000 | 08413nam a2201261 i 4500 | ||
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001 | 5237890 | ||
003 | IEEE | ||
005 | 20220712205617.0 | ||
006 | m o d | ||
007 | cr |n||||||||| | ||
008 | 070326t20152007njua ob 001 0 eng d | ||
020 |
_a9780470052198 _qeBook |
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020 |
_z0471631817 _qpaper |
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020 |
_z1601195044 _qebook |
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020 |
_z9781601195043 _qebook |
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020 |
_z0470052198 _qeBook |
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020 |
_z9780471631811 _qhardback |
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020 |
_z047005218X _qelectronic |
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020 |
_z9780470052181 _qelectronic |
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024 | 7 |
_a10.1002/0470052198 _2doi |
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035 | _a(CaBNVSL)mat05237890 | ||
035 | _a(IDAMS)0b00006481095d90 | ||
040 |
_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL |
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050 | 4 |
_aQP551 _b.G343 2007eb |
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050 | 4 |
_aQH441.2 _b.G47 2007eb |
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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. |
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300 |
_a1 PDF (xv, 297 pages) : _billustrations. |
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336 |
_atext _2rdacontent |
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337 |
_aelectronic _2isbdmedia |
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338 |
_aonline resource _2rdacarrier |
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490 | 1 |
_aIEEE press series on biomedical engineering ; _v25 |
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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 |
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650 | 0 |
_aGenomics. _915296 |
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650 | 0 |
_aBioinformatics. _99561 |
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650 | 2 |
_aGenomics. _915296 |
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650 | 2 |
_aProteomics. _926492 |
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650 | 2 |
_aGenetic Engineering. _918549 |
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650 | 2 |
_aProteins _xgenetics. _926493 |
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650 | 2 |
_aGenetic Techniques. _913348 |
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650 | 2 |
_aComputational Biology. _915342 |
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655 | 0 |
_aElectronic books. _93294 |
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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 |
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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 |