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099 eBook O'Reilly for Public Libraries
245 00 Algorithmic and artificial intelligence methods for
protein bioinformatics /|cedited by Yi Pan, Jianxin Wang,
Min Li.|h[O'Reilly electronic resource]
260 Hoboken, N.J. :|bJ. Wiley & Sons,|c©2014.
300 1 online resource (1 volume) :|billustrations
336 text|btxt|2rdacontent
337 computer|bc|2rdamedia
338 online resource|bcr|2rdacarrier
504 Includes bibliographical references and index.
505 0 Cover; Series; Title Page; Copyright; Preface;
Contributors; Part I: From Protein Sequence to Structure;
Chapter 1: Emphasizing The Role of Proteins in
Construction of the Developmental Genetic Toolkit in
Plants; 1.1 Introduction; 1.2 Evolutionary Developmental
(Evo-Devo) Roles in Embryogenesis of Plants (in
Developmental Plant Genetic Toolkit Formation); 1.3 Phases
in Embryogenesis in Arabidopsis Thaliana; 1.4 Analysis;
1.5 Conclusions; References; Bibliography; Chapter 2:
Protein Sequence Motif Information Discovery; 2.1
Introduction; 2.2 Granule Computing Approaches; 2.3
Experimental Setup.
505 8 2.4 Protein Sequence Motif Information Discovered by FGK
ModelReferences; Chapter 3: Identifying Calcium Binding
Sites in Proteins; 3.1 Introduction; 3.2 Methods; 3.3
Results and Discussion; 3.4 Conclusion; References;
Chapter 4: Review of Imbalanced Data Learning for Protein
Methylation Prediction; 4.1 Introduction; 4.2 Protein and
Methylation; 4.3 Related Works on Methylation Prediction;
4.4 Conclusion; Acknowledgments; References; Chapter 5:
Analysis and Prediction of Protein Posttranslational
Modification Sites; 5.1 Introduction; 5.2 Musite: A
Machine Learning Approach.
505 8 5.3 Musite Implementation5.4 Summary; Acknowledgments;
References; Part II: Protein Analysis and Prediction;
Chapter 6: Protein Local Structure Prediction; 6.1
Introduction; 6.2 Structural Cluster Approach; 6.3
Sequence Cluster Approach; 6.4 Support Vector Machines for
Local Protein Structure Prediction; 6.5 Clustering Support
Vector Machines for Local Protein Structure Prediction;
6.6 Experimental Results; References; Chapter 7: Protein
Structural Boundary Prediction; 7.1 Introduction; 7.2
Background; 7.3 New Binary Classifiers for Protein
Structural Boundary Prediction; 7.4 Conclusion.
505 8 9.4 Experimental Results9.5 Conclusions and Future
Directions; Acknowledgments; References; Chapter 10:
Protein Contact Order Prediction: Update; 10.1
Introduction; 10.2 Correlated protein properties; 10.3
Other contact measurements; 10.4 Contact order
calculation; 10.5 Contact order prediction by homology;
10.6 Contact order prediction from sequence; 10.7 The
public contact order web server; 10.8 Conclusions;
References; Chapter 11: Progress in Prediction of
Oxidation States of Cysteines via Computational
Approaches; 11.1 Introduction.
520 An in, depth look at the latest research, methods, and
applications in the field of protein bioinformatics This
book presents the latest developments in protein
bioinformatics, introducing for the first time cutting,
edge research results alongside novel algorithmic and AI
methods for the analysis of protein data. In one complete,
self, contained volume, Algorithmic and Artificial
Intelligence Methods for Protein Bioinformatics addresses
key challenges facing both computer scientists and
biologists, arming readers with tools and techniques for
analyzing and interpreting protein data and solving a
variety of biological problems. Featuring a collection of
authoritative articles by leaders in the field, this work
focuses on the analysis of protein sequences, structures,
and interaction networks using both traditional algorithms
and AI methods. It also examines, in great detail, data
preparation, simulation, experiments, evaluation methods,
and applications. Algorithmic and Artificial Intelligence
Methods for Protein Bioinformatics: -Highlights protein
analysis applications such as protein, related drug
activity comparison -Incorporates salient case studies
illustrating how to apply the methods outlined in the book
-Tackles the complex relationship between proteins from a
systems biology point of view -Relates the topic to other
emerging technologies such as data mining and
visualization -Includes many tables and illustrations
demonstrating concepts and performance figures Algorithmic
and Artificial Intelligence Methods for Protein
Bioinformatics is an essential reference for
bioinformatics specialists in research and industry, and
for anyone wishing to better understand the rich field of
protein bioinformatics.
588 0 Print version record.
590 O'Reilly|bO'Reilly Online Learning: Academic/Public
Library Edition
650 0 Proteins|xAnalysis|xMathematics.
650 0 Bioinformatics.
650 0 Artificial intelligence.
650 0 Computational biology.
650 6 Protéines|xAnalyse|xMathématiques.
650 6 Bio-informatique.
650 6 Intelligence artificielle.
650 7 artificial intelligence.|2aat
650 7 Computational biology|2fast
650 7 Artificial intelligence|2fast
650 7 Bioinformatics|2fast
700 1 Pan, Yi,|d1960-
700 1 Wang, Jianxin,|d1969-
700 1 Li, Min,|d1978-
776 08 |iPrint version:|tAlgorithmic and artificial intelligence
methods for protein bioinformatics.|dHoboken, New Jersey :
John Wiley & Sons, Inc., [2014]|z9781118345788|w(DLC)
2012040076|w(OCoLC)808215922
856 40 |uhttps://ezproxy.naperville-lib.org/login?url=https://
learning.oreilly.com/library/view/~/9781118567814/?ar
|zAvailable on O'Reilly for Public Libraries
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