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本帖最后由 细胞海洋 于 2010-9-19 20:20 编辑 + G4 w6 q4 k7 d( V( C/ v
9 @$ X3 N$ h2 FSECTION I INTRODUCTION – DATA DIVERSITY AND INTEGRATION 1
/ j; p' n% ?; | Q4 r$ P1 Integrative Data Analysis and Visualization: Introduction: c2 D1 v& f! t! @6 R
to Critical Problems, Goals and Challenges 3$ h3 b, G( I9 X
Francisco Azuaje and Joaquı´n Dopazo
" ~3 @; i+ U9 q# ~2 Y1.1 Data Analysis and Visualization: An Integrative Approach 31 R% m& E( f% i4 g5 A# u) |: P1 W
1.2 Critical Design and Implementation Factors 5
4 p( o2 i; |& f H1.3 Overview of Contributions 8 ?2 o2 U) C" V" y
References 9
9 V$ O' `) j4 [5 F2 Biological Databases: Infrastructure, Content
% d2 y; Y) _4 y; O7 Iand Integration 11
4 P4 ?$ C9 `# E5 o; H/ GAllyson L. Williams, Paul J. Kersey, Manuela Pruess) u9 \9 g4 Q0 }+ K- \5 w
and Rolf Apweiler
! W) U: N( x6 ?, x+ _8 X0 h2.1 Introduction 11, }, [4 h8 }& j7 P3 B% Y
2.2 Data Integration 12. t/ J* a2 r9 j: V. k
2.3 Review of Molecular Biology Databases 17
! V7 u2 h) w9 ^, ?7 K: {! Q2.4 Conclusion 23
' V; P) B7 V: u9 {0 @" n+ nReferences 26
" \ M- G# j9 @# m4 y7 C3 Data and Predictive Model Integration: an Overview
; V" f. m2 B2 X# Oof Key Concepts, Problems and Solutions 29
4 E' ~7 b, Q& \& V! \Francisco Azuaje, Joaquı´n Dopazo and Haiying Wang, m5 [* @) l! D' L! {) e6 C' f" t
3.1 Integrative Data Analysis and Visualization: Motivation and Approaches 29
% U* I, }4 e% v* N% f3.2 Integrating Informational Views and Complexity for Understanding Function 31) _1 R; ^3 D: L" h; M! G
3.3 Integrating Data Analysis Techniques for Supporting Functional
8 X6 ]% u5 E* [SECTION II INTEGRATIVE DATA MINING AND VISUALIZATION –/ `/ G+ f- i S
EMPHASIS ON COMBINATION OF MULTIPLE% K. z! j1 G( Z: t4 [* J1 N
DATA TYPES 41+ g* z9 n5 M6 f; l' o
4 Applications of Text Mining in Molecular Biology, from Name; Y( ]7 J4 z) B3 G3 ^% ^
Recognition to Protein Interaction Maps 43! Q; W f- U1 I/ d
Martin Krallinger and Alfonso Valencia
; f! f; ]% n. ]. @5 r3 _8 W4.1 Introduction 44; N; A9 t. I0 w# V5 H* c+ k
4.2 Introduction to Text Mining and NLP 45( ?1 M/ @9 Y2 Q! @
4.3 Databases and Resources for Biomedical Text Mining 47
( g1 n l1 m1 e- `( e# q' x4.4 Text Mining and Protein–Protein Interactions 50
R6 e7 c. I7 ]4.5 Other Text-Mining Applications in Genomics 55, _% U" E( v8 w% \
4.6 The Future of NLP in Biomedicine 566 r# s: m$ U- ^5 O* U
Acknowledgements 56/ ~" C3 `8 G! V. Q7 Z
References 56% N, {/ A' D% p7 m) }3 ?9 M
5 Protein Interaction Prediction by Integrating Genomic
2 T; {+ m: P0 S0 W; Q3 z2 \Features and Protein Interaction Network Analysis 61
* E- k* Z: ]- H& y3 q* |7 x; U+ zLong J. Lu, Yu Xia, Haiyuan Yu, Alexander Rives, Haoxin Lu,% E; \' L; h: F+ S) ^
Falk Schubert and Mark Gerstein
$ i) L) v+ J+ i; a. T5.1 Introduction 62
s; S2 Y3 K# C ^7 I& q5.2 Genomic Features in Protein Interaction Predictions 63
/ K! X9 ?( J6 ^6 K% ^; A. n8 T8 C5.3 Machine Learning on Protein–Protein Interactions 67
% ]# j! M! n# Y- d$ j5.4 The Missing Value Problem 73
% s7 \" ^5 g* c2 \" E, M; w5.5 Network Analysis of Protein Interactions 75% t, _3 z+ r4 p1 t+ `) L3 @! W* S6 N
5.6 Discussion 79
6 D4 t2 \3 M$ e; W& a0 M; ZReferences 80
1 g0 G; p N; `( O: K6 Integration of Genomic and Phenotypic Data 83
3 r2 h2 D7 M9 y; g* [1 \3 X: aAmanda Clare+ Q5 A: ] ` l. C
6.1 Phenotype 83
$ C1 b! d/ t5 v6.2 Forward Genetics and QTL Analysis 85- e& j# P9 l" ~& H8 w
6.3 Reverse Genetics 87
( J4 g: ~4 Z4 F; K8 P% v! s6.4 Prediction of Phenotype from Other Sources of Data 880 |# b/ ^9 z2 P/ b% |6 Q; b
6.5 Integrating Phenotype Data with Systems Biology 90
( H. a( _9 Y2 g& Q, b' O' c! m6.6 Integration of Phenotype Data in Databases 93
% ?4 D! \ l9 O+ p/ a6.7 Conclusions 958 d6 O$ D1 y0 D9 l
References 95
9 A+ E$ V3 Q5 P" e# }7 Ontologies and Functional Genomics 992 Y( x. X3 a6 O, E
Fa´tima Al-Shahrour and Joaquı´n Dopazo* k0 f& _2 Y% y9 j* d- Z$ K8 V
7.1 Information Mining in Genome-Wide Functional Analysis 99
; `; j5 o0 N1 O6 Q7 ^3 t7.2 Sources of Information: Free Text Versus Curated Repositories 1007 x$ U; m0 h' C9 ?& M
7.3 Bio-Ontologies and the Gene Ontology in Functional Genomics 101
; a _ D8 _* D7 I7 Y0 o7.4 Using GO to Translate the Results of Functional Genomic Experiments into; o! b7 l8 O5 Q& D+ M
Biological Knowledge 103
5 i% ~3 i4 p R2 u, O1 G* `4 j7.5 Statistical Approaches to Test Significant Biological Differences 104
; M- E# j6 S7 @7 Y& e! J7.6 Using FatiGO to Find Significant Functional Associations
& T; J3 Q. F1 B/ Zin Clusters of Genes 106) n; Q' _5 q& w) N( ^# n; _. O! N
7.7 Other Tools 107% j4 F) i% U7 b" H! j; k, R# B
7.8 Examples of Functional Analysis of Clusters of Genes 108( z+ T, D# g) l( T9 b
7.9 Future Prospects 1106 B: N3 x7 y5 D/ q2 M: W: \
References 110
) ]% z- V8 y e# v7 ]8 The C. elegans Interactome: its Generation and Visualization 113
' `8 n7 V& d3 VAlban Chesnau and Claude Sardet
4 T8 I2 p) z7 I2 I8.1 Introduction 113
. y% i5 Q3 ]1 W: m; T8.2 The ORFeome: the first step toward the interactome of C. elegans 1164 p4 u% [/ N. x
8.3 Large-Scale High-Throughput Yeast Two-Hybrid Screens to Map the C. elegans4 D3 w5 b6 V& `. Z$ T; r6 k6 @
Protein–Protein Interaction (Interactome) Network: Technical Aspects 118
9 e4 F6 B! B+ }/ l8.4 Visualization and Topology of Protein–Protein Interaction Networks 121
# K* v$ U& T% ]8.5 Cross-Talk Between the C. elegans Interactome and other Large-Scale* B$ N! \6 l( y8 L' A
Genomics and Post-Genomics Data Sets 123* F9 G% `, |2 u6 b' [
8.6 Conclusion: From Interactions to Therapies 129" o/ G+ o, e m" v R+ H5 G
References 1307 ?8 N6 @5 r9 o p' Q1 b
SECTION III INTEGRATIVE DATA MINING AND
6 k6 ?' H! ]+ t$ b+ Q- M* UVISUALIZATION – EMPHASIS ON5 _% G7 I( b4 r1 H+ _
COMBINATION OF MULTIPLE
j, W# h- j8 P6 Y9 ~" IPREDICTION MODELS AND METHODS 135, s/ K" C3 H, X8 w2 z! Z0 t
9 Integrated Approaches for Bioinformatic Data Analysis
, u8 m7 J( Z+ R) `; g. V' F; g. Iand Visualization – Challenges, Opportunities! K7 t& D0 n7 w* M& H
and New Solutions 137
& `3 `- k: X1 `) m$ R/ a) C( @, vSteve R. Pettifer, James R. Sinnott and Teresa K. Attwood
: b5 l3 h/ |( R4 ?8 U6 c, @0 q9.1 Introduction 137
2 ~$ W. p+ g6 o- ?+ z! X9.2 Sequence Analysis Methods and Databases 139
6 w& w. b4 S- I0 {3 F8 @9 Y2 m9.3 A View Through a Portal 1419 _& x' ?5 A1 z9 e+ e- J( ^
9.4 Problems with Monolithic Approaches: One Size Does Not Fit All 142
' c; p& N$ }( D# v9 c4 Q9.5 A Toolkit View 143
) e- c1 S# q$ q2 z: ~# ]; W9 f9.6 Challenges and Opportunities 1454 s: y3 m0 F' g( |
9.7 Extending the Desktop Metaphor 147" N8 `9 |# m! T, D
9.8 Conclusions 151! R5 h# E% p `% P
Acknowledgements 151- s9 F+ f5 ^* S2 Y" y+ a6 @
References 152 K' |; j/ i4 U! [3 f
10 Advances in Cluster Analysis of Microarray Data 153
" \) ?* X( A$ ]# d4 W/ \) ~" N4 UQizheng Sheng, Yves Moreau, Frank De Smet, Kathleen Marchal% n& [ c+ L9 p* x) Z: {
and Bart De Moor2 l# [/ ~: \# b% j% a
10.1 Introduction 153# T: [1 U4 @" z/ f% K! g
10.2 Some Preliminaries 155" H5 p( w6 L5 S1 H: U' L% f8 @8 u- w
10.3 Hierarchical Clustering 1575 G) r0 Z6 _' T4 r: e/ g; R1 V4 u
10.4 k-Means Clustering 159+ l9 t8 N6 e7 U e3 ]6 A
10.5 Self-Organizing Maps 159# q9 a( A4 M- v9 L' O
10.6 A Wish List for Clustering Algorithms 160
$ `" K( v+ J' |- m! r) H, s$ _) c1 e10.7 The Self-Organizing Tree Algorithm 161
) I, M# P1 f& T10.8 Quality-Based Clustering Algorithms 162/ y6 S. {8 e- o) p
10.9 Mixture Models 1632 A" I! x6 ?8 r5 o5 g; Y
10.10 Biclustering Algorithms 166 [' c8 i x8 @5 n) g
10.11 Assessing Cluster Quality 168
; |* l( | r8 e* S10.12 Open Horizons 170
$ Y9 g, x9 l' f+ {2 C/ F- \8 ?References 171( e; X9 I: U. Y
11 Unsupervised Machine Learning to Support Functional
4 }3 x8 f! _; {, k# P! i' S5 QCharacterization of Genes: Emphasis on Cluster
! |! K: _" p/ C" RDescription and Class Discovery 1759 {; i' _4 ]: F4 c- Y
Olga G. Troyanskaya
& B) x3 u, U- a ~) {11.1 Functional Genomics: Goals and Data Sources 175
A2 G& r# D# b2 _11.2 Functional Annotation by Unsupervised Analysis of Gene
9 _' D- a' W( lExpression Microarray Data 177" t; y. v' _# O5 b) j1 h
11.3 Integration of Diverse Functional Data For Accurate Gene Function
. L% H; _0 ?/ L* qPrediction 179
( [, f4 `# z: |$ O9 M11.4 MAGIC – General Probabilistic Integration of Diverse Genomic Data 180
) Z0 Q& k- w* s7 ?11.5 Conclusion 188
0 u% ?5 L }& M" Z8 k! A) MReferences 189; ?' ]5 l8 f4 d. L% {
12 Supervised Methods with Genomic Data: a Review
1 @: ^8 ^- k6 O7 R* `1 N& X# D' H' ^and Cautionary View 193/ \7 s+ R) n, d" n$ W" |% x, F; F
Ramo´n Dı´az-Uriarte
0 \% v7 ?* l5 j8 C! e: B) E12.1 Chapter Objectives 193
+ V) y- t' `! Z) F12.2 Class Prediction and Class Comparison 194
$ l% x. U- G2 W% F6 o+ \12.3 Class Comparison: Finding/Ranking Differentially Expressed Genes 194 Z% u) n# \$ d8 |% [
12.4 Class Prediction and Prognostic Prediction 198" M' @* _0 ]: ]0 b
12.5 ROC Curves for Evaluating Predictors and Differential Expression 201
* u7 x) ?8 i6 g Q12.6 Caveats and Admonitions 203
) b8 I2 _* A( o" ?1 T9 m12.7 Final Note: Source Code Should be Available 2095 q" \ t% J6 u" p
Acknowledgements 210! w3 F6 m$ }' ]2 Y3 c5 y# K! f" P- t
References 2101 U1 {9 J% c4 t Q+ X! Z8 M$ l0 z
13 A Guide to the Literature on Inferring Genetic Networks
5 u& R" l4 @5 U Y& sby Probabilistic Graphical Models 2159 s' `9 ]" p, _* h
Pedro Larran˜aga, In˜aki Inza and Jose L. Flores2 J. M, S+ g3 S& S3 ?8 ^& w
13.1 Introduction 2151 F7 j- Q) r2 c
13.2 Genetic Networks 216
, @+ O5 A. b! z5 H1 o13.3 Probabilistic Graphical Models 218/ q! x: Z' W* j7 F X, ^* F
13.4 Inferring Genetic Networks by Means of Probabilistic Graphical Models 229& g! Z( Z# v8 P+ w
13.5 Conclusions 234
$ @1 \* \9 r9 P8 T& V3 s/ J( vAcknowledgements 2354 G2 x1 `9 t, e. R1 p! }# p+ J
References 2358 B& D- j% D' x S" N5 l9 @
14 Integrative Models for the Prediction and Understanding
, U8 l* Q' g( Y. X* Y/ v. U# lof Protein Structure Patterns 239
k6 F: A, \+ ?- C. qInge Jonassen& [4 c5 `$ U9 t* Y
14.1 Introduction 239
; G1 H# s$ |: R8 `14.2 Structure Prediction 241/ v! r, _+ u# ?) a, |* h
14.3 Classifications of Structures 244
3 c+ C3 F, V# G# Z1 Z L14.4 Comparing Protein Structures 246( \' Z, b3 K5 u: W. }
14.5 Methods for the Discovery of Structure Motifs 2492 H* W, ], k" Y* n! w
14.6 Discussion and Conclusions 252# {2 H) I p4 m4 j
References 254
8 v1 }0 _) E9 n' _: q: @% k. o5 \9 w3 `3 P$ I0 j
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发表于 2010-9-19 18:29
