Genomics and proteomics engineering in medicine and biology
Author(s)
Bibliographic Information
Genomics and proteomics engineering in medicine and biology
(IEEE Press series in biomedical engineering)
IEEE Press , Wiley-Interscience, c2007
Available at 6 libraries
  Aomori
  Iwate
  Miyagi
  Akita
  Yamagata
  Fukushima
  Ibaraki
  Tochigi
  Gunma
  Saitama
  Chiba
  Tokyo
  Kanagawa
  Niigata
  Toyama
  Ishikawa
  Fukui
  Yamanashi
  Nagano
  Gifu
  Shizuoka
  Aichi
  Mie
  Shiga
  Kyoto
  Osaka
  Hyogo
  Nara
  Wakayama
  Tottori
  Shimane
  Okayama
  Hiroshima
  Yamaguchi
  Tokushima
  Kagawa
  Ehime
  Kochi
  Fukuoka
  Saga
  Nagasaki
  Kumamoto
  Oita
  Miyazaki
  Kagoshima
  Okinawa
  Korea
  China
  Thailand
  United Kingdom
  Germany
  Switzerland
  France
  Belgium
  Netherlands
  Sweden
  Norway
  United States of America
Note
sponsor: IEEE Engineering in Medicine and Biology Society
Includes bibliographical references and index
Description and Table of Contents
Description
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
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, Monica 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.
by "Nielsen BookData"