Bioinformatics Technologies (eBook)

Professor Yi-Ping Phoebe Chen is Professor and Chair & Head of Department at the Department of Computer Science and Computer Engineering, La Trobe University, Melbourne Australia. Prof Chen is the Chief Investigator of ARC Centre of Excellence in Bioinformatics from 2003-2010. Phoebe received her BInfTech degree with First Class Honours and PhD in Computer Science (Bioinformatics) from the University of Queensland. Before she joined La Trobe, Phoebe was Associate Professor (Reader) in Deakin University from Dec 2003 to April 2010. She worked as a Associate Lecturer/Lecturer/Senior Lecturer in Queensland University of Technology from Jul 1999 to Nov 2003. She is currently working on knowledge discovery technologies and is especially interested in their application to genomics and biomedical science. Her research focus is to find best solutions for mining, integrating and analyzing complex data structure and functions for scientific and biomedical applications. She has been working in the area of bioinformatics, health informatics, multimedia databases, query system and systems biology, co-authored over 150 research papers with many published in top journals and conferences such as IEEE Transactions on Biomedical Engineering, IEEE Transactions on Information Technology in Biomedicine, Nucleic Acids Research, BMC Genomics, BMC Bioinformatics, Current Drug Metabolism, Data Mining and Knowledge Discovery, IEEE Transactions on Knowledge and Data Engineering, Information Systems and ACM Transactions. She is steering committee chair of Asia-Pacific Bioinformatics Conference (founder) and International conference on Multimedia Modelling. She has been on the program committees of over 100 international conferences, including top ranking conferences such as ICDE, ICPR, ISMB, CIKM etc. Phoebe is a senior member of IEEE, member of ACM.

Preface 5
Contents 10
1 Introduction to Bioinformatics 15
1.1 Introduction 15
1.2 Needs of Bioinformatics Technologies 16
1.3 An Overview of Bioinformatics Technologies 19
1.4 A Brief Discussion on the Chapters 22
References 26
2 Overview of Structural Bioinformatics 29
2.1 Introduction 29
2.2 Organization of Structural Bioinformatics 31
2.3 Primary Resource: Protein Data Bank 32
2.4 Secondary Resources and Applications 36
2.5 Using Structural Bioinformatics Approaches in Drug Design 51
2.6 The Future 53
References 54
3 Database Warehousing in Bioinformatics 59
3.1 Introduction 59
3.2 Bioinformatics Data 62
3.3 Transforming Data to Knowledge 65
3.4 Data Warehousing 68
3.5 Data Warehouse Architecture 70
3.6 Data Quality 72
3.7 Concluding Remarks 74
References 75
4 Data Mining for Bioinformatics 77
4.1 Introduction 77
4.2 Biomedical Data Analysis 78
4.3 DNA Data Analysis 85
4.4 Protein Data Analysis 106
References 123
5 Machine Learning in Bioinformatics 131
5.1 Introduction 131
5.2 Artificial Neural Network 134
5.3 Neural Network Architectures and Applications 142
5.4 Genetic Algorithm 149
5.5 Fuzzy System 155
References 161
6 Systems Biotechnology: a New Paradigm in Biotechnology Development 169
6.1 Introduction 169
6.2 Why Systems Biotechnology? 170
6.3 Tools for Systems Biotechnology 172
6.4 Integrative Approaches 178
6.5 In Silico Modeling and Simulation of Cellular Processes 180
6.6 Conclusion 184
References 185
7 Computational Modeling of Biological Processes with Petri Net- Based Architecture 193
7.1 Introduction 193
7.2 Hybrid Petri Net and Hybrid Dynamic Net 197
7.3 Hybrid Functional Petri Net 204
7.4 Hybrid Functional Petri Net with Extension 205
7.5 Modeling of Biological Processes with HFPNe 212
7.6 Related Works with HFPNe 225
7.7 Genomic Object Net: GON 226
7.8 Visualizer 238
7.9 BPE 247
7.10 Conclusion 250
References 250
8 Biological Sequence Assembly and Alignment 257
8.1 Introduction 257
8.2 Large-Scale Sequence Assembly 259
8.3 Large-Scale Pairwise Sequence Alignment 268
8.4 Large-Scale Multiple Sequence Alignment 271
8.5 Load Balancing and Communication Overhead 273
8.6 Conclusion 273
References 274
9 Modeling for Bioinformatics 277
9.1 Introduction 277
9.2 Hidden Markov Modeling for Biological Data Analysis 278
9.3 Comparative Modeling 295
9.4 Probabilistic Modeling 301
9.5 Molecular Modeling 304
References 311
10 Pattern Matching for Motifs 313
10.1 Introduction 313
10.2 Gene Regulation 315
10.3 Motif Recognition 317
10.4 Motif Detection Strategies 319
10.5 Single Gene, Multi-species Approach 321
10.6 Multi-genes, Multi-species Approach 323
10.7 Summary 323
References 324
11 Visualization and Fractal Analysis of Biological Sequences 327
11.1 Introduction 327
11.2 Fractal Analysis 331
11.3 DNA Walk Models 337
11.4 Chaos Game Representation of Biological Sequences 339
11.5 Two-Dimensional Portrait Representation of DNA Sequences 344
11.6 One-Dimensional Measure Representation of Biological Sequences 349
References 362
12 Microarray Data Analysis 367
12.1 Introduction 367
12.2 Microarray Technology for Genome Expression Study 368
12.3 Image Analysis for Data Extraction 370
12.4 Data Analysis for Pattern Discovery 377
References 398
Index 403