Genomics and Proteomics Engineering in Medicine and Biology

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

Current applications and recent advances in genomics andproteomicsGenomics and Proteomics Engineering in Medicine andBiology presents a well-rounded, interdisciplinary discussionof a topic that is at the cutting edge of both molecular biologyand bioengineering. Compiling contributions by established expertsthis book highlights up-to-date applications of biomedicalinformatics, as well as advancements in genomics-proteomics areas.Structures and algorithms are used to analyze genomic data anddevelop 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

Preface.Contributors.1. Qualitative Knowledge Models in Functional Genomics andProteomics (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 ApplicationsUsing 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 BartDe 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 CoregulatedGenes.3.8. Inclusive: Online Integrated Analysis of MicroarrayData.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, JuanitaMartinez, Anthony Aragon, Margaret Werner-Washburne, Mo´nicaMosquera-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 UnderstandingCancer Behavior and Optimizing Radiation Therapy Treatment (G.Stamatakos, D. Dionysiou, and N. Uzunoglu).5.1. Philosophiae Tumoralis Principia Algorithmica: AlgorithmicPrinciples of Simulating Cancer on Computer.5.2. Brief Literature Review.5.3. Paradigm of Four-Dimensional Simulation of Tumor Growth andResponse to Radiation Therapy In Vivo.5.4. Discussion.5.5. Future Trends.References.6. Genomewide Motif Identification Using a DictionaryModel (Chiara Sabatti and Kenneth Lange).6.1. Introduction.6.2. Unified Model.6.3. Algorithms for Likelihood Evaluation.6.4. Parameter Estimation via Minorization-MaximizationAlgorithm.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 BenMiled, 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 CostasPapaloukas).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 andTumor 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.