevanis Andreas, ed. Current protocols in bioinformatics. New York: Wiley, 2003.

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15

E, Vance Dennis, and Vance Jean E, eds. Biochemistry of lipids, lipoproteins, and membranes. Amsterdam: Elsevier, 1991.

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16

Zhang, Aidong. Protein Interaction Networks: Computational Analysis. Cambridge University Press, 2009.

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17

Zhang, Aidong. Protein Interaction Networks: Computational Analysis. Cambridge University Press, 2009.

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18

Zhang, Aidong. Protein Interaction Networks: Computational Analysis. Cambridge University Press, 2007.

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19

Zhang, Aidong. Protein Interaction Networks: Computational Analysis. Cambridge University Press, 2010.

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20

Sikosek, T. Tobias. Computational Methods in Protein Evolution. Springer New York, 2018.

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21

Ansari, Sam. Analysis of Protein-Protein Interactions- A Computational Approach. VDM Verlag Dr. Mueller e.K., 2007.

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22

Srihari, Sriganesh, Chern Han Yong, and Limsoon Wong. Computational Prediction of Protein Complexes from Protein Interaction Networks. Morgan & Claypool Publishers, 2017.

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23

Srihari, Sriganesh, Chern Han Yong, M. Tamer Ozsu, and Limsoon Wong. Computational Prediction of Protein Complexes from Protein Interaction Networks. Morgan & Claypool Publishers, 2017.

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24

Srihari, Sriganesh, Chern Han Yong, M. Tamer Ozsu, and Limsoon Wong. Computational Prediction of Protein Complexes from Protein Interaction Networks. Morgan & Claypool Publishers, 2017.

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25

Wang, Bing, Peng Chen, and Jun Zhang. Computational Intelligence in Protein-Ligand Interaction Analysis. Elsevier Science & Technology Books, 2021.

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26

Gromiha, M. Michael. Protein Interactions: Computational Methods, Analysis and Applications. World Scientific Publishing Co Pte Ltd, 2020.

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27

Wang, Bing, Peng Chen, and Jun Zhang. Computational Intelligence in Protein-Ligand Interaction Analysis. Elsevier Science & Technology, 2022.

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28

Aszodi, Andras, and William R. Taylor. Protein Geometry, Classification, Topology and Symmetry: A Computational Analysis of Structure. Taylor & Francis Group, 2004.

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29

Aszodi, Andras, and William R. Taylor. Protein Geometry, Classification, Topology and Symmetry: A Computational Analysis of Structure. Taylor & Francis Group, 2004.

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30

Aszodi, Andras, and William R. Taylor. Protein Geometry, Classification, Topology and Symmetry: A Computational Analysis of Structure (Series in Biophysics). Taylor & Francis, 2004.

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31

Das, Swagatam, Nanda Dulal Jana, and Jaya Sil. A Metaheuristic Approach to Protein Structure Prediction: Algorithms and Insights from Fitness Landscape Analysis. Springer, 2018.

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32

Das, Swagatam, Nanda Dulal Jana, and Jaya Sil. A Metaheuristic Approach to Protein Structure Prediction: Algorithms and Insights from Fitness Landscape Analysis. Springer, 2018.

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33

Christine, Orengo, Jones David, and Thornton Janet M, eds. Bioinformatics: Genes, proteins, and computers. Oxford: BIOS Scientific, 2003.

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34

Green, Peter, Kanti Mardia, Vysaul Nyirongo, and Yann Ruffieux. Bayesian modelling for matching and alignment of biomolecules. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.2.

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This article describes Bayesian modelling for matching and alignment of biomolecules. One particular task where statistical modelling and inference can be useful in scientific understanding of protein structure is that of matching and alignment of two or more proteins. In this regard, statistical shape analysis potentially has something to offer in solving biomolecule matching and alignment problems. The article discusses the use of Bayesian methods for shape analysis to assist with understanding the three-dimensional structure of protein molecules, with a focus on the problem of matching instances of the same structure in the CoMFA (Comparative Molecular Field Analysis) database of steroid molecules. It introduces a Bayesian hierarchical model for pairwise matching and for alignment of multiple configurations before concluding with an overview of some advantages of the Bayesian approach to problems in protein bioinformatics, along with modelling and computation issues, alternative approaches, and directions for future research.
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35

Suhai, Sándor. Genomics and Proteomics: Functional and Computational Aspects. Springer, 2013.

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36

Graham, Daniel J. Invitation to Protein Sequence Analysis Through Probability and Information. Taylor & Francis Group, 2019.

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37

Graham, Daniel J. Invitation to Protein Sequence Analysis Through Probability and Information. Taylor & Francis Group, 2019.

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38

Invitation to Protein Sequence Analysis Through Probability and Information. Taylor & Francis Group, 2019.

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39

Graham, Daniel J. Invitation to Protein Sequence Analysis Through Probability and Information. Taylor & Francis Group, 2021.

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40

Graham, Daniel J. Invitation to Protein Sequence Analysis Through Probability and Information. Taylor & Francis Group, 2019.

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41

Baxevanis, Andreas D., Gary D. Bader, and David S. Wishart. Bioinformatics. Wiley & Sons, Incorporated, John, 2020.

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42

Baxevanis, Andreas D., and B. F. Francis Ouellette. Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins, Second Edition. Wiley-Interscience, 2001.

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43

Boero, Mauro, and Masaru Tateno. Quantum-theoretical approaches to proteins and nucleic acids. Edited by A. V. Narlikar and Y. Y. Fu. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199533046.013.17.

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This article describes quantum methods used to study proteins and nucleic acids: Hartree–Fock all-electron approaches, density-functional theory approaches, and hybrid quantum-mechanics/molecular-mechanics approaches. In addition to an analysis of the electronic structure, quantum-mechanical approaches for simulating proteins and nucleic acids can elucidate the cleavage and formation of chemical bonds in biochemical reactions. This presents a computational challenge, and a number of methods have been proposed to overcome this difficulty, including enhanced temperature methods such as high-temperature molecular dynamics, parallel tempering and replica exchange. Alternative methods not relying on the knowledge a priori of the final products make use of biasing potentials to push the initial system away from its local minimum and to enhance the sampling of the free-energy landscape. This article considers two of these biasing techniques, namely Blue Moon and metadynamics.
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44

Henderson, Daniel A., R. J. Boys, Carole J. Proctor, and Darren J. Wilkinson. Linking systems biology models to data: A stochastic kinetic model of p53 oscillations. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.7.

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This article discusses the use of a stochastic kinetic model to study protein level oscillations in single living cancer cells, using the p53 and Mdm2 proteins as examples. It describes the refinement of a dynamic stochastic process model of the cellular response to DNA damage and compares this model to time course data on the levels of p53 and Mdm2. The article first provides a biological background on p53 and Mdm2 before explaining how the stochastic kinetic model is constructed. It then introduces the stochastic kinetic model and links it to the data and goes on to apply sophisticated MCMC methods to compute posterior distributions. The results demonstrate that it is possible to develop computationally intensive Markov chain Monte Carlo (MCMC) methods for conducting a Bayesian analysis of an intra-cellular stochastic systems biology model using single-cell time course data.
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45

Computational and Visualization Techniques for Structural Bioinformatics Using Chimera Chapman HallCRC Mathematical Computational Biology. CRC Press, 2013.

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46

1951-, Mewes Hans-Werner, Weiss B. 1966-, and Seidel Henrik, eds. Bioinformatics and genome analysis. Berlin: Springer, 2002.

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47

Bioinformatics and Genome Analysis. Springer, 2003.

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48

(Editor), D. Higgins, and Willie Taylor (Editor), eds. Bioinformatics: Sequence, Structure and Databanks: A Practical Approach (Practical Approach Series). Oxford University Press, USA, 2000.

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49

(Editor), Des Higgins, and Willie Taylor (Editor), eds. Bioinformatics: Sequence, Structure and Databanks: A Practical Approach. Oxford University Press, USA, 2000.

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50

Narlikar, A. V., and Y. Y. Fu, eds. Oxford Handbook of Nanoscience and Technology. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199533060.001.0001.

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This volume highlights engineering and related developments in the field of nanoscience and technology, with a focus on frontal application areas like silicon nanotechnologies, spintronics, quantum dots, carbon nanotubes, and protein-based devices as well as various biomolecular, clinical and medical applications. Topics include: the role of computational sciences in Si nanotechnologies and devices; few-electron quantum-dot spintronics; spintronics with metallic nanowires; Si/SiGe heterostructures in nanoelectronics; nanoionics and its device applications; and molecular electronics based on self-assembled monolayers. The volume also explores the self-assembly strategy of nanomanufacturing of hybrid devices; templated carbon nanotubes and the use of their cavities for nanomaterial synthesis; nanocatalysis; bifunctional nanomaterials for the imaging and treatment of cancer; protein-based nanodevices; bioconjugated quantum dots for tumor molecular imaging and profiling; modulation design of plasmonics for diagnostic and drug screening; theory of hydrogen storage in nanoscale materials; nanolithography using molecular films and processing; and laser applications in nanotechnology. The volume concludes with an analysis of the various risks that arise when using nanomaterials.
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