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[null,"1.","Record Nr.",null,"UNINA9910455030403321"]
[null,null,"Titolo",null,"Applications of fuzzy logic in bioinformatics [[electronic resource] /] / Dong Xu ... [et al.]"]
[null,null,"Pubbl/distr/stampa",null,"London, : Imperial College Press"]
[{"$nulls":4},"Hackensack, N.J., : Distributed by World Scientific, c2008"]
[null,null,"ISBN",null,"1-84816-259-6"]
[null,null,"Descrizione fisica",null,"1 online resource (248 p.)"]
[null,null,"Collana",null,"Series on advances in bioinformatics and computational biology ; ; v. 9"]
[null,null,"Disciplina",null,"572.80285 22"]
[{"$nulls":4},"570.285"]
[null,null,"Soggetti",null,"Bioinformatics"]
[{"$nulls":4},"Computational biology"]
[{"$nulls":4},"Fuzzy logic"]
[{"$nulls":4},"Fuzzy sets"]
[{"$nulls":4},"Electronic books."]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references (p. 196-221) and index."]
[null,null,"Nota di contenuto",null,"Foreword; 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."]
[{"$nulls":4},"2.5 Fuzzy Relations and Fuzzy Logic Inference.2.6 Fuzzy Clustering; 2.6.1 Fuzzy C-Means; 2.6.2 Extension to fuzzy C-Means.; 2.6.3 Possibilistic C-Means (PCM); 2.7 Fuzzy K-Nearest Neighbors; 2.8 Fuzzy Measures and Fuzzy Integrals.; 2.8.1 Fuzzy measures.; 2.8.2 Fuzzy integrals; 2.9 Summary and Final Thoughts; 3. Fuzzy Similarities in Ontologies.; 3.1 Introduction; 3.2 Definition of Ontology-Based Similarity; 3.3 Set-Based Similarity Measure.; 3.3.1 Pair-wise aggregation.; 3.3.2 Bag of words similarities.; 3.4 Fuzzy Measure Similarity"]
[{"$nulls":4},"3.5 Fuzzy Measure Similarity for Augmented Sets of Ontology Objects.3.6 Choquet Fuzzy Integral Similarity Measure.; 3.7 Examples and "]
[{"$nulls":4},"Applications of Fuzzy Measure Similarity Using GO Terms; 3.7.1 Lymphoma case study; 3.7.2 Gene clustering using Gene Ontology annotations.; 3.7.3 Gene summarization using Gene Ontology terms.; 3.8 Ontology Similarity in Data Mining; 3.9 Discussion and Summary.; 4. Fuzzy Logic in Structural Bioinformatics; 4.1 Introduction; 4.2 Protein Secondary Structure Prediction.; 4.3 Protein Solvent Accessibility Prediction."]
[{"$nulls":4},"4.4 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"]
[{"$nulls":4},"5.2.4 Fuzzy co-clustering algorithms 5.3 Inferring Gene Networks Using Fuzzy Rule Systems; 5.4 Discussion and Summary.; 6. Other Applications.; 6.1 Overview; 6.2 Applications in Biological Sequence Analyses; 6.2.1 Protein sequence comparison; 6.2.2 Application in sequence family classification; 6.2.3 Application in motif identification.; 6.2.4 Application in protein subcellular localization prediction.; 6.2.5 Genomic structure prediction; 6.3 Application in Computational Proteomics; 6.3.1 Electrophoresis analysis.; 6.3.2 Protein identification through mass-spec; 6.4 Application in Drug Design."]
[{"$nulls":4},"6.5 Discussion and Summary."]
[null,null,null,"Sommario/riassunto","Many 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"]