E-raamat: Bioinformatics Technologies

Preface		V
1 Introduction to Bioinformatics		1	(14)
1.1 Introduction		1	(1)
1.2 Needs of Bioinformatics Technologies		2	(3)
1.3 An Overview of Bioinformatics Technologies		5	(3)
1.4 A Brief Discussion on the Chapters		8	(4)
References		12	(3)
2 Overview of Structural Bioinformatics		15	(30)
2.1 Introduction		15	(2)
2.2 Organization of Structural Bioinformatics		17	(1)
2.3 Primary Resource: Protein Data Bank		18	(4)
2.3.1 Data Format		18	(1)
2.3.2 Growth of Data		18	(2)
2.3.3 Data Processing and Quality Control		20	(1)
2.3.4 The Future of the PDB		21	(1)
2.3.5 Visualization		21	(1)
2.4 Secondary Resources and Applications		22	(15)
2.4.1 Structural Classification		22	(6)
2.4.2 Structure Prediction		28	(2)
2.4.3 Functional Assignments in Structural Genomics		30	(2)
2.4.4 Protein-Protein Interactions		32	(2)
2.4.5 Protein-Ligand Interactions		34	(3)
2.5 Using Structural Bioinformatics Approaches in Drug Design		37	(2)
2.6 The Future		39	(1)
2.6.1 Integration over Multiple Resources		39	(1)
2.6.2 The Impact of Structural Genomics		39	(1)
2.6.3 The Role of Structural Bioinformatics in Systems Biology		39	(1)
References		40	(5)
3 Database Warehousing in Bioinformatics		45	(18)
3.1 Introduction		45	(3)
3.2 Bioinformatics Data		48	(3)
3.3 Transforming Data to Knowledge		51	(3)
3.4 Data Warehousing		54	(2)
3.5 Data Warehouse Architecture		56	(2)
3.6 Data Quality		58	(2)
3.7 Concluding Remarks		60	(1)
References		61	(2)
4 Data Mining for Bioinformatics		63	(54)
4.1 Introduction		63	(1)
4.2 Biomedical Data Analysis		64	(7)
4.2.1 Major Nucleotide Sequence Database, Protein Sequence Database, and Gene Expression Database		65	(3)
4.2.2 Software Tools for Bioinformatics Research		68	(3)
4.3 DNA Data Analysis		71	(21)
4.3.1 DNA Sequence		71	(5)
4.3.2 DNA Data Analysis		76	(16)
4.4 Protein Data Analysis		92	(17)
4.4.1 Protein and Amino Acid Sequence		92	(7)
4.4.2 Protein Data Analysis		99	(10)
References		109	(8)
5 Machine Learning in Bioinformatics		117	(38)
5.1 Introduction		117	(3)
5.2 Artificial Neural Network		120	(8)
5.3 Neural Network Architectures and Applications		128	(7)
5.3.1 Neural Network Architecture		128	(3)
5.3.2 Neural Network Learning Algorithms		131	(3)
5.3.3 Neural Network Applications in Bioinformatics		134	(1)
5.4 Genetic Algorithm		135	(6)
5.5 Fuzzy System		141	(6)
References		147	(8)
6 Systems Biotechnology: a New Paradigm in Biotechnology Development		155	(24)
6.1 Introduction		155	(1)
6.2 Why Systems Biotechnology?		156	(2)
6.3 Tools for Systems Biotechnology		158	(6)
6.3.1 Genome Analyses		158	(1)
6.3.2 Transcriptome Analyses		159	(2)
6.3.3 Proteome Analyses		161	(2)
6.3.4 Metabolome/Fluxome Analyses		163	(1)
6.4 Integrative Approaches		164	(2)
6.5 In Silico Modeling and Simulation of Cellular Processes		166	(4)
6.5.1 Statistical Modeling		167	(2)
6.5.2 Dynamic Modeling		169	(1)
6.6 Conclusion		170	(1)
References		171	(8)
7 Computational Modeling of Biological Processes with Petri Net-Based Architecture		179	(64)
7.1 Introduction		179	(4)
7.2 Hybrid Petri Net and Hybrid Dynamic Net		183	(7)
7.3 Hybrid Functional Petri Net		190	(1)
7.4 Hybrid Functional Petri Net with Extension		191	(7)
7.4.1 Definitions		191	(6)
7.4.2 Relationships with Other Petri Nets		197	(1)
7.4.3 Implementation of HFPNe in Genomic Object Net		198	(1)
7.5 Modeling of Biological Processes with HFPNe		198	(13)
7.5.1 From DNA to mRNA in Eucaryotes - Alternative Splicing		199	(4)
7.5.2 Translation of mRNA - Frameshift		203	(1)
7.5.3 Huntington's Disease		203	(4)
7.5.4 Protein Modification - p53		207	(4)
7.6 Related Works with HFPNe		211	(1)
7.7 Genomic Object Net: GON		212	(12)
7.7.1 GON Features That Derived from HFPNe Features		214	(1)
7.7.2 GON GUI and Other Features		214	(6)
7.7.3 GONML and Related Works with GONML		220	(2)
7.7.4 Related Works with GON		222	(2)
7.8 Visualizer		224	(9)
7.8.1 Bio-processes on Visualizer		226	(5)
7.8.2 Related Works with Visualizer		231	(2)
7.9 BPE		233	(3)
7.10 Conclusion		236	(1)
References		236	(7)
8 Biological Sequence Assembly and Alignment		243	(20)
8.1 Introduction		243	(2)
8.2 Large-Scale Sequence Assembly		245	(9)
8.2.1 Related Research		245	(4)
8.2.2 Euler Sequence Assembly		249	(1)
8.2.3 PESA Sequence Assembly Algorithm		249	(5)
8.3 Large-Scale Pairwise Sequence Alignment		254	(3)
8.3.1 Pairwise Sequence Alignment		254	(2)
8.3.2 Large Smith-Waterman Pairwise Sequence Alignment		256	(1)
8.4 Large-Scale Multiple Sequence Alignment		257	(2)
8.4.1 Multiple Sequence Alignment		257	(1)
8.4.2 Large-Scale Clustal W Multiple Sequence Alignment		258	(1)
8.5 Load Balancing and Communication Overhead		259	(1)
8.6 Conclusion		259	(1)
References		260	(3)
9 Modeling for Bioinformatics		263	(36)
9.1 Introduction		263	(1)
9.2 Hidden Markov Modeling for Biological Data Analysis		264	(17)
9.2.1 Hidden Markov Modeling for Sequence Identification		264	(9)
9.2.2 Hidden Markov Modeling for Sequence Classification		273	(5)
9.2.3 Hidden Markov Modeling for Multiple Alignment Generation		278	(2)
9.2.4 Conclusion		280	(1)
9.3 Comparative Modeling		281	(6)
9.3.1 Protein Comparative Modeling		281	(3)
9.3.2 Comparative Genomic Modeling		284	(3)
9.4 Probabilistic Modeling		287	(3)
9.4.1 Bayesian Networks		287	(1)
9.4.2 Stochastic Context-Free Grammars		288	(1)
9.4.3 Probabilistic Boolean Networks		288	(2)
9.5 Molecular Modeling		290	(7)
9.5.1 Molecular and Related Visualization Applications		290	(4)
9.5.2 Molecular Mechanics		294	(1)
9.5.3 Modern Computer Programs for Molecular Modeling		295	(2)
References		297	(2)
10 Pattern Matching for Motifs		299	(14)
10.1 Introduction		299	(2)
10.2 Gene Regulation		301	(2)
10.2.1 Promoter Organization		302	(1)
10.3 Motif Recognition		303	(2)
10.4 Motif Detection Strategies		305	(2)
10.4.1 Multi-genes, Single Species Approach		306	(1)
10.5 Single Gene, Multi-species Approach		307	(2)
10.6 Multi-genes, Multi-species Approach		309	(1)
10.7 Summary		309	(1)
References		310	(3)
11 Visualization and Fractal Analysis of Biological Sequences		313	(40)
11.1 Introduction		313	(4)
11.2 Fractal Analysis		317	(6)
11.2.1 What Is a Fractal?		317	(2)
11.2.2 Recurrent Iterated Function System Model		319	(1)
11.2.3 Moment Method to Estimate the Parameters of the IFS (RIFS) Model		320	(1)
11.2.4 Multifractal Analysis		321	(2)
11.3 DNA Walk Models		323	(2)
11.3.1 One-Dimensional DNA Walk		323	(1)
11.3.2 Two-Dimensional DNA Walk		324	(1)
11.3.3 Higher-Dimensional DNA Walk		325	(1)
11.4 Chaos Game Representation of Biological Sequences		325	(5)
11.4.1 Chaos Game Representation of DNA Sequences		325	(1)
11.4.2 Chaos Game Representation of Protein Sequences		326	(1)
11.4.3 Chaos Game Representation of Protein Structures		326	(1)
11.4.4 Chaos Game Representation of Amino Acid Sequences Based on the Detailed HP Model		327	(3)
11.5 Two-Dimensional Portrait Representation of DNA Sequences		330	(5)
11.5.1 Graphical Representation of Counters		330	(2)
11.5.2 Fractal Dimension of the Fractal Set for a Given Tag		332	(3)
11.6 One-Dimensional Measure Representation of Biological Sequences		335	(13)
11.6.1 Measure Representation of Complete Genomes		335	(5)
11.6.2 Measure Representation of Linked Protein Sequences		340	(4)
11.6.3 Measure Representation of Protein Sequences Based on Detailed HP Model		344	(4)
References		348	(5)
12 Microarray Data Analysis		353	(36)
12.1 Introduction		353	(1)
12.2 Microarray Technology for Genome Expression Study		354	(2)
12.3 Image Analysis for Data Extraction		356	(7)
12.3.1 Image Preprocessing		357	(2)
12.3.2 Block Segmentation		359	(1)
12.3.3 Automatic Gridding		360	(1)
12.3.4 Spot Extraction		360	(1)
12.3.5 Background Correction, Data Normalization and Filtering, and Missing Value Estimation		361	(2)
12.4 Data Analysis for Pattern Discovery		363	(21)
12.4.1 Cluster Analysis		363	(8)
12.4.2 Temporal Expression Profile Analysis and Gene Regulation		371	(11)
12.4.3 Gene Regulatory Network Analysis		382	(2)
References		384	(5)
Index		389

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.