Genomics and Proteomics Engineering in Medicine and Biology / Edition 1

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Overview

Current applications and recent advances in genomics and proteomics

Genomics and Proteomics Engineering in Medicine and Biology presents a well-rounded, interdisciplinary discussion of a topic that is at the cutting edge of both molecular biology and bioengineering. Compiling contributions by established experts, this book highlights up-to-date applications of biomedical informatics, as well as advancements in genomics-proteomics areas. Structures and algorithms are used to analyze genomic data and develop computational solutions for pathological understanding.

Topics discussed include:

  • Qualitative knowledge models
  • Interpreting micro-array data
  • Gene regulation bioinformatics
  • Methods to analyze micro-array
  • Cancer behavior and radiation therapy
  • Error-control codes and the genome
  • Complex life science multi-database queries
  • Computational protein analysis
  • Tumor and tumor suppressor proteins interactions
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Editorial Reviews

From The Critics
Reviewer: Erick Snellman, PhD (The Citadel)
Description: This book is dedicated to detailed descriptions of methodologies in the integration and application of mathematics, engineering, and physical and computer sciences in studying complex biological systems. The contributors of the 10 chapters provide in-depth discussions on topics ranging from models in functional genomics and proteomics, methods of microarray data analyses to computational analysis between tumor and tumor suppressor proteins.
Purpose: The purpose is to provide an integrative reference for those interested in genomics and proteomics engineering. The book meets the increasing demand for such information from the fast growing multidisciplinary research audience.
Audience: Biological sciences and biomedical engineering students, genomics and proteomics researchers, and students studying systems biology are the intended audience. In addition, students and postdoctoral fellows in mathematics and computer sciences with applications to biological sciences would benefit from this reading this book. It also serves as an excellent reference for those teaching in the field. The editor, Professor Metin Akay, is an award winning scholar and author of over 14 books related to the topic.
Features: The chapters contain in-depth discussions on genomics and proteomics engineering ranging from models in functional genomics and proteomics, methods of microarray data analyses, gene regulation bioinformatics, multidatabase queries, and computational analysis of proteins. The book is especially robust in its coverage of microarray analysis and computational analysis of proteins. It is well illustrated with useful tables and figures. I found the illustrations depicting models and research strategies most helpful. Each chapter also uses a sufficient number of examples to illustrate key points.
Assessment: I found the book very useful. It is not intended for those learning bioinformatics or proteomic and genomic analysis, but for those already working in the discipline. It fills a void in the pertinent literature; there are few works that compare.
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Product Details

Meet the Author

METIN AKAY, PHD, is the Interim Chair and Professor of Bioengineering for the Harrington Department of Bioengineering at the Arizona State University. Dr. Akay is the founding Series Editor of the IEEE Press Series on Biomedical Engineering. In 1997, he received the prestigious Early Career Achievement Award from the IEEE Engineering in Medicine and Biology Society (EMBS). He was the program chair of both the annual IEEE EMBS Conference and Summer School for 2001. Dr. Akay has published several papers in the field and authored, coauthored, or edited fourteen books. He is also the editor in chief of the Wiley Encyclopedia of Biomedical Engineering.

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Table of Contents

Preface.

Contributors.

1. Qualitative Knowledge Models in Functional Genomics and Proteomics (Mor Peleg, Irene S. Gabashvili, and Russ B. Altman).

1.1. Introduction.

1.2. Methods and Tools.

1.3. Modeling Approach and Results.

1.4. Discussion.

1.5. Conclusion.

References.

2. Interpreting Microarray Data and Related Applications Using Nonlinear System Identification (Michael Korenberg).

2.1. Introduction.

2.2. Background.

2.3. Parallel Cascade Identification.

2.4. Constructing Class Predictors.

2.5. Prediction Based on Gene Expression Profiling.

2.6. Comparing Different Predictors Over the Same Data Set.

2.7. Concluding Remarks.

References.

3. Gene Regulation Bioinformatics of Microarray Data (Gert Thijs, Frank De Smet, Yves Moreau, Kathleen Marchal, and Bart De Moor).

3.1. Introduction.

3.2. Introduction to Transcriptional Regulation.

3.3. Measuring Gene Expression Profiles.

3.4. Preprocessing of Data.

3.5. Clustering of Gene Expression Profiles.

3.6. Cluster Validation.

3.7. Searching for Common Binding Sites of Coregulated Genes.

3.8. Inclusive: Online Integrated Analysis of Microarray Data.

3.9. Further Integrative Steps.

3.10. Conclusion.

References.

4. Robust Methods for Microarray Analysis (George S. Davidson, Shawn Martin, Kevin W. Boyack, Brian N. Wylie, Juanita Martinez, Anthony Aragon, Margaret Werner-Washburne, Mo´nica Mosquera-Caro, and Cheryl Willman).

4.1. Introduction.

4.2. Microarray Experiments and Analysis Methods.

4.3. Unsupervised Methods.

4.4. Supervised Methods.

4.5. Conclusion.

References.

5. In Silico Radiation Oncology: A Platform for Understanding Cancer Behavior and Optimizing Radiation Therapy Treatment (G. Stamatakos, D. Dionysiou, and N. Uzunoglu).

5.1. Philosophiae Tumoralis Principia Algorithmica: Algorithmic Principles of Simulating Cancer on Computer.

5.2. Brief Literature Review.

5.3. Paradigm of Four-Dimensional Simulation of Tumor Growth and Response to Radiation Therapy In Vivo.

5.4. Discussion.

5.5. Future Trends.

References.

6. Genomewide Motif Identification Using a Dictionary Model (Chiara Sabatti and Kenneth Lange).

6.1. Introduction.

6.2. Unified Model.

6.3. Algorithms for Likelihood Evaluation.

6.4. Parameter Estimation via Minorization–Maximization Algorithm.

6.5. Examples.

6.6. Discussion and Conclusion.

References.

7. Error Control Codes and the Genome (Elebeoba E. May).

7.1. Error Control and Communication: A Review.

7.3. Reverse Engineering the Genetic Error Control System.

7.4. Applications of Biological Coding Theory.

References.

8. Complex Life Science Multidatabase Queries (Zina Ben Miled, Nianhua Li, Yue He, Malika Mahoui, and Omran Bukhres).

8.1. Introduction.

8.2. Architecture.

8.3. Query Execution Plans.

8.4. Related Work.

8.5. Future Trends.

References.

9. Computational Analysis of Proteins (Dimitrios I. Fotiadis, Yorgos Goletsis, Christos Lampros, and Costas Papaloukas).

9.1. Introduction: Definitions.

9.2. Databases.

9.3. Sequence Motifs and Domains.

9.4. Sequence Alignment.

9.5. Modeling.

9.6. Classification and Prediction.

9.7. Natural Language Processing.

9.8. Future Trends.

References.

10. Computational Analysis of Interactions Between Tumor and Tumor Suppressor Proteins (E. Pirogova, M. Akay, and I. Cosic).

10.1. Introduction.

10.2. Methodology: Resonant Recognition Model.

10.3. Results and Discussions.

10.4. Conclusion.

References.

Index.

About the Editor.

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