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本帖最后由 细胞海洋 于 2010-9-19 20:20 编辑 " G7 U9 I$ u. c4 M/ I1 \
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SECTION I INTRODUCTION – DATA DIVERSITY AND INTEGRATION 18 x5 m/ r! z% Y# K! z
1 Integrative Data Analysis and Visualization: Introduction
5 ^0 s1 R2 J6 o+ l- E0 Ato Critical Problems, Goals and Challenges 39 g4 K5 d/ o0 {& L& z) c0 x
Francisco Azuaje and Joaquı´n Dopazo
- h$ H5 b# r) B1.1 Data Analysis and Visualization: An Integrative Approach 3
: C- Q6 |- E( y' j. \: i& H+ [1.2 Critical Design and Implementation Factors 5. }. n) \. H3 ~$ `
1.3 Overview of Contributions 8
|6 L, E% G$ o6 `# M( tReferences 9
$ W3 C1 A# k ?! R3 Y3 n; T' N' B2 Biological Databases: Infrastructure, Content# S* {( F6 a7 J' ~
and Integration 11
; ^( X; V* h* t7 DAllyson L. Williams, Paul J. Kersey, Manuela Pruess- j& B' W; y# ]4 @8 y2 R
and Rolf Apweiler( ]$ _1 m$ A+ g0 D
2.1 Introduction 11# p% k" x8 K0 Z, v2 n- a
2.2 Data Integration 12
. x. u3 g7 `, x7 k8 E2.3 Review of Molecular Biology Databases 17
, f& ?" w7 p' R$ a- i$ r* w1 z2.4 Conclusion 23
2 }9 L# F7 m+ K! }* ~2 H- O4 }8 wReferences 26) @0 b0 E# U0 T4 O' ~2 f
3 Data and Predictive Model Integration: an Overview
- o" t8 k6 _, w: l4 V- |of Key Concepts, Problems and Solutions 29
) {) c w# c0 ?$ BFrancisco Azuaje, Joaquı´n Dopazo and Haiying Wang
4 q! ^ T% R1 b# `$ d+ {) m3 D& q6 I3.1 Integrative Data Analysis and Visualization: Motivation and Approaches 29
) W$ f& v! B' V6 S# L6 t8 |# s3.2 Integrating Informational Views and Complexity for Understanding Function 31
# y+ B6 U3 {' S2 h3.3 Integrating Data Analysis Techniques for Supporting Functional
( n2 J. m) t; H5 j# C, P7 hSECTION II INTEGRATIVE DATA MINING AND VISUALIZATION –
* V5 u! l! B$ f5 J& x1 nEMPHASIS ON COMBINATION OF MULTIPLE( U/ x( i1 C# f# C) T
DATA TYPES 41
' s: b+ Q2 E& j4 {7 ~/ z% e4 Applications of Text Mining in Molecular Biology, from Name4 s2 [- _5 e+ b8 ~9 W: w i6 R
Recognition to Protein Interaction Maps 43' E3 `* O1 ^: g( C
Martin Krallinger and Alfonso Valencia: o1 c( r2 A0 U4 p6 M8 i% b B
4.1 Introduction 44& W' j( v' B! e" i) t
4.2 Introduction to Text Mining and NLP 45
: K0 r$ q) n H4.3 Databases and Resources for Biomedical Text Mining 47) n$ Q) q) m9 s6 [8 l) B" v
4.4 Text Mining and Protein–Protein Interactions 50; H; I4 s( W. u/ T4 x
4.5 Other Text-Mining Applications in Genomics 552 D& s: y( R5 P% G. o& D0 `2 ]8 u
4.6 The Future of NLP in Biomedicine 56- }- W) q; v2 ^8 u' k
Acknowledgements 56! u& L2 s1 W4 O$ ~5 P1 j
References 56
, Y4 _! c3 e4 Y5 i+ J% o) m) P! Q5 Protein Interaction Prediction by Integrating Genomic
6 D4 B4 M {" B0 KFeatures and Protein Interaction Network Analysis 61
' d) k* {$ Q7 s5 PLong J. Lu, Yu Xia, Haiyuan Yu, Alexander Rives, Haoxin Lu,) d7 N0 D; E# C& `6 A0 B
Falk Schubert and Mark Gerstein. ^4 `/ t' B" n- X" q5 K
5.1 Introduction 620 S+ }1 \8 r8 ]3 b4 l& i/ K
5.2 Genomic Features in Protein Interaction Predictions 63: p5 y4 d) U# g6 y, m1 ?' u6 U1 ?
5.3 Machine Learning on Protein–Protein Interactions 67
6 w3 V% L' g- }' A' ], N% F+ V5.4 The Missing Value Problem 73
3 S) D0 k$ H+ x0 ~1 g9 d& C5.5 Network Analysis of Protein Interactions 75
1 y5 t$ Y: U9 o5 Q2 T5.6 Discussion 79
, w6 t' A3 _7 K% P! B( e* _References 80
2 p! v" I ^7 X" z9 q6 Integration of Genomic and Phenotypic Data 83
+ b8 |0 y- o' w" y6 |Amanda Clare! c5 k0 K/ l V5 d( D3 ~/ D) _
6.1 Phenotype 83+ E2 Y9 T+ b! J8 y
6.2 Forward Genetics and QTL Analysis 85: {' i1 J/ c; O) c8 g4 [
6.3 Reverse Genetics 87
; h0 V0 n! b4 ?; P9 h+ m0 ^6.4 Prediction of Phenotype from Other Sources of Data 88
! Y& Z7 g* x) h2 L& `6.5 Integrating Phenotype Data with Systems Biology 90
0 w9 R. J+ K9 x! J6.6 Integration of Phenotype Data in Databases 93
% b- m& M# i0 s5 X( G2 [6.7 Conclusions 95; p; e9 d3 l! d$ Y5 W
References 95
8 Y4 g* j3 U$ }7 w Q) A7 Ontologies and Functional Genomics 99) ?- v M7 J% {7 ]( f
Fa´tima Al-Shahrour and Joaquı´n Dopazo& H. u4 H7 N. t. Y9 X7 ~
7.1 Information Mining in Genome-Wide Functional Analysis 992 r7 D4 {& x' d9 t8 { y
7.2 Sources of Information: Free Text Versus Curated Repositories 100
5 K( J" ?( T0 y: P- r7.3 Bio-Ontologies and the Gene Ontology in Functional Genomics 1010 o& R. P I4 `3 l
7.4 Using GO to Translate the Results of Functional Genomic Experiments into3 {3 ]8 W# @9 D$ M" i
Biological Knowledge 1036 r+ h- Q1 Y0 Z% z' D1 e
7.5 Statistical Approaches to Test Significant Biological Differences 1044 }3 B! ^' T7 B2 G, A9 Y1 s
7.6 Using FatiGO to Find Significant Functional Associations, e5 |& ~3 N- r
in Clusters of Genes 106
8 n6 [3 s6 W) E8 w2 v7.7 Other Tools 107
& C9 t C9 J9 f, w! G7.8 Examples of Functional Analysis of Clusters of Genes 1089 v; Y' o. c" x* A
7.9 Future Prospects 110
- I+ l5 w. `6 s# x( ]; {$ d% LReferences 1100 J0 l3 N/ o! ^8 A' U; U
8 The C. elegans Interactome: its Generation and Visualization 113/ B3 c [, b* i% ~3 o
Alban Chesnau and Claude Sardet( C6 W" u$ c/ N9 Z# }( c+ [
8.1 Introduction 113( s, Y; m# H1 E w- I
8.2 The ORFeome: the first step toward the interactome of C. elegans 116
0 I' v$ C: Y7 Y8.3 Large-Scale High-Throughput Yeast Two-Hybrid Screens to Map the C. elegans
) ?* }* s! u# i0 l: t2 e( ]Protein–Protein Interaction (Interactome) Network: Technical Aspects 118
5 C. W: _6 F3 @/ }# r' U7 w8.4 Visualization and Topology of Protein–Protein Interaction Networks 121
t( d0 Q$ G4 b6 X8.5 Cross-Talk Between the C. elegans Interactome and other Large-Scale, m: L: L, Y, o! y( ^5 W
Genomics and Post-Genomics Data Sets 1239 p) _* m8 h; Q O
8.6 Conclusion: From Interactions to Therapies 1291 @3 b* Z) g2 E" u' y) F
References 130
' k+ ~+ ~5 [0 Q: N+ u1 ISECTION III INTEGRATIVE DATA MINING AND+ A7 D3 k7 T8 ?, M. T
VISUALIZATION – EMPHASIS ON6 j6 }. g- f% a+ X2 q% a
COMBINATION OF MULTIPLE; E% Z7 z3 H4 i. w+ V; F7 d
PREDICTION MODELS AND METHODS 135! N* V/ S* B1 @" ^; j, E
9 Integrated Approaches for Bioinformatic Data Analysis c6 u2 F2 m7 o
and Visualization – Challenges, Opportunities0 A! a; _. ^ _6 N. C* w* F
and New Solutions 137" P4 z# m3 ]& I- ?6 w2 W, h4 }( ]. n
Steve R. Pettifer, James R. Sinnott and Teresa K. Attwood5 x) I) y, m; H; j% ~$ b$ ^
9.1 Introduction 137
7 q; i, K( i& o- o9.2 Sequence Analysis Methods and Databases 139% }# B' Q, u6 Y" F3 W8 m
9.3 A View Through a Portal 1411 n4 X+ V% S/ u2 M: a$ I
9.4 Problems with Monolithic Approaches: One Size Does Not Fit All 142/ e: W: Y- m0 E
9.5 A Toolkit View 143- C- ]: n3 c4 n2 w3 Q; c9 \
9.6 Challenges and Opportunities 1452 q3 J3 K- E O: S
9.7 Extending the Desktop Metaphor 1477 i3 |: v" ~& G6 {8 h
9.8 Conclusions 151
, N6 c% k+ p% Z4 v8 ?Acknowledgements 1517 F0 K# Z5 R6 M q; O
References 152
# R. ?& O) n$ S5 W* O3 x9 @10 Advances in Cluster Analysis of Microarray Data 153! R( J" s' p4 d& R0 l7 R3 A
Qizheng Sheng, Yves Moreau, Frank De Smet, Kathleen Marchal
2 o5 }6 o' |+ `- a* V2 t! o+ Tand Bart De Moor2 N$ B* s) K8 ~5 r" ~
10.1 Introduction 153* i4 Z8 D$ S- l; o& J
10.2 Some Preliminaries 1550 ?- ?6 _* K) g! L1 k: v
10.3 Hierarchical Clustering 157
) {& {) z9 g( q; G3 m8 C) x10.4 k-Means Clustering 159) W4 y. t7 Q4 e1 x2 p
10.5 Self-Organizing Maps 159
5 f) ~# I* q# ~& B) l10.6 A Wish List for Clustering Algorithms 160
9 t" N" O/ V4 m0 [# K- M5 \: @8 S10.7 The Self-Organizing Tree Algorithm 161
) ]/ X9 _" j1 x0 v# f10.8 Quality-Based Clustering Algorithms 162
, K# L$ D) g0 N! A F6 {, T10.9 Mixture Models 163+ q* w9 x8 a; H1 U" G9 {
10.10 Biclustering Algorithms 166
5 G" R* r$ J# E10.11 Assessing Cluster Quality 168
) Q% B, w( ^, |& m, F10.12 Open Horizons 170
) c7 }/ g. Q* A0 p: SReferences 1710 A* |2 X; W/ E, W( R6 H
11 Unsupervised Machine Learning to Support Functional$ f! R6 h* |6 K* {
Characterization of Genes: Emphasis on Cluster
) Y& y, ^) ~7 L0 E$ v! b) NDescription and Class Discovery 175
2 \+ d5 Q( i+ W P. { b% `, _Olga G. Troyanskaya! ]# s v0 e- K1 W1 i1 i5 z+ U! Q& y) {
11.1 Functional Genomics: Goals and Data Sources 175
' L. K( u1 ]! J# q& l: Z1 K11.2 Functional Annotation by Unsupervised Analysis of Gene m6 ^$ I1 U' } f7 e! C% p1 G+ [! _
Expression Microarray Data 177
( P1 N8 S4 u% \8 \11.3 Integration of Diverse Functional Data For Accurate Gene Function
l8 }% o4 R N/ L$ L& t, |Prediction 179) h5 y" n* I- {% B
11.4 MAGIC – General Probabilistic Integration of Diverse Genomic Data 180
; K7 C4 R4 q5 m0 A' g& s+ |1 H8 t3 T11.5 Conclusion 188/ |8 q, g) M( D% a& |) C
References 189
3 R3 N' z# `# Y: X& q3 V12 Supervised Methods with Genomic Data: a Review, y0 M/ M* C2 P1 L& p
and Cautionary View 193
4 y% c n2 F; u. RRamo´n Dı´az-Uriarte
4 |8 Q V$ z/ u12.1 Chapter Objectives 193. l4 [+ P3 z! V9 z; K) y
12.2 Class Prediction and Class Comparison 1944 t0 v# N) N/ _* n1 Z5 \' J- |
12.3 Class Comparison: Finding/Ranking Differentially Expressed Genes 194& _( x! X* S* A. u9 f+ q4 \, P
12.4 Class Prediction and Prognostic Prediction 1986 Q; c$ E* o( t8 D( J% `7 N
12.5 ROC Curves for Evaluating Predictors and Differential Expression 201
# `5 Z" f; x( k2 z# }2 O6 g0 o! L12.6 Caveats and Admonitions 203
; H8 a0 [8 n+ o7 Q$ a! b) O+ u; p3 ~+ s12.7 Final Note: Source Code Should be Available 209: |! d$ a1 s' s' z& W2 P4 c; M6 `
Acknowledgements 2102 d( [: D# r+ P7 b
References 210
3 u9 q; J) y9 `# i: X; Z5 ?13 A Guide to the Literature on Inferring Genetic Networks
) J2 e6 G+ Y# @) [" g2 m/ ?by Probabilistic Graphical Models 215
2 `+ w5 @0 I6 G! B8 v# {. VPedro Larran˜aga, In˜aki Inza and Jose L. Flores
, G* h# d1 f: G13.1 Introduction 215
5 _3 r) w% q( E! |# k13.2 Genetic Networks 216& w( i6 d; ~, U2 k( _+ [0 t* J+ f
13.3 Probabilistic Graphical Models 218
- x) O3 f# M9 h$ u13.4 Inferring Genetic Networks by Means of Probabilistic Graphical Models 229( {4 o5 I( P0 z
13.5 Conclusions 234% P' w& P8 f; R# W( M Q6 f
Acknowledgements 235
1 ?. w5 V! w; j7 MReferences 235
# U6 @8 K4 E \# b8 w- n0 M1 N0 o14 Integrative Models for the Prediction and Understanding7 R/ l, Q- ^8 h+ N# x/ R
of Protein Structure Patterns 239) O8 M* m, r9 m: A( e
Inge Jonassen; l5 y3 T0 V( p9 _
14.1 Introduction 239) g. _! q/ g2 u1 b: M
14.2 Structure Prediction 241: e3 g+ e- M/ w
14.3 Classifications of Structures 244
" J! x, b8 k- @0 w u ^2 s: v1 Q14.4 Comparing Protein Structures 246/ I1 F( u& n9 ?& d- s% x$ K
14.5 Methods for the Discovery of Structure Motifs 249# _; r4 x8 W( R* s
14.6 Discussion and Conclusions 252; \1 Q4 J, W a6 o( E1 u- V' `
References 254& ^: g) {/ C3 N1 C+ P
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发表于 2010-9-19 18:29
