000			08669nam a2201165 i 4500
001			5361011
003			IEEE
005			20200421114117.0
006			m o d
007			cr |n|||||||||
008			071221t20152007nyua o 000 0 eng d
020			_a9780470199091 _qelectronic
020			_z9780470105269 _qprint
020			_z0470199091 _qelectronic
024	7		_a10.1002/9780470199091 _2doi
035			_a(CaBNVSL)mat05361011
035			_a(IDAMS)0b0000648117880f
040			_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL
050		4	_aQH324.2 _b.C636 2007eb
082	0	4	_a572.028563 _222
245	0	0	_aComputational intelligence in bioinformatics / _cedited by Gary B. Fogel, David W. Corne and Yi Pan.
264		1	_a[Hoboken, New Jersey] : _bWiley-IEEE, _c2007.
264		2	_a[Piscataqay, New Jersey] : _bIEEE Xplore, _c[2007]
300			_a1 PDF (xix, 355 pages) : _billustrations.
336			_atext _2rdacontent
337			_aelectronic _2isbdmedia
338			_aonline resource _2rdacarrier
490	1		_aIEEE press series on computational intelligence ; _v7
505	0		_aPreface -- Contributors -- Part One Gene Expression Analysis and Systems Biology -- 1. Hybrid of Neural Classifi er and Swarm Intelligence in Multiclass Cancer Diagnosis with Gene Expression Signatures (Rui Xu, Georgios C. Anagnostopoulos, and Donald C. Wunsch II) -- 1.1 Introduction -- 1.2 Methods and Systems -- 1.3 Experimental Results -- 1.4 Conclusions -- 2. Classifying Gene Expression Profi les with Evolutionary Computation (Jin-Hyuk Hong and Sung-Bae Cho) -- 2.1 DNA Microarray Data Classifi cation -- 2.2 Evolutionary Approach to the Problem -- 2.3 Gene Selection with Speciated Genetic Algorithm -- 2.4 Cancer Classifi ction Based on Ensemble Genetic Programming -- 2.5 Conclusion -- 3. Finding Clusters in Gene Expression Data Using EvoCluster (Patrick C. H. Ma, Keith C. C. Chan, and Xin Yao) -- 3.1 Introduction -- 3.2 Related Work -- 3.3 Evolutionary Clustering Algorithm -- 3.4 Experimental Results -- 3.5 Conclusions -- 4. Gene Networks and Evolutionary Computation (Jennifer Hallinan) -- 4.1 Introduction -- 4.2 Evolutionary Optimization -- 4.3 Computational Network Modeling -- 4.4 Extending Reach of Gene Networks -- 4.5 Network Topology Analysis -- 4.6 Summary -- Part Two Sequence Analysis and Feature Detection -- 5. Fuzzy-Granular Methods for Identifying Marker Genes from Microarray Expression Data (Yuanchen He, Yuchun Tang, Yan-Qing Zhang, and Rajshekhar Sunderraman) -- 5.1 Introduction -- 5.2 Traditional Algorithms for Gene Selection -- 5.3 New Fuzzy-Granular-Based Algorithm for Gene Selection -- 5.4 Simulation -- 5.5 Conclusions -- 6. Evolutionary Feature Selection for Bioinformatics (Laetitia Jourdan, Clarisse Dhaenens, and El-Ghazali Talbi) -- 6.1 Introduction -- 6.2 Evolutionary Algorithms for Feature Selection -- 6.3 Feature Selection for Clustering in Bioinformatics -- 6.4 Feature Selection for Classifi cation in Bioinformatics -- 6.5 Frameworks and Data Sets -- 6.6 Conclusion -- 7. Fuzzy Approaches for the Analysis CpG Island Methylation Patterns (Ozy Sjahputera, Mihail Popescu, James M. Keller, and Charles W. Caldwell).
505	8		_a7.1 Introduction -- 7.2 Methods -- 7.3 Biological Signifi cance -- 7.4 Conclusions -- Part Three Molecular Structure and Phylogenetics -- 8. Protein-Ligand Docking with Evolutionary Algorithms(Ren� Thomsen) -- 8.1 Introduction -- 8.2 Biochemical Background -- 8.3 The Docking Problem -- 8.4 Protein-Ligand Docking Algorithms -- 8.5 Evolutionary Algorithms -- 8.6 Effect of Variation Operators -- 8.7 Differential Evolution -- 8.8 Evaluating Docking Methods -- 8.9 Comparison between Docking Methods -- 8.10 Summary -- 8.11 Future Research Topics -- 9. RNA Secondary Structure Prediction Employing Evolutionary Algorithms (Kay C. Wiese, Alain A. Desch�nes, and Andrew G. Hendriks) -- 9.1 Introduction -- 9.2 Thermodynamic Models -- 9.3 Methods -- 9.4 Results -- 9.5 Conclusion -- 10. Machine Learning Approach for Prediction of Human Mitochondrial Proteins (Zhong Huang, Xuheng Xu, and Xiaohua Hu) -- 10.1 Introduction -- 10.2 Methods and Systems -- 10.3 Results and Discussion -- 10.4 Conclusions -- 11. Phylogenetic Inference Using Evolutionary Algorithms(Clare Bates Congdon) -- 11.1 Introduction -- 11.2 Background in Phylogenetics -- 11.3 Challenges and Opportunities for Evolutionary Computation -- 11.4 One Contribution of Evolutionary Computation: Graphyl -- 11.5 Some Other Contributions of Evolutionary computation -- 11.6 Open Questions and Opportunities -- Part Four Medicine -- 12. Evolutionary Algorithms for Cancer Chemotherapy Optimization (John McCall, Andrei Petrovski, and Siddhartha Shakya) -- 12.1 Introduction -- 12.2 Nature of Cancer -- 12.3 Nature of Chemotherapy -- 12.4 Models of Tumor Growth and Response -- 12.5 Constraints on Chemotherapy -- 12.6 Optimal Control Formulations of Cancer Chemotherapy -- 12.7 Evolutionary Algorithms for Cancer Chemotherapy Optimization -- 12.8 Encoding and Evaluation -- 12.9 Applications of EAs to Chemotherapy Optimization Problems -- 12.10 Related Work -- 12.11 Oncology Workbench -- 12.12 Conclusion -- 13. Fuzzy Ontology-Based Text Mining System for Knowledge Acquisition, Ontology Enhancement, and Query Answering from Biomedical Texts (Lipika Dey and Muhammad Abulaish).
505	8		_a13.1 Introduction -- 13.2 Brief Introduction to Ontologies -- 13.3 Information Retrieval form Biological Text Documents: Related Work -- 13.4 Ontology-Based IE and Knowledge Enhancement System -- 13.5 Document Processor -- 13.6 Biological Relation Extractor -- 13.7 Relation-Based Query Answering -- 13.8 Evaluation of the Biological Relation Extraction Process -- 13.9 Biological Relation Characterizer -- 13.10 Determining Strengths of Generic Biological Relations -- 13.11 Enhancing GENIA to Fuzzy Relational Ontology -- 13.12 Conclusions and Future Work -- References -- Appendix Feasible Biological Relations -- Index.
506	1		_aRestricted to subscribers or individual electronic text purchasers.
520			_aCombining biology, computer science, mathematics, and statistics, the field of bioinformatics has become a hot new discipline with profound impacts on all aspects of biology and industrial application. Now, Computational Intelligence in Bioinformatics offers an introduction to the topic, covering the most relevant and popular CI methods, while also encouraging the implementation of these methods to readers' research.
530			_aAlso available in print.
538			_aMode of access: World Wide Web
588			_aDescription based on PDF viewed 12/21/2015.
650		0	_aBioinformatics.
650		0	_aComputational intelligence.
655		0	_aElectronic books.
695			_aAccuracy
695			_aAlgorithm design and analysis
695			_aAmino acids
695			_aArrays
695			_aBioinformatics
695			_aBiological cells
695			_aBiological system modeling
695			_aBiomembranes
695			_aCancer
695			_aChemicals
695			_aCloning
695			_aClustering algorithms
695			_aComputational modeling
695			_aDNA
695			_aDrugs
695			_aDynamic programming
695			_aEllipsoids
695			_aEncoding
695			_aEvolution (biology)
695			_aEvolutionary computation
695			_aGene expression
695			_aGenomics
695			_aHeart
695			_aHumans
695			_aIndexes
695			_aLiver
695			_aLungs
695			_aMathematical model
695			_aMeasurement
695			_aNeurons
695			_aNoise measurement
695			_aOntologies
695			_aOptimization
695			_aParticle swarm optimization
695			_aPeptides
695			_aPhylogeny
695			_aPlasmas
695			_aPrediction algorithms
695			_aProbes
695			_aProtein engineering
695			_aProteins
695			_aRNA
695			_aSections
695			_aSpace exploration
695			_aStrain
695			_aSubspace constraints
695			_aSupport vector machines
695			_aThermodynamics
695			_aTraining
695			_aTumors
695			_aVegetation
700	1		_aCorne, David.
700	1		_aPan, Yi.
700	1		_aFogel, Gary, _d1968-
710	2		_aJohn Wiley & Sons, _epublisher.
710	2		_aIEEE Xplore (Online service), _edistributor.
776	0	8	_iPrint version: _z9780470105269
830		0	_aIEEE press series on computational intelligence ; _v7
856	4	2	_3Abstract with links to resource _uhttp://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=5361011
942			_cEBK
999			_c59586 _d59586