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发表于 2010-9-19 18:29 |只看该作者 |倒序浏览 |打印

本帖最后由细胞海洋于 2010-9-19 20:20 编辑
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SECTION I INTRODUCTION – DATA DIVERSITY AND INTEGRATION 1- I* ]' y/ h# z6 S5 N
1 Integrative Data Analysis and Visualization: Introduction5 d/ h+ V7 R9 n4 R/ {
to Critical Problems, Goals and Challenges 34 W* z5 c: i! @
Francisco Azuaje and Joaquı´n Dopazo/ n2 }& E, U7 f  ~1 u4 H
1.1 Data Analysis and Visualization: An Integrative Approach 3
1.2 Critical Design and Implementation Factors 5! T9 w; w  I3 _; `9 ~
1.3 Overview of Contributions 8
References 91 G) n- G" Q* o
2 Biological Databases: Infrastructure, Content! T# {0 \6 v* w* v: h, f7 Z: l
and Integration 11
Allyson L. Williams, Paul J. Kersey, Manuela Pruess+ c9 }( h2 g; h9 \5 M4 @
and Rolf Apweiler
2.1 Introduction 11
2.2 Data Integration 12
2.3 Review of Molecular Biology Databases 17
2.4 Conclusion 23
References 26- Q$ S8 M4 n6 V5 G7 N' v0 w" {
3 Data and Predictive Model Integration: an Overview
of Key Concepts, Problems and Solutions 29
Francisco Azuaje, Joaquı´n Dopazo and Haiying Wang
3.1 Integrative Data Analysis and Visualization: Motivation and Approaches 293 \" s, ~5 X0 R8 j
3.2 Integrating Informational Views and Complexity for Understanding Function 31
3.3 Integrating Data Analysis Techniques for Supporting Functional# t8 F2 j& U" ]& q, [, V
SECTION II INTEGRATIVE DATA MINING AND VISUALIZATION –- }3 p1 I2 M/ @
EMPHASIS ON COMBINATION OF MULTIPLE
DATA TYPES 41/ {, N/ N9 k" t' q- S- k9 U
4 Applications of Text Mining in Molecular Biology, from Name
Recognition to Protein Interaction Maps 43
Martin Krallinger and Alfonso Valencia5 f# ]! e6 m# N) I/ V
4.1 Introduction 44
4.2 Introduction to Text Mining and NLP 45
4.3 Databases and Resources for Biomedical Text Mining 47& ?, ~/ [! [) s& ]) i1 [
4.4 Text Mining and Protein–Protein Interactions 50
4.5 Other Text-Mining Applications in Genomics 55
4.6 The Future of NLP in Biomedicine 56; r* t8 F0 @, g' @& c2 x4 H
Acknowledgements 56
References 56) \5 n6 _- F& M! v
5 Protein Interaction Prediction by Integrating Genomic, b+ \) D! u1 u4 X6 U1 Q* y
Features and Protein Interaction Network Analysis 61
Long J. Lu, Yu Xia, Haiyuan Yu, Alexander Rives, Haoxin Lu,. f/ l& p0 L3 K
Falk Schubert and Mark Gerstein
5.1 Introduction 62% s7 e, h$ J0 ~% l+ f
5.2 Genomic Features in Protein Interaction Predictions 63
5.3 Machine Learning on Protein–Protein Interactions 67
5.4 The Missing Value Problem 73
5.5 Network Analysis of Protein Interactions 75
5.6 Discussion 79( j) g) M6 m% i( X/ j7 B
References 803 J8 q1 m$ S# T$ p
6 Integration of Genomic and Phenotypic Data 836 y1 o4 {3 M/ n/ V# q
Amanda Clare3 I9 M) y$ ~' O5 _! b* p) ]
6.1 Phenotype 83
6.2 Forward Genetics and QTL Analysis 85- g, E1 ~% D9 m: O( E7 V
6.3 Reverse Genetics 87# V4 u" i( g, C% H
6.4 Prediction of Phenotype from Other Sources of Data 887 {' z4 ~& h& B; @  U# @9 Z" }
6.5 Integrating Phenotype Data with Systems Biology 90
6.6 Integration of Phenotype Data in Databases 93
6.7 Conclusions 95
References 953 y1 o! U# |% _) W
7 Ontologies and Functional Genomics 99
Fa´tima Al-Shahrour and Joaquı´n Dopazo9 h9 [# s9 o2 a9 u
7.1 Information Mining in Genome-Wide Functional Analysis 99* ]+ {9 ~: ]' z- m9 H5 O
7.2 Sources of Information: Free Text Versus Curated Repositories 100, F2 z+ U7 z1 H- m0 a
7.3 Bio-Ontologies and the Gene Ontology in Functional Genomics 101
7.4 Using GO to Translate the Results of Functional Genomic Experiments into
Biological Knowledge 103
7.5 Statistical Approaches to Test Significant Biological Differences 104
7.6 Using FatiGO to Find Significant Functional Associations3 ~- V% a  {! {. s! q  W6 n; |8 T
in Clusters of Genes 106
7.7 Other Tools 107
7.8 Examples of Functional Analysis of Clusters of Genes 108
7.9 Future Prospects 110+ n& p) b5 H3 n
References 110
8 The C. elegans Interactome: its Generation and Visualization 113
Alban Chesnau and Claude Sardet
8.1 Introduction 113
8.2 The ORFeome: the first step toward the interactome of C. elegans 116, w5 C8 F/ B  G+ Z. U7 C0 q
8.3 Large-Scale High-Throughput Yeast Two-Hybrid Screens to Map the C. elegans' H: e" F- Y: L9 e1 v, N$ Z
Protein–Protein Interaction (Interactome) Network: Technical Aspects 1186 @( R2 g1 a: j; G; W
8.4 Visualization and Topology of Protein–Protein Interaction Networks 121. @5 Q" d8 k  i+ p
8.5 Cross-Talk Between the C. elegans Interactome and other Large-Scale$ Q- L5 J/ U1 a1 J6 R& [
Genomics and Post-Genomics Data Sets 123  v: F( s# e  N3 F
8.6 Conclusion: From Interactions to Therapies 129. F  \9 \8 b8 `0 `( A8 f/ y
References 130
SECTION III INTEGRATIVE DATA MINING AND
VISUALIZATION – EMPHASIS ON- H8 c( F7 |) N# E3 T/ _
COMBINATION OF MULTIPLE
PREDICTION MODELS AND METHODS 135
9 Integrated Approaches for Bioinformatic Data Analysis
and Visualization – Challenges, Opportunities
and New Solutions 137/ n# \8 W" C8 S; {3 p
Steve R. Pettifer, James R. Sinnott and Teresa K. Attwood
9.1 Introduction 137
9.2 Sequence Analysis Methods and Databases 139, E, n6 X/ s& x) M- T5 Z) {+ a( \
9.3 A View Through a Portal 141
9.4 Problems with Monolithic Approaches: One Size Does Not Fit All 142
9.5 A Toolkit View 143
9.6 Challenges and Opportunities 145
9.7 Extending the Desktop Metaphor 147
9.8 Conclusions 151+ g+ L  J2 ]+ G/ d' J
Acknowledgements 151( U& [+ M0 P3 ~4 _3 ]% M, `8 N- B: u
References 1529 e, `) v4 U8 y- h
10 Advances in Cluster Analysis of Microarray Data 1534 R& z& I+ [. i/ k, K
Qizheng Sheng, Yves Moreau, Frank De Smet, Kathleen Marchal- K# ~4 C4 l% `7 [! @
and Bart De Moor
10.1 Introduction 153
10.2 Some Preliminaries 155! m4 a' g2 `8 @0 g$ u( ?+ c# a
10.3 Hierarchical Clustering 157
10.4 k-Means Clustering 159
10.5 Self-Organizing Maps 159
10.6 A Wish List for Clustering Algorithms 160
10.7 The Self-Organizing Tree Algorithm 161
10.8 Quality-Based Clustering Algorithms 162
10.9 Mixture Models 1635 r8 u9 j5 g+ f' Z: v
10.10 Biclustering Algorithms 166
10.11 Assessing Cluster Quality 1682 `/ _5 J# V8 Q1 _. L  s8 L
10.12 Open Horizons 170
References 171+ f; o& k# A/ k$ a6 r( j9 m0 G+ @
11 Unsupervised Machine Learning to Support Functional
Characterization of Genes: Emphasis on Cluster( N% ^; C) E! M( I8 E  R
Description and Class Discovery 175
Olga G. Troyanskaya8 A, x" Q5 x. k: P5 u' q  A8 `8 [2 K3 v. z
11.1 Functional Genomics: Goals and Data Sources 175
11.2 Functional Annotation by Unsupervised Analysis of Gene
Expression Microarray Data 177
11.3 Integration of Diverse Functional Data For Accurate Gene Function
Prediction 179
11.4 MAGIC – General Probabilistic Integration of Diverse Genomic Data 180; P, G+ w: p0 a; v
11.5 Conclusion 188+ D/ ~# U% k( T8 G8 q1 q9 G( W
References 189
12 Supervised Methods with Genomic Data: a Review
and Cautionary View 193
Ramo´n Dı´az-Uriarte
12.1 Chapter Objectives 1939 C  M5 z8 e1 {) S& y
12.2 Class Prediction and Class Comparison 194
12.3 Class Comparison: Finding/Ranking Differentially Expressed Genes 194
12.4 Class Prediction and Prognostic Prediction 198; I! U/ i4 V) M+ g
12.5 ROC Curves for Evaluating Predictors and Differential Expression 201
12.6 Caveats and Admonitions 203+ V0 l0 W( |2 v2 P3 L9 ?, }1 t% l
12.7 Final Note: Source Code Should be Available 209
Acknowledgements 210: {; _7 `! w! F3 r% G1 R
References 210
13 A Guide to the Literature on Inferring Genetic Networks! m9 N( J0 b2 @, P* E  s
by Probabilistic Graphical Models 215, M0 \; t! s1 G5 L$ q( f+ U) g1 ^
Pedro Larran˜aga, In˜aki Inza and Jose L. Flores
13.1 Introduction 215  n  A: a# O4 ]
13.2 Genetic Networks 216
13.3 Probabilistic Graphical Models 218
13.4 Inferring Genetic Networks by Means of Probabilistic Graphical Models 229
13.5 Conclusions 234  W1 B3 \  ]6 B6 a# u
Acknowledgements 235
References 235+ S8 J( D' S! ?- H" V
14 Integrative Models for the Prediction and Understanding
of Protein Structure Patterns 239
Inge Jonassen
14.1 Introduction 239
14.2 Structure Prediction 241
14.3 Classifications of Structures 244. X& R9 K! S4 f1 G
14.4 Comparing Protein Structures 246+ \8 }7 s0 P* c  T6 x9 e5 A* Q7 X0 z
14.5 Methods for the Discovery of Structure Motifs 249
14.6 Discussion and Conclusions 252
References 254
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