LEADER 06564nam 2200697 450 001 996212249903316 005 20230125205708.0 010 $a1-280-73984-3 010 $a9786610739844 010 $a0-470-05219-8 010 $a1-60119-504-4 010 $a0-470-05218-X 024 7 $a10.1002/0470052198 035 $a(CKB)1000000000355567 035 $a(EBL)288353 035 $a(SSID)ssj0000072058 035 $a(PQKBManifestationID)11118899 035 $a(PQKBTitleCode)TC0000072058 035 $a(PQKBWorkID)10095029 035 $a(PQKB)11265163 035 $a(MiAaPQ)EBC288353 035 $a(CaBNVSL)mat05237890 035 $a(IDAMS)0b00006481095d90 035 $a(IEEE)5237890 035 $a(OCoLC)86228637 035 $a(PPN)267789556 035 $a(EXLCZ)991000000000355567 100 $a20070326h20152007 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aGenomics and proteomics engineering in medicine and biology$b[electronic resource] /$fedited by Metin Akay 210 1$aPiscataway, New Jersey :$cIEEE Press,$dc2007. 215 $a1 online resource (317 p.) 225 1 $aIEEE press series on biomedical engineering ;$v25 300 $a"IEEE Engineering in Medicine and Biology Society, sponsor." 311 $a0-471-63181-7 320 $aIncludes bibliographical references and index. 327 $aPreface. -- 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. 327 $a5.4. 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. 330 $aCurrent applications and recent advances in genomics and proteomics Genomics and Proteomics Engineering in Medicine and Biology presents a well-rounded, interdisciplinary discussion of a topic that is at the cutting edge of both molecular biology and bioengineering. Compiling contributions by established experts, this book highlights up-to-date applications of biomedical informatics, as well as advancements in genomics-proteomics areas. Structures and algorithms are used to analyze genomic data and develop computational solutions for pathological understanding. Topics discussed include: . Qualitative knowledge models. Interpreting micro-array data. Gene regulation bioinformatics. Methods to analyze micro-array. Cancer behavior and radiation therapy. Error-control codes and the genome. Complex life science multi-database queries. Computational protein analysis. Tumor and tumor suppressor proteins interactions. 410 0$aIEEE Press Series on Biomedical Engineering ;$v25 606 $aProteomics 606 $aGenomics 606 $aBioinformatics 615 0$aProteomics. 615 0$aGenomics. 615 0$aBioinformatics. 676 $a572.86 676 $a572/.6 676 $a660.65 701 $aAkay$b Metin$0772085 801 0$bCaBNVSL 801 1$bCaBNVSL 801 2$bCaBNVSL 906 $aBOOK 912 $a996212249903316 996 $aGenomics and proteomics engineering in medicine and biology$91886527 997 $aUNISA