000 08413nam a2201261 i 4500
001 5237890
003 IEEE
005 20220712205617.0
006 m o d
007 cr |n|||||||||
008 070326t20152007njua ob 001 0 eng d
020 _a9780470052198
_qeBook
020 _z0471631817
_qpaper
020 _z1601195044
_qebook
020 _z9781601195043
_qebook
020 _z0470052198
_qeBook
020 _z9780471631811
_qhardback
020 _z047005218X
_qelectronic
020 _z9780470052181
_qelectronic
024 7 _a10.1002/0470052198
_2doi
035 _a(CaBNVSL)mat05237890
035 _a(IDAMS)0b00006481095d90
040 _aCaBNVSL
_beng
_erda
_cCaBNVSL
_dCaBNVSL
050 4 _aQP551
_b.G343 2007eb
050 4 _aQH441.2
_b.G47 2007eb
082 0 4 _a572.86
_222
245 0 0 _aGenomics and proteomics engineering in medicine and biology
_h[electronic resource] /
_cedited by Metin Akay.
264 1 _aPiscataway, New Jersey :
_bIEEE Press,
_cc2007.
300 _a1 PDF (xv, 297 pages) :
_billustrations.
336 _atext
_2rdacontent
337 _aelectronic
_2isbdmedia
338 _aonline resource
_2rdacarrier
490 1 _aIEEE press series on biomedical engineering ;
_v25
500 _a"IEEE Engineering in Medicine and Biology Society, Sponsor."
504 _aIncludes bibliographical references and index.
505 0 _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.
505 8 _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.
506 1 _aRestricted to subscribers or individual electronic text purchasers.
520 _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.
538 _aMode of access: World Wide Web.
588 _aDescription based on PDF viewed 12/18/2015.
650 0 _aProteomics.
_926492
650 0 _aGenomics.
_915296
650 0 _aBioinformatics.
_99561
650 2 _aGenomics.
_915296
650 2 _aProteomics.
_926492
650 2 _aGenetic Engineering.
_918549
650 2 _aProteins
_xgenetics.
_926493
650 2 _aGenetic Techniques.
_913348
650 2 _aComputational Biology.
_915342
655 0 _aElectronic books.
_93294
695 _aAmino acids
695 _aApproximation methods
695 _aArrays
695 _aBiographies
695 _aBioinformatics
695 _aBiological system modeling
695 _aBiology
695 _aCancer
695 _aChannel coding
695 _aChemicals
695 _aComplexity theory
695 _aComputational modeling
695 _aCorrelation
695 _aDNA
695 _aData visualization
695 _aDatabases
695 _aDecision support systems
695 _aDictionaries
695 _aDiseases
695 _aDistributed databases
695 _aError correction
695 _aEstimation
695 _aGene expression
695 _aGenerators
695 _aGenomics
695 _aHidden Markov models
695 _aHumans
695 _aIndexes
695 _aKernel
695 _aKnowledge based systems
695 _aLaboratories
695 _aMathematical model
695 _aMaximum likelihood decoding
695 _aNoise
695 _aNoise measurement
695 _aNonlinear systems
695 _aOntologies
695 _aPetri nets
695 _aPolymers
695 _aProtein engineering
695 _aProtein sequence
695 _aProteins
695 _aRNA
695 _aRobustness
695 _aShape
695 _aSolid modeling
695 _aTransforms
695 _aTumors
695 _aWarehousing
700 1 _aAkay, Metin.
_926043
710 2 _aIEEE Engineering in Medicine and Biology Society.
_926044
710 2 _aIEEE Xplore (Online service),
_edistributor.
_926494
776 0 8 _iPrint version:
_z9780471631811
830 0 _aIEEE Press Series on Biomedical Engineering ;
_v25
_926495
856 4 2 _3Abstract with links to resource
_uhttps://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=5237890
942 _cEBK
999 _c73791
_d73791