LEADER 05571nam 2200709Ia 450 001 9910455030403321 005 20200520144314.0 010 $a1-84816-259-6 035 $a(CKB)1000000000767311 035 $a(EBL)1193220 035 $a(SSID)ssj0000517149 035 $a(PQKBManifestationID)12159102 035 $a(PQKBTitleCode)TC0000517149 035 $a(PQKBWorkID)10486612 035 $a(PQKB)10852054 035 $a(MiAaPQ)EBC1193220 035 $a(WSP)00002034 035 $a(Au-PeEL)EBL1193220 035 $a(CaPaEBR)ebr10688065 035 $a(CaONFJC)MIL491658 035 $a(OCoLC)826660269 035 $a(EXLCZ)991000000000767311 100 $a20090407d2008 uy 0 101 0 $aeng 135 $aurcuu|||uu||| 181 $ctxt 182 $cc 183 $acr 200 00$aApplications of fuzzy logic in bioinformatics$b[electronic resource] /$fDong Xu ... [et al.] 210 $aLondon $cImperial College Press ;$aHackensack, N.J. $cDistributed by World Scientific$dc2008 215 $a1 online resource (248 p.) 225 1 $aSeries on advances in bioinformatics and computational biology ;$vv. 9 300 $aDescription based upon print version of record. 311 $a1-84816-258-8 320 $aIncludes bibliographical references (p. 196-221) and index. 327 $aForeword; Preface; Contents; 1. Introduction to Bioinformatics; 1.1 What Is Bioinformatics; 1.2 A Brief History of Bioinformatics; 1.3 Scope of Bioinformatics.; 1.4 Major Challenges in Bioinformatics; 1.5 Bioinformatics and Computer Science; 2. Introduction to Fuzzy Set Theory and Fuzzy Logic; 2.1 Where Does Fuzzy Logic Fit in Computational Science?; 2.2 Why Do We Need to Use Fuzziness in Biology?; 2.3 Brief History of the Field.; 2.4 Fuzzy Membership Functions and Operators.; 2.4.1 Membership functions; 2.4.2 Basic fuzzy set operators.; 2.4.3 Compensatory operators. 327 $a2.5 Fuzzy Relations and Fuzzy Logic Inference.2.6 Fuzzy Clustering; 2.6.1 Fuzzy C-Means; 2.6.2 Extension to fuzzy C-Means.; 2.6.3 Possibilistic C-Means (PCM); 2.7 Fuzzy K-Nearest Neighbors; 2.8 Fuzzy Measures and Fuzzy Integrals.; 2.8.1 Fuzzy measures.; 2.8.2 Fuzzy integrals; 2.9 Summary and Final Thoughts; 3. Fuzzy Similarities in Ontologies.; 3.1 Introduction; 3.2 Definition of Ontology-Based Similarity; 3.3 Set-Based Similarity Measure.; 3.3.1 Pair-wise aggregation.; 3.3.2 Bag of words similarities.; 3.4 Fuzzy Measure Similarity 327 $a3.5 Fuzzy Measure Similarity for Augmented Sets of Ontology Objects.3.6 Choquet Fuzzy Integral Similarity Measure.; 3.7 Examples and Applications of Fuzzy Measure Similarity Using GO Terms; 3.7.1 Lymphoma case study; 3.7.2 Gene clustering using Gene Ontology annotations.; 3.7.3 Gene summarization using Gene Ontology terms.; 3.8 Ontology Similarity in Data Mining; 3.9 Discussion and Summary.; 4. Fuzzy Logic in Structural Bioinformatics; 4.1 Introduction; 4.2 Protein Secondary Structure Prediction.; 4.3 Protein Solvent Accessibility Prediction. 327 $a4.4 Protein Structure Matching Using Fuzzy Alignments 4.5 Protein Similarity Calculation Using Fuzzy Contact Maps; 4.6 Protein Structure Class Classification; 4.7 Summary.; 5. Application of Fuzzy Logic in Microarray Data Analyses.; 5.1 Introduction; 5.1.1 Microarray data description; 5.1.2 Microarray processing algorithms for gene selection and patient classification.; 5.1.3 Microarray processing algorithms for gene regulatory network discovery; 5.2 Clustering Algorithms; 5.2.1 (Dis)similarity measures for microarray data; 5.2.2 Fuzzy C-means (FCM); 5.2.3 Relational fuzzy C-means 327 $a5.2.4 Fuzzy co-clustering algorithms 5.3 Inferring Gene Networks Using Fuzzy Rule Systems; 5.4 Discussion and Summary.; 6. Other Applications.; 6.1 Overview; 6.2 Applications in Biological Sequence Analyses; 6.2.1 Protein sequence comparison; 6.2.2 Application in sequence family classification; 6.2.3 Application in motif identification.; 6.2.4 Application in protein subcellular localization prediction.; 6.2.5 Genomic structure prediction; 6.3 Application in Computational Proteomics; 6.3.1 Electrophoresis analysis.; 6.3.2 Protein identification through mass-spec; 6.4 Application in Drug Design. 327 $a6.5 Discussion and Summary. 330 $aMany biological systems and objects are intrinsically fuzzy as their properties and behaviors contain randomness or uncertainty. In addition, it has been shown that exact or optimal methods have significant limitation in many bioinformatics problems. Fuzzy set theory and fuzzy logic are ideal to describe some biological systems/objects and provide good tools for some bioinformatics problems. This book comprehensively addresses several important bioinformatics topics using fuzzy concepts and approaches, including measurement of ontological similarity, protein structure prediction/analysis, and 410 0$aSeries on advances in bioinformatics and computational biology ;$vv. 9. 606 $aBioinformatics 606 $aComputational biology 606 $aFuzzy logic 606 $aFuzzy sets 608 $aElectronic books. 615 0$aBioinformatics. 615 0$aComputational biology. 615 0$aFuzzy logic. 615 0$aFuzzy sets. 676 $a572.80285 22 676 $a570.285 701 $aXu$b Dong$0942389 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910455030403321 996 $aApplications of fuzzy logic in bioinformatics$92126584 997 $aUNINA