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.

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.