LEADER 01032nam a2200289 i 4500 001 991001067189707536 005 20020507182613.0 008 961029s1971 de ||| | eng 020 $a3540056483 035 $ab10796344-39ule_inst 035 $aLE01306526$9ExL 040 $aDip.to Matematica$beng 082 0 $a515.84 084 $aAMS 30C65 100 1 $aVaisala, Jussi$059269 245 10$aLectures on n-dimensional quasiconformal mappings /$cJussi Vaisala 260 $aBerlin :$bSpringer-Verlag,$c1971 300 $axiv, 144 p. ;$c26 cm 490 0 $aLecture notes in mathematics,$x0075-8434 ;$v229 500 $aBibliography: p. 140-142 650 0$aConformal mapping 907 $a.b10796344$b23-02-17$c28-06-02 912 $a991001067189707536 945 $aLE013 30C VAI11 (1971)$g1$i2013000064109$lle013$o-$pE0.00$q-$rl$s- $t0$u0$v0$w0$x0$y.i10897847$z28-06-02 996 $aLectures on n-dimensional quasiconformal mappings$981559 997 $aUNISALENTO 998 $ale013$b01-01-96$cm$da $e-$feng$gde $h0$i1 LEADER 06992nam 2200589 450 001 9910829979903321 005 20230125205108.0 010 $a0-470-65215-2 010 $a1-281-22169-4 010 $a9786611221690 010 $a0-470-19909-1 010 $a0-470-19908-3 024 7 $a10.1002/9780470199091 035 $a(OCoLC)184983178 035 $a(MiAaPQ)EBC331455 035 $a(EXLCZ)991000000000376989 100 $a20220506h20152007 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aComputational intelligence in bioinformatics$fedited by Gary B. Fogel, David W. Corne and Yi Pan 210 1$a[Hoboken, New Jersey] :$cWiley-IEEE$d2007 210 2$a[Piscataqay, New Jersey] :$cIEEE Xplore,$d[2007] 215 $a1 online resource (377 p.) 225 1 $aIEEE press series on computational intelligence ;$v7 300 $aDescription based upon print version of record. 311 $a0-470-10526-7 320 $aIncludes bibliographical references and index. 327 $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). 327 $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(Rene 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. Deschanes, 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). 327 $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. 330 $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. 606 $aBioinformática$2UAMSUB 606 $aInteligencia computacional$2UAMSUB 608 $aLibros electrónicos 610 $aInformática general 615 7$aBioinformática 615 7$aInteligencia computacional 676 $a572.028563 676 $a572.80285 701 $aCorne$b David$01659680 701 $aPan$b Yi$0296154 701 $aFogel$b Gary$f1968-$01659681 801 0$bCaBNVSL 801 1$bCaBNVSL 801 2$bCaBNVSL 906 $aBOOK 912 $a9910829979903321 996 $aComputational intelligence in bioinformatics$94014442 997 $aUNINA