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 microarray data analysis. It also reviews other bioinformatics applications using fuzzy techniques.
Contents: Introduction to Bioinformatics; Introduction to Fuzzy Set Theory and Fuzzy Logic; Fuzzy Similarities in Ontologies; Fuzzy Logic in Structural Bioinformatics; Application of Fuzzy Logic in Microarray Data Analyses; Other Applications; Summary and Outlook.
Author(s): Dong Xu, James M. Keller, Mihail Popescu, Rajkumar Bondugula
Series: Series on advances in bioinformatics and computational biology 9
Publisher: Imperial College Press; Distributed by World Scientific
Year: 2008
Language: English
Pages: 246
City: London :, Hackensack, N.J
Tags: Биологические дисциплины;Матметоды и моделирование в биологии;Биоинформатика;
Contents......Page 18
Foreword......Page 8
Preface......Page 12
1.1 What Is Bioinformatics......Page 22
1.2 A Brief History of Bioinformatics......Page 23
1.3 Scope of Bioinformatics.......Page 29
1.4 Major Challenges in Bioinformatics......Page 34
1.5 Bioinformatics and Computer Science......Page 35
2.1 Where Does Fuzzy Logic Fit in Computational Science?......Page 37
2.2 Why Do We Need to Use Fuzziness in Biology?......Page 38
2.3 Brief History of the Field.......Page 42
2.4.1 Membership functions......Page 44
2.4.2 Basic fuzzy set operators.......Page 48
2.4.3 Compensatory operators.......Page 54
2.5 Fuzzy Relations and Fuzzy Logic Inference.......Page 58
2.6 Fuzzy Clustering......Page 69
2.6.1 Fuzzy C-Means......Page 70
2.6.2 Extension to fuzzy C-Means.......Page 75
2.6.3 Possibilistic C-Means (PCM)......Page 80
2.7 Fuzzy K-Nearest Neighbors......Page 84
2.8 Fuzzy Measures and Fuzzy Integrals.......Page 87
2.8.1 Fuzzy measures.......Page 88
2.8.2 Fuzzy integrals......Page 90
2.9 Summary and Final Thoughts......Page 93
3.1 Introduction......Page 94
3.2 Definition of Ontology-Based Similarity......Page 97
3.3.1 Pair-wise aggregation.......Page 101
3.3.2 Bag of words similarities.......Page 105
3.4 Fuzzy Measure Similarity......Page 106
3.5 Fuzzy Measure Similarity for Augmented Sets of Ontology Objects.......Page 107
3.6 Choquet Fuzzy Integral Similarity Measure.......Page 108
3.7.1 Lymphoma case study......Page 111
3.7.2 Gene clustering using Gene Ontology annotations.......Page 113
3.7.3 Gene summarization using Gene Ontology terms.......Page 119
3.8 Ontology Similarity in Data Mining......Page 120
3.9 Discussion and Summary.......Page 123
4.1 Introduction......Page 124
4.2 Protein Secondary Structure Prediction.......Page 127
4.3 Protein Solvent Accessibility Prediction.......Page 137
4.4 Protein Structure Matching Using Fuzzy Alignments......Page 139
4.5 Protein Similarity Calculation Using Fuzzy Contact Maps......Page 145
4.6 Protein Structure Class Classification......Page 147
4.7 Summary.......Page 151
5.1 Introduction......Page 152
5.1.1 Microarray data description......Page 155
5.1.2 Microarray processing algorithms for gene selection and patient classification.......Page 157
5.1.3 Microarray processing algorithms for gene regulatory network discovery......Page 158
5.2 Clustering Algorithms......Page 159
5.2.1 (Dis)similarity measures for microarray data......Page 161
5.2.2 Fuzzy C-means (FCM)......Page 165
5.2.3 Relational fuzzy C-means......Page 169
5.2.4 Fuzzy co-clustering algorithms......Page 173
5.3 Inferring Gene Networks Using Fuzzy Rule Systems......Page 176
5.4 Discussion and Summary.......Page 180
6.1 Overview......Page 181
6.2.1 Protein sequence comparison......Page 182
6.2.2 Application in sequence family classification......Page 185
6.2.3 Application in motif identification.......Page 186
6.2.4 Application in protein subcellular localization prediction.......Page 187
6.2.5 Genomic structure prediction......Page 188
6.3 Application in Computational Proteomics......Page 189
6.3.1 Electrophoresis analysis.......Page 190
6.3.2 Protein identification through mass-spec......Page 191
6.4 Application in Drug Design.......Page 192
6.5 Discussion and Summary.......Page 195
7. Summary and Outlook......Page 197
AI.1.1 DNA (deoxyribonucleic acid)......Page 200
AI.1.3 Genome......Page 202
AI.2 Protein and Its Structure......Page 203
AI.3 Central Dogma of Biology......Page 207
AII.1 Online Resources for Molecular Biology......Page 210
AII.2 Online Resources for Bioinformatics......Page 211
AII.2.1.2 Protein structure visualization.......Page 212
AII.2.2.1 Microarray databases......Page 213
AII.2.2.2 Microarray analysis tool.......Page 214
AII.2.4 Online portals for bioinformatics.......Page 215
AII.3 Online Resources for Fuzzy Set Theory and Fuzzy Logic.......Page 216
Bibliography......Page 217
Index......Page 243
