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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 & G; x* c  v- j$ d# ~5 o
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SECTION I INTRODUCTION – DATA DIVERSITY AND INTEGRATION 1
1 Integrative Data Analysis and Visualization: Introduction1 i* u6 O% }/ P/ R' ^8 `7 u% A
to Critical Problems, Goals and Challenges 3, F6 X9 r3 d0 t% Q1 h5 ^0 G4 ~( p1 k
Francisco Azuaje and Joaquı´n Dopazo2 E6 E% t+ [/ c2 D- g/ u* T- i
1.1 Data Analysis and Visualization: An Integrative Approach 34 G6 Q3 }0 L( K% G2 l$ d
1.2 Critical Design and Implementation Factors 57 M5 V" R) @4 D4 S2 w
1.3 Overview of Contributions 8" i; a6 u5 ]' r# B
References 9
2 Biological Databases: Infrastructure, Content
and Integration 11
Allyson L. Williams, Paul J. Kersey, Manuela Pruess' B, c9 Q1 X6 }
and Rolf Apweiler* c- g" }' w' c7 f
2.1 Introduction 11# T' ~5 q2 c% P# ?
2.2 Data Integration 12
2.3 Review of Molecular Biology Databases 17- Y$ F" [$ h* j3 c/ E9 b/ l
2.4 Conclusion 23; \) T- D1 }$ c& D: M
References 26
3 Data and Predictive Model Integration: an Overview, F+ s" J! H. U. Z# i6 `
of Key Concepts, Problems and Solutions 29
Francisco Azuaje, Joaquı´n Dopazo and Haiying Wang: r, g5 j& N7 d/ |+ J+ w
3.1 Integrative Data Analysis and Visualization: Motivation and Approaches 29$ j, D  z! W8 x# s+ e+ W1 o0 o
3.2 Integrating Informational Views and Complexity for Understanding Function 31' T" F: v# C3 H$ H0 m; P
3.3 Integrating Data Analysis Techniques for Supporting Functional
SECTION II INTEGRATIVE DATA MINING AND VISUALIZATION –
EMPHASIS ON COMBINATION OF MULTIPLE
DATA TYPES 41% F6 \. J) [$ w! f5 ~
4 Applications of Text Mining in Molecular Biology, from Name3 M4 f1 Q0 t' p! L4 x$ W" F: |
Recognition to Protein Interaction Maps 43
Martin Krallinger and Alfonso Valencia
4.1 Introduction 44
4.2 Introduction to Text Mining and NLP 45
4.3 Databases and Resources for Biomedical Text Mining 47
4.4 Text Mining and Protein–Protein Interactions 50! u6 a4 u( O1 j7 P5 l
4.5 Other Text-Mining Applications in Genomics 55. \: D* u4 b: R7 [" E
4.6 The Future of NLP in Biomedicine 56
Acknowledgements 565 x  ^& S6 t' F5 E$ N
References 56
5 Protein Interaction Prediction by Integrating Genomic
Features and Protein Interaction Network Analysis 614 g: j3 s4 `( d. K* X. }0 H
Long J. Lu, Yu Xia, Haiyuan Yu, Alexander Rives, Haoxin Lu,
Falk Schubert and Mark Gerstein/ p( z3 x; s* D- C  g# }
5.1 Introduction 62! G1 R8 c( c0 b. L" m9 z1 o+ N
5.2 Genomic Features in Protein Interaction Predictions 63
5.3 Machine Learning on Protein–Protein Interactions 67( W3 b/ n, x% `+ O. l: n
5.4 The Missing Value Problem 739 |6 x8 t! _+ Z; v
5.5 Network Analysis of Protein Interactions 75+ P4 t/ @  f- u1 B5 ~  Q" ?* v
5.6 Discussion 79/ p! ^. Q4 U; D3 w
References 80* X3 w- c. B( G3 d9 H  q6 m) W
6 Integration of Genomic and Phenotypic Data 83  U" J0 e# q: @# E6 S9 }$ z* D& ]/ S
Amanda Clare! e; Y7 M, N- w+ l1 j$ N; i% l
6.1 Phenotype 83
6.2 Forward Genetics and QTL Analysis 85* A  u5 S8 y: m- N! @' H" ?" M
6.3 Reverse Genetics 87
6.4 Prediction of Phenotype from Other Sources of Data 88# F* \  i) b* n
6.5 Integrating Phenotype Data with Systems Biology 90
6.6 Integration of Phenotype Data in Databases 93
6.7 Conclusions 954 G0 @1 D1 e' S9 g& }9 v+ \$ x
References 95
7 Ontologies and Functional Genomics 99) _: S. S3 O0 Y9 J+ q2 G7 m' x
Fa´tima Al-Shahrour and Joaquı´n Dopazo" }8 ]2 ?1 m: _* t. y3 P: Q9 P5 h
7.1 Information Mining in Genome-Wide Functional Analysis 99
7.2 Sources of Information: Free Text Versus Curated Repositories 1006 V& T/ k- d/ F6 |/ d  R$ R" ]
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, \& u' Y# i! K. Q9 A! f
Biological Knowledge 103
7.5 Statistical Approaches to Test Significant Biological Differences 1042 J8 A4 `% r) r' S& u5 X$ v3 S' Z  z7 ~
7.6 Using FatiGO to Find Significant Functional Associations6 x3 O7 p* V0 J3 b! ~; q& Z2 P; x
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
References 110
8 The C. elegans Interactome: its Generation and Visualization 113
Alban Chesnau and Claude Sardet2 l3 K- B+ Q5 w$ z) t: U% R- `4 i
8.1 Introduction 113
8.2 The ORFeome: the first step toward the interactome of C. elegans 116
8.3 Large-Scale High-Throughput Yeast Two-Hybrid Screens to Map the C. elegans
Protein–Protein Interaction (Interactome) Network: Technical Aspects 118
8.4 Visualization and Topology of Protein–Protein Interaction Networks 1215 l" Y) v% e  Y" D) Z  d# ?
8.5 Cross-Talk Between the C. elegans Interactome and other Large-Scale
Genomics and Post-Genomics Data Sets 123
8.6 Conclusion: From Interactions to Therapies 129
References 130
SECTION III INTEGRATIVE DATA MINING AND; c# s- p1 e& q7 j8 Q! T
VISUALIZATION – EMPHASIS ON
COMBINATION OF MULTIPLE
PREDICTION MODELS AND METHODS 135' a$ [$ B! W( C( j% b. X
9 Integrated Approaches for Bioinformatic Data Analysis
and Visualization – Challenges, Opportunities5 [( T0 e4 H8 k% s8 N( y& J
and New Solutions 137" r1 |. k( L% j) s( a
Steve R. Pettifer, James R. Sinnott and Teresa K. Attwood: V1 m7 k9 K) [) h! [
9.1 Introduction 1377 n3 C9 G, p! B  D/ S
9.2 Sequence Analysis Methods and Databases 139
9.3 A View Through a Portal 141
9.4 Problems with Monolithic Approaches: One Size Does Not Fit All 142$ U3 U& |- j7 C) }0 v
9.5 A Toolkit View 143
9.6 Challenges and Opportunities 1455 ?! h) ?0 d6 p: r% b' B
9.7 Extending the Desktop Metaphor 147) V' v# k' |& e. ~( f" S
9.8 Conclusions 151
Acknowledgements 151
References 152
10 Advances in Cluster Analysis of Microarray Data 153
Qizheng Sheng, Yves Moreau, Frank De Smet, Kathleen Marchal
and Bart De Moor
10.1 Introduction 153
10.2 Some Preliminaries 155
10.3 Hierarchical Clustering 157
10.4 k-Means Clustering 159
10.5 Self-Organizing Maps 159* [3 E  E3 h- A/ P3 S( O
10.6 A Wish List for Clustering Algorithms 160& I- a- u/ q: c* m6 F
10.7 The Self-Organizing Tree Algorithm 161: Y! S! b7 C$ ~& }
10.8 Quality-Based Clustering Algorithms 162
10.9 Mixture Models 163- j) k! X0 s2 i, }. |2 r; C+ l
10.10 Biclustering Algorithms 166
10.11 Assessing Cluster Quality 168) S1 a, m  X$ ?9 q& b
10.12 Open Horizons 170
References 171
11 Unsupervised Machine Learning to Support Functional2 n  g3 Z, d1 \/ K0 ^7 H. B
Characterization of Genes: Emphasis on Cluster+ E% ~) j. u7 \3 I3 G1 G. x3 N1 R
Description and Class Discovery 1757 |( x" g$ F# q1 I7 e1 Q' g
Olga G. Troyanskaya; r+ Z, w2 \5 I
11.1 Functional Genomics: Goals and Data Sources 175$ B4 C& q0 W7 b" c
11.2 Functional Annotation by Unsupervised Analysis of Gene
Expression Microarray Data 177% I0 t: ~, X+ D% C5 v/ v+ |
11.3 Integration of Diverse Functional Data For Accurate Gene Function" f, Y) r) ?$ u! D! x
Prediction 179- T# I1 `& z- ^( B8 b. I
11.4 MAGIC – General Probabilistic Integration of Diverse Genomic Data 180
11.5 Conclusion 1882 P0 a3 b- V; z& s2 k$ ^
References 189+ p6 F8 L2 J: c! [: y6 Q
12 Supervised Methods with Genomic Data: a Review4 M( z4 n+ V3 H& k: p
and Cautionary View 193- r5 I1 H$ X9 g0 P, p
Ramo´n Dı´az-Uriarte
12.1 Chapter Objectives 193! @, `. S+ M3 ^' B
12.2 Class Prediction and Class Comparison 1943 q2 i6 Y/ q! L  l& b: x2 D
12.3 Class Comparison: Finding/Ranking Differentially Expressed Genes 194( {1 s* ?0 u6 t' ?+ u  p! d4 Y
12.4 Class Prediction and Prognostic Prediction 198- C9 w! c- ~- W9 D; i
12.5 ROC Curves for Evaluating Predictors and Differential Expression 2011 t' R8 K7 c5 l2 E6 q: g
12.6 Caveats and Admonitions 203
12.7 Final Note: Source Code Should be Available 209
Acknowledgements 2108 }  h; P) ?# c2 W8 W) \
References 2107 \4 d; R4 Y: a/ D* M. d
13 A Guide to the Literature on Inferring Genetic Networks2 P4 ~2 L( _  p
by Probabilistic Graphical Models 215
Pedro Larran˜aga, In˜aki Inza and Jose L. Flores
13.1 Introduction 2151 r7 q  Q! a  A9 Q! S4 x, K
13.2 Genetic Networks 216& i9 S2 l& k4 S5 @. h- w
13.3 Probabilistic Graphical Models 2180 D6 }; y3 x# {7 Q- h
13.4 Inferring Genetic Networks by Means of Probabilistic Graphical Models 229
13.5 Conclusions 234' _/ _! I  `6 K, b& V" D! f
Acknowledgements 2359 a+ V8 i- g; ]" \4 }
References 235
14 Integrative Models for the Prediction and Understanding
of Protein Structure Patterns 239( v1 I, v0 l- z1 v, j% u. p
Inge Jonassen" }# X$ ]/ z0 c4 [$ X* V# ?( Z
14.1 Introduction 239/ F; f7 @5 ]$ H; }  |
14.2 Structure Prediction 241
14.3 Classifications of Structures 244
14.4 Comparing Protein Structures 246/ d" n# r+ C/ d
14.5 Methods for the Discovery of Structure Motifs 249, u. C* @& }4 T9 Z- w
14.6 Discussion and Conclusions 252$ q0 h' |! x* v
References 2540 C3 F4 P9 ?4 A

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