Bioinformatics Technologies

By: Yi-Ping Phoebe Chen (Editor)

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Hardcover | 1 February 2005

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418 Pages

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24.13 x 15.88 x 1.91

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Solving modern biological problems requires advanced computational methods. Bioinformatics evolved from the active interaction of two fast-developing disciplines, biology and information technology. The central issue of this emerging field is the transformation of often distributed and unstructured biological data into meaningful information.

This book describes the application of well-established concepts and techniques from areas like data mining, machine learning, database technologies, and visualization techniques to problems like protein data analysis, genome analysis and sequence databases. Chen has collected contributions from leading researchers in each area. The chapters can be read independently, as each offers a complete overview of its specific area, or, combined, this monograph is a comprehensive treatment that will appeal to students, researchers, and R&D professionals in industry who need a state-of-the-art introduction into this challenging and exciting young field.

Industry Reviews

From the reviews:

"Today, the word 'bioinformatics' is most commonly used to denote computational molecular biology. ... This book collects surveys of the most significant themes of the field, written by various researchers. ... Extensive bibliographies accompany each chapter. ... the wide range of subject matter covered makes the book a good starting point for students or researchers with the necessary computer science background who are embarking on bioinformatics projects or research in the area." (R. M. Malyankar, Computing Reviews, April, 2006)

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Paperback

Published: 31st March 2014

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Preface	p. V
Introduction to Bioinformatics	p. 1
Introduction	p. 1
Needs of Bioinformatics Technologies	p. 2
An Overview of Bioinformatics Technologies	p. 5
A Brief Discussion on the Chapters	p. 8
References	p. 12
Overview of Structural Bioinformatics 15
Introduction	p. 15
Organization of Structural Bioinformatics	p. 17
Primary Resource: Protein Data Bank	p. 18
Data Format	p. 18
Growth of Data	p. 18
Data Processing and Quality Control	p. 20
The Future of the PDB	p. 21
Visualization	p. 21
Secondary Resources and Applications	p. 22
Structural Classification	p. 22
Structure Prediction	p. 28
Functional Assignments in Structural Genomics	p. 30
Protein-Protein Interactions	p. 32
Protein-Ligand Interactions	p. 34
Using Structural Bioinformatics Approaches in Drug Design	p. 37
The Future	p. 39
Integration over Multiple Resources	p. 39
The Impact of Structural Genomics	p. 39
The Role of Structural Bioinformatics in Systems Biology	p. 39
References	p. 40
Database Warehousing in Bioinformatics	p. 45
Introduction	p. 45
Bioinformatics Data	p. 48
Transforming Data to Knowledge	p. 51
Data Warehousing	p. 54
Data Warehouse Architecture	p. 56
Data Quality	p. 58
Concluding Remarks	p. 60
References	p. 61
Data Mining for Bioinformatics	p. 63
Introduction	p. 63
Biomedical Data Analysis	p. 64
Major Nucleotide Sequence Database, Protein Sequence Database, and Gene Expression Database	p. 65
Software Tools for Bioinformatics Research	p. 68
DNA Data Analysis	p. 71
DNA Sequence	p. 71
DNA Data Analysis	p. 76
Protein Data Analysis	p. 92
Protein and Amino Acid Sequence	p. 92
Protein Data Analysis	p. 99
References	p. 109
Machine Learning in Bioinformatics	p. 117
Introduction	p. 117
Artificial Neural Network	p. 120
Neural Network Architectures and Applications	p. 128
Neural Network Architecture	p. 128
Neural Network Learning Algorithms	p. 131
Neural Network Applications in Bioinformatics	p. 134
Genetic Algorithm	p. 135
Fuzzy System	p. 141
References	p. 147
Systems Biotechnology: a New Paradigm in Biotechnology Development	p. 155
Introduction	p. 155
Why Systems Biotechnology?	p. 156
Tools for Systems Biotechnology	p. 158
Genome Analyses	p. 158
Transcriptome Analyses	p. 159
Proteome Analyses	p. 161
Metabolome/Fluxome Analyses	p. 163
Integrative Approaches	p. 164
In Silico Modeling and Simulation of Cellular Processes	p. 166
Statistical Modeling	p. 167
Dynamic Modeling	p. 169
Conclusion	p. 170
References	p. 171
Computational Modeling of Biological Processes with Petri Net-Based Architecture	p. 179
Introduction	p. 179
Hybrid Petri Net and Hybrid Dynamic Net	p. 183
Hybrid Functional Petri Net	p. 190
Hybrid Functional Petri Net with Extension	p. 191
Definitions	p. 191
Relationships with Other Petri Nets	p. 197
Implementation of HFPNe in Genomic Object Net	p. 198
Modeling of Biological Processes with HFPNe	p. 198
From DNA to mRNA in Eucaryotes - Alternative Splicing	p. 199
Translation of mRNA - Frameshift	p. 203
Huntington's Disease	p. 203
Protein Modification - p53	p. 207
Related Works with HFPNe	p. 211
Genomic Object Net: GON	p. 212
GON Features That Derived from HFPNe Features	p. 214
GON GUI and Other Features	p. 214
GONML and Related Works with GONML	p. 220
Related Works with GON	p. 222
Visualizer	p. 224
Bio-processes on Visualizer	p. 226
Related Works with Visualizer	p. 231
BPE	p. 233
Conclusion	p. 236
References	p. 236
Biological Sequence Assembly and Alignment	p. 243
Introduction	p. 243
Large-Scale Sequence Assembly	p. 245
Related Research	p. 245
Euler Sequence Assembly	p. 249
PESA Sequence Assembly Algorithm	p. 249
Large-Scale Pairwise Sequence Alignment	p. 254
Pairwise Sequence Alignment	p. 254
Large Smith-Waterman Pairwise Sequence Alignment	p. 256
Large-Scale Multiple Sequence Alignment	p. 257
Multiple Sequence Alignment	p. 257
Large-Scale Clustal W Multiple Sequence Alignment	p. 258
Load Balancing and Communication Overhead	p. 259
Conclusion	p. 259
References	p. 260
Modeling for Bioinformatics 263
Introduction	p. 263
Hidden Markov Modeling for Biological Data Analysis	p. 264
Hidden Markov Modeling for Sequence Identification	p. 264
Hidden Markov Modeling for Sequence Classification	p. 273
Hidden Markov Modeling for Multiple Alignment Generation	p. 278
Conclusion	p. 280
Comparative Modeling	p. 281
Protein Comparative Modeling	p. 281
Comparative Genomic Modeling	p. 284
Probabilistic Modeling	p. 287
Bayesian Networks	p. 287
Stochastic Context-Free Grammars	p. 288
Probabilistic Boolean Networks	p. 288
Molecular Modeling	p. 290
Molecular and Related Visualization Applications	p. 290
Molecular Mechanics	p. 294
Modern Computer Programs for Molecular Modeling	p. 295
References	p. 297
Pattern Matching for Motifs	p. 299
Introduction	p. 299
Gene Regulation	p. 301
Promoter Organization	p. 302
Motif Recognition	p. 303
Motif Detection Strategies	p. 305
Multi-genes, Single Species Approach	p. 306
Single Gene, Multi-species Approach	p. 307
Multi-genes, Multi-species Approach	p. 309
Summary	p. 309
References	p. 310
Visualization and Fractal Analysis of Biological Sequences	p. 313
Introduction	p. 313
Fractal Analysis	p. 317
What Is a Fractal?	p. 317
Recurrent Iterated Function System Model	p. 319
Moment Method to Estimate the Parameters of the IFS (RIFS) Model	p. 320
Multifractal Analysis	p. 321
DNA Walk Models	p. 323
One-Dimensional DNA Walk	p. 323
Two-Dimensional DNA Walk	p. 324
Higher-Dimensional DNA Walk	p. 325
Chaos Game Representation of Biological Sequences	p. 325
Chaos Game Representation of DNA Sequences	p. 325
Chaos Game Representation of Protein Sequences	p. 326
Chaos Game Representation of Protein Structures	p. 326
Chaos Game Representation of Amino Acid Sequences Based on the Detailed HP Model	p. 327
Two-Dimensional Portrait Representation of DNA Sequences	p. 330
Graphical Representation of Counters	p. 330
Fractal Dimension of the Fractal Set for a Given Tag	p. 332
One-Dimensional Measure Representation of Biological Sequences	p. 335
Measure Representation of Complete Genomes	p. 335
Measure Representation of Linked Protein Sequences	p. 340
Measure Representation of Protein Sequences Based on Detailed HP Model	p. 344
References	p. 348
Microarray Data Analysis	p. 353
Introduction	p. 353
Microarray Technology for Genome Expression Study	p. 354
Image Analysis for Data Extraction	p. 356
Image Preprocessing	p. 357
Block Segmentation	p. 359
Automatic Gridding	p. 360
Spot Extraction	p. 360
Background Correction, Data Normalization and Filtering, and Missing Value Estimation	p. 361
Data Analysis for Pattern Discovery	p. 363
Cluster Analysis	p. 363
Temporal Expression Profile Analysis and Gene Regulation	p. 371
Gene Regulatory Network Analysis	p. 382
References	p. 384
Index	p. 389
Table of Contents provided by Publisher. All Rights Reserved.

Bioinformatics Technologies

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