Genomics and proteomics engineering in medicine and biology
著者
書誌事項
Genomics and proteomics engineering in medicine and biology
(IEEE Press series in biomedical engineering)
IEEE Press , Wiley-Interscience, c2007
大学図書館所蔵 全6件
  青森
  岩手
  宮城
  秋田
  山形
  福島
  茨城
  栃木
  群馬
  埼玉
  千葉
  東京
  神奈川
  新潟
  富山
  石川
  福井
  山梨
  長野
  岐阜
  静岡
  愛知
  三重
  滋賀
  京都
  大阪
  兵庫
  奈良
  和歌山
  鳥取
  島根
  岡山
  広島
  山口
  徳島
  香川
  愛媛
  高知
  福岡
  佐賀
  長崎
  熊本
  大分
  宮崎
  鹿児島
  沖縄
  韓国
  中国
  タイ
  イギリス
  ドイツ
  スイス
  フランス
  ベルギー
  オランダ
  スウェーデン
  ノルウェー
  アメリカ
注記
sponsor: IEEE Engineering in Medicine and Biology Society
Includes bibliographical references and index
内容説明・目次
内容説明
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
目次
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.
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