Bioinformatics Technologies
暫譯: 生物資訊技術

Yi-Ping Phoebe Chen

Bioinformatics Technologies
  • 出版商: Demos Medical Publis
  • 出版日期: 2005-01-18
  • 售價: $1,150
  • 貴賓價: 9.8 折 $1,127
  • 語言: 英文
  • 頁數: 396
  • 裝訂: Hardcover
  • ISBN: 3540208739
  • ISBN-13: 9783540208730
  • 相關分類: 生物資訊 Bioinformatics

    • 下單後立即進貨 (約5~7天)

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Description

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.

 

Table of contents


Preface ...................................................................................................V

1 Introduction to Bioinformatics............................................................1
1.1 Introduction ...................................................................................1
1.2 Needs of Bioinformatics Technologies...........................................2
1.3 An Overview of Bioinformatics Technologies................................5
1.4 A Brief Discussion on the Chapters................................................8
References.........................................................................................12

2 Overview of Structural Bioinformatics.............................................15
2.1 Introduction .................................................................................15
2.2 Organization of Structural Bioinformatics ....................................17
2.3 Primary Resource: Protein Data Bank ..........................................18
2.3.1 Data Format..........................................................................18
2.3.2 Growth of Data .....................................................................18
2.3.3 Data Processing and Quality Control.....................................20
2.3.4 The Future of the PDB..........................................................21
2.3.5 Visualization.........................................................................21
2.4 Secondary Resources and Applications ........................................22
2.4.1 Structural Classification ........................................................22
2.4.2 Structure Prediction ..............................................................28
2.4.3 Functional Assignments in Structural Genomics....................30
2.4.4 Protein-Protein Interactions...................................................32
2.4.5 Protein-Ligand Interactions ...................................................34
2.5 Using Structural Bioinformatics Approaches in Drug Design .......37
2.6 The Future...................................................................................39
2.6.1 Integration over Multiple Resources ......................................39
2.6.2 The Impact of Structural Genomics .......................................39
2.6.3 The Role of Structural Bioinformatics in Systems Biology ....39
References.........................................................................................40

3 Database Warehousing in Bioinformatics.........................................45
3.1 Introduction .................................................................................45
3.2 Bioinformatics Data.....................................................................48
3.3 Transforming Data to Knowledge ................................................51
3.4 Data Warehousing .......................................................................54
3.5 Data Warehouse Architecture.......................................................56
3.6 Data Quality ................................................................................58
3.7 Concluding Remarks....................................................................60
XII Contents
References.........................................................................................61

4 Data Mining for Bioinformatics ........................................................63
4.1 Introduction .................................................................................63
4.2 Biomedical Data Analysis............................................................64
4.2.1 Major Nucleotide Sequence Database, Protein Sequence
Database, and Gene Expression Database..............................65
4.2.2 Software Tools for Bioinformatics Research .........................68
4.3 DNA Data Analysis .....................................................................71
4.3.1 DNA Sequence .....................................................................71
4.3.2 DNA Data Analysis ..............................................................76
4.4 Protein Data Analysis ..................................................................92
4.4.1 Protein and Amino Acid Sequence ........................................92
4.4.2 Protein Data Analysis............................................................99
References.......................................................................................109

5 Machine Learning in Bioinformatics ..............................................117
5.1 Introduction ...............................................................................117
5.2 Artificial Neural Network ..........................................................120
5.3 Neural Network Architectures and Applications.........................128
5.3.1 Neural Network Architecture ..............................................128
5.3.2 Neural Network Learning Algorithms .................................131
5.3.3 Neural Network Applications in Bioinformatics ..................134
5.4 Genetic Algorithm.....................................................................135
5.5 Fuzzy System ............................................................................141
References.......................................................................................147

6 Systems Biotechnology: a New Paradigm in Biotechnology
Development ....................................................................................155
6.1 Introduction ...............................................................................155
6.2 Why Systems Biotechnology?....................................................156
6.3 Tools for Systems Biotechnology...............................................158
6.3.1 Genome Analyses ...............................................................158
6.3.2 Transcriptome Analyses ......................................................159
6.3.3 Proteome Analyses..............................................................161
6.3.4 Metabolome/Fluxome Analyses ..........................................163
6.4 Integrative Approaches ..............................................................164
6.5 In Silico Modeling and Simulation of Cellular Processes............166
6.5.1 Statistical Modeling ............................................................167
6.5.2 Dynamic Modeling .............................................................169
6.6 Conclusion ................................................................................170
References.......................................................................................171
Contents XIII

7 Computational Modeling of Biological Processes with Petri Net-
Based Architecture ..........................................................................179
7.1 Introduction ...............................................................................179
7.2 Hybrid Petri Net and Hybrid Dynamic Net.................................183
7.3 Hybrid Functional Petri Net .......................................................190
7.4 Hybrid Functional Petri Net with Extension ...............................191
7.4.1 Definitions ..........................................................................191
7.4.2 Relationships with Other Petri Nets.....................................197
7.4.3 Implementation of HFPNe in Genomic Object Net..............198
7.5 Modeling of Biological Processes with HFPNe ..........................198
7.5.1 From DNA to mRNA in Eucaryotes – Alternative Splicing .199
7.5.2 Translation of mRNA – Frameshift .....................................203
7.5.3 Huntington’s Disease ..........................................................203
7.5.4 Protein Modification – p53..................................................207
7.6 Related Works with HFPNe.......................................................211
7.7 Genomic Object Net: GON........................................................212
7.7.1 GON Features That Derived from HFPNe Features .............214
7.7.2 GON GUI and Other Features .............................................214
7.7.3 GONML and Related Works with GONML ........................220
7.7.4 Related Works with GON ...................................................222
7.8 Visualizer ..................................................................................224
7.8.1 Bio-processes on Visualizer ................................................226
7.8.2 Related Works with Visualizer ............................................231
7.9 BPE...........................................................................................233
7.10 Conclusion...............................................................................236
References.......................................................................................236

8 Biological Sequence Assembly and Alignment ...............................243
8.1 Introduction ...............................................................................243
8.2 Large-Scale Sequence Assembly................................................245
8.2.1 Related Research.................................................................245
8.2.2 Euler Sequence Assembly ...................................................249
8.2.3 PESA Sequence Assembly Algorithm.................................249
8.3 Large-Scale Pairwise Sequence Alignment ................................254
8.3.1 Pairwise Sequence Alignment .............................................254
8.3.2 Large Smith-Waterman Pairwise Sequence Alignment ........256
8.4 Large-Scale Multiple Sequence Alignment ................................257
8.4.1 Multiple Sequence Alignment .............................................257
8.4.2 Large-Scale Clustal W Multiple Sequence Alignment .........258
8.5 Load Balancing and Communication Overhead..........................259
8.6 Conclusion ................................................................................259
References.......................................................................................260
XIV Contents

9 Modeling for Bioinformatics ...........................................................263
9.1 Introduction ...............................................................................263
9.2 Hidden Markov Modeling for Biological Data Analysis .............264
9.2.1 Hidden Markov Modeling for Sequence Identification.........264
9.2.2 Hidden Markov Modeling for Sequence Classification ........273
9.2.3 Hidden Markov Modeling for Multiple Alignment
Generation ..........................................................................278
9.2.4 Conclusion..........................................................................280
9.3 Comparative Modeling ..............................................................281
9.3.1 Protein Comparative Modeling............................................281
9.3.2 Comparative Genomic Modeling.........................................284
9.4 Probabilistic Modeling...............................................................287
9.4.1 Bayesian Networks .............................................................287
9.4.2 Stochastic Context-Free Grammars .....................................288
9.4.3 Probabilistic Boolean Networks ..........................................288
9.5 Molecular Modeling ..................................................................290
9.5.1 Molecular and Related Visualization Applications...............290
9.5.2 Molecular Mechanics ..........................................................294
9.5.3 Modern Computer Programs for Molecular Modeling .........295
References.......................................................................................297

10 Pattern Matching for Motifs .........................................................299
10.1 Introduction .............................................................................299
10.2 Gene Regulation ......................................................................301
10.2.1 Promoter Organization ......................................................302
10.3 Motif Recognition....................................................................303
10.4 Motif Detection Strategies .......................................................305
10.4.1 Multi-genes, Single Species Approach ..............................306
10.5 Single Gene, Multi-species Approach.......................................307
10.6 Multi-genes, Multi-species Approach.......................................309
10.7 Summary .................................................................................309
References.......................................................................................310

11 Visualization and Fractal Analysis of Biological Sequences.........313
11.1 Introduction .............................................................................313
11.2 Fractal Analysis .......................................................................317
11.2.1 What Is a Fractal? .............................................................317
11.2.2 Recurrent Iterated Function System Model........................319
11.2.3 Moment Method to Estimate the Parameters of the IFS
(RIFS) Model....................................................................320
11.2.4 Multifractal Analysis.........................................................321
11.3 DNA Walk Models ..................................................................323
Contents XV
11.3.1 One-Dimensional DNA Walk............................................323
11.3.2 Two-Dimensional DNA Walk...........................................324
11.3.3 Higher-Dimensional DNA Walk .......................................325
11.4 Chaos Game Representation of Biological Sequences ..............325
11.4.1 Chaos Game Representation of DNA Sequences ...............325
11.4.2 Chaos Game Representation of Protein Sequences.............326
11.4.3 Chaos Game Representation of Protein Structures .............326
11.4.4 Chaos Game Representation of Amino Acid Sequences Based
on the Detailed HP Model............................................................327
11.5 Two-Dimensional Portrait Representation of DNA Sequences .330
11.5.1 Graphical Representation of Counters ...............................330
11.5.2 Fractal Dimension of the Fractal Set for a Given Tag.........332
11.6 One-Dimensional Measure Representation of Biological
Sequences................................................................................335
11.6.1 Measure Representation of Complete Genomes .................335
11.6.2 Measure Representation of Linked Protein Sequences .......340
11.6.3 Measure Representation of Protein Sequences Based on
Detailed HP Model............................................................344
References.......................................................................................348

12 Microarray Data Analysis .............................................................353
12.1 Introduction .............................................................................353
12.2 Microarray Technology for Genome Expression Study.............354
12.3 Image Analysis for Data Extraction..........................................356
12.3.1 Image Preprocessing .........................................................357
12.3.2 Block Segmentation ..........................................................359
12.3.3 Automatic Gridding ..........................................................360
12.3.4 Spot Extraction .................................................................360
12.3.5 Background Correction, Data Normalization and Filtering,
and Missing Value Estimation...........................................361
12.4 Data Analysis for Pattern Discovery.........................................363
12.4.1 Cluster Analysis ................................................................363
12.4.2 Temporal Expression Profile Analysis and Gene
Regulation ........................................................................371
12.4.3 Gene Regulatory Network Analysis...................................382
References.......................................................................................384

Index ...................................................................................................389

 

商品描述(中文翻譯)

**描述**

解決現代生物問題需要先進的計算方法。生物資訊學是生物學和資訊技術這兩個快速發展的學科之間積極互動的產物。這個新興領域的核心問題是將經常分散且無結構的生物數據轉換為有意義的信息。

本書描述了如何將數據挖掘、機器學習、數據庫技術和可視化技術等領域的成熟概念和技術應用於蛋白質數據分析、基因組分析和序列數據庫等問題。陳博士收集了各領域領先研究者的貢獻。各章節可以獨立閱讀，因為每一章都提供了其特定領域的完整概述；或者，這本專著結合起來，將為需要對這個充滿挑戰和激動人心的新興領域進行最先進介紹的學生、研究人員和產業研發專業人士提供全面的處理。

**目錄**

**前言 ...................................................................................................V**

**1 生物資訊學導論............................................................1**
1.1 介紹 ...................................................................................1
1.2 生物資訊學技術的需求 ...........................................2
1.3 生物資訊學技術概述 ..............................................5
1.4 各章節簡要討論 ....................................................8
參考文獻.........................................................................................12

**2 結構生物資訊學概述.............................................15**
2.1 介紹 .................................................................................15
2.2 結構生物資訊學的組織 ...........................................17
2.3 主要資源：蛋白質數據庫 ......................................18
2.3.1 數據格式 ..................................................................18
2.3.2 數據增長 ..................................................................18
2.3.3 數據處理與質量控制 ..........................................20
2.3.4 PDB的未來 ............................................................21
2.3.5 可視化 ........................................................................21
2.4 次要資源與應用 ....................................................22
2.4.1 結構分類 ................................................................22
2.4.2 結構預測 ................................................................28
2.4.3 結構基因組中的功能分配 ................................30
2.4.4 蛋白質-蛋白質相互作用 ......................................32
2.4.5 蛋白質-配體相互作用 ..........................................34
2.5 在藥物設計中使用結構生物資訊學方法 ...............37
2.6 未來 ................................................................................39
2.6.1 多資源整合 ........................................................39
2.6.2 結構基因組的影響 .............................................39
2.6.3 結構生物資訊學在系統生物學中的角色 ........39
參考文獻.........................................................................................40

**3 生物資訊學中的數據倉儲.........................................45**
3.1 介紹 .................................................................................45
3.2 生物資訊學數據 ......................................................48
3.3 將數據轉化為知識 ..................................................51
3.4 數據倉儲 ......................................................................54
3.5 數據倉儲架構 ..........................................................56
3.6 數據質量 ......................................................................58
3.7 總結 ................................................................................60
參考文獻.........................................................................................61

**4 生物資訊學中的數據挖掘 ......................................63**
4.1 介紹 .................................................................................63
4.2 生物醫學數據分析 ..................................................64
4.2.1 主要核苷酸序列數據庫、蛋白質序列數據庫和基因表達數據庫 ........65
4.2.2 生物資訊學研究的軟體工具 .................................68
4.3 DNA數據分析 ..........................................................71
4.3.1 DNA序列 ..................................................................71
4.3.2 DNA數據分析 ......................................................76
4.4 蛋白質數據分析 .......................................................92
4.4.1 蛋白質和氨基酸序列 ...........................................92
4.4.2 蛋白質數據分析 ..................................................99
參考文獻.......................................................................................109

**5 生物資訊學中的機器學習 ...................................117**
5.1 介紹 .................................................................................117
5.2 人工神經網絡 ............................................................120
5.3 神經網絡架構與應用 ..............................................128
5.3.1 神經網絡架構 ....................................................128
5.3.2 神經網絡學習算法 ..............................................131
5.3.3 生物資訊學中的神經網絡應用 ..........................134
5.4 遺傳算法 ......................................................................135
5.5 模糊系統 ......................................................................141
參考文獻.......................................................................................147

**6 系統生物技術：生物技術發展中的新範式 ........155**
6.1 介紹 .................................................................................155
6.2 為什麼選擇系統生物技術？ ....................................156
6.3 系統生物技術的工具 ..............................................158
6.3.1 基因組分析 ..........................................................158
6.3.2 轉錄組分析 ..........................................................159
6.3.3 蛋白質組分析 .......................................................161
6.3.4 代謝組/流量組分析 .............................................163
6.4 整合方法 ......................................................................164
6.5 細胞過程的計算建模與模擬 ..................................166
6.5.1 統計建模 ................................................................167
6.5.2 動態建模 ................................................................169
6.6 結論 ................................................................................170
參考文獻.......................................................................................171

**7 基於Petri網架構的生物過程計算建模 ............179**
7.1 介紹 .................................................................................179
7.2 混合Petri網與混合動態網 ....................................183
7.3 混合功能Petri網 ......................................................190
7.4 帶擴展的混合功能Petri網 ......................................191
7.4.1 定義 ..........................................................................191
7.4.2 與其他Petri網的關係 ..........................................197
7.4.3 在基因組對象網中實現HFPNe ..........................198
7.5 使用HFPNe建模生物過程 ......................................198
7.5.1 從DNA到真核生物的mRNA – 可變剪接 ..........199
7.5.2 mRNA的翻譯 – 框移 ...........................................203
7.5.3 亨廷頓病 ..............................................................203
7.5.4 蛋白質修飾 – p53 ..................................................207
7.6 與HFPNe相關的工作 ................................................211
7.7 基因組對象網：GON ................................................212
7.7.1 GON的特徵源自HFPNe的特徵 .........................214
7.7.2 GON GUI及其他特徵 ...........................................214
7.7.3 GONML及與GONML相關的工作 .......................220
7.7.4 與GON相關的工作 ..............................................222
7.8 可視化工具 ....................................................................

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