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|a 860626512
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|a 9781118567814
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|a QP551
|b .A4335 2014
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|a HCDD
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|a Algorithmic and artificial intelligence methods for protein bioinformatics /
|c edited by Yi Pan, Jianxin Wang, Min Li.
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|a Hoboken, N.J. :
|b J. Wiley & Sons,
|c ©2014.
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| 300 |
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|a 1 online resource (1 volume) :
|b illustrations
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| 336 |
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|a text
|b txt
|2 rdacontent
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|a online resource
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|a Print version record.
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| 504 |
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|a Includes bibliographical references and index.
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|a 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.
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|a 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.
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|a 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.
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|a 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.
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|a 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.
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| 650 |
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|a Proteins
|x Analysis
|x Mathematics.
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| 650 |
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|a Bioinformatics.
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| 650 |
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|a Artificial intelligence.
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| 650 |
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|a Computational biology.
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| 650 |
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7 |
|a artificial intelligence.
|2 aat
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| 650 |
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|a MEDICAL
|x Immunology.
|2 bisacsh
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| 650 |
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|a Computational biology
|2 fast
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| 650 |
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|a Artificial intelligence
|2 fast
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| 650 |
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|a Bioinformatics
|2 fast
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| 700 |
1 |
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|a Pan, Yi,
|d 1960-
|1 https://id.oclc.org/worldcat/entity/E39PCjvMmd3fPyGgp8QW78HPV3
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| 700 |
1 |
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|a Wang, Jianxin,
|d 1969-
|1 https://id.oclc.org/worldcat/entity/E39PCjwryfr4gVCvfGRd7xGjFq
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| 700 |
1 |
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|a Li, Min,
|d 1978-
|1 https://id.oclc.org/worldcat/entity/E39PCjrGc6hv3XTRbf3Bx64gDm
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| 758 |
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|i has work:
|a Algorithmic and Artificial Intelligence Methods for Protein Bioinformatics (Text)
|1 https://id.oclc.org/worldcat/entity/E39PCGyJfbvQkQBMTm6Cvqj7Xm
|4 https://id.oclc.org/worldcat/ontology/hasWork
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| 776 |
0 |
8 |
|i Print version:
|t Algorithmic and artificial intelligence methods for protein bioinformatics.
|d Hoboken, New Jersey : John Wiley & Sons, Inc., [2014]
|z 9781118345788
|w (DLC) 2012040076
|w (OCoLC)808215922
|
| 856 |
4 |
0 |
|u https://ebookcentral.proquest.com/lib/holycrosscollege-ebooks/detail.action?docID=1465941
|y Click for online access
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