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# x, u- s Z; `9 P4 s2 C; f0 hSECTION I INTRODUCTION – DATA DIVERSITY AND INTEGRATION 1" I7 @ d9 _' P. l: c; ~4 E W9 U
1 Integrative Data Analysis and Visualization: Introduction
, P+ c, Y8 \' I7 F& W$ Vto Critical Problems, Goals and Challenges 38 A, G7 M5 R0 o
Francisco Azuaje and Joaquı´n Dopazo
% B) B( W' b( S2 `1.1 Data Analysis and Visualization: An Integrative Approach 3# Y2 |- A; u3 {
1.2 Critical Design and Implementation Factors 5
3 q8 W5 m ?- d" K1 z7 q3 ^1.3 Overview of Contributions 82 {1 G- U* [4 @8 H/ P
References 9- I! k* a( P6 k5 Z4 Z
2 Biological Databases: Infrastructure, Content
# E# E$ H, a5 d0 l# band Integration 11 R k1 c7 D6 W7 S, Z( y: Y: b
Allyson L. Williams, Paul J. Kersey, Manuela Pruess
5 o* O" w8 d5 W0 ~and Rolf Apweiler6 O5 j* E( M1 {' Y( D
2.1 Introduction 11
# L$ i& q3 K, o- B4 @, a2.2 Data Integration 12
1 H W; \% D% Y3 a' h2.3 Review of Molecular Biology Databases 17' h' ?+ T \& P8 r
2.4 Conclusion 23, ], O* N! h) ?0 b0 S: j: E X
References 26
' L9 k/ b! ?/ ?/ }, W3 Data and Predictive Model Integration: an Overview0 _1 Q1 }' z/ Y, v
of Key Concepts, Problems and Solutions 29
$ s/ Y6 y: I! KFrancisco Azuaje, Joaquı´n Dopazo and Haiying Wang5 m8 O, c1 e! j/ A: {+ j
3.1 Integrative Data Analysis and Visualization: Motivation and Approaches 29) I( J2 _3 ~# Y9 ?' W C
3.2 Integrating Informational Views and Complexity for Understanding Function 31 Y( O1 h! ?9 H' X- q) m
3.3 Integrating Data Analysis Techniques for Supporting Functional
! K4 Y8 E6 U# o1 j8 q% C' L$ gSECTION II INTEGRATIVE DATA MINING AND VISUALIZATION –1 C) Q; i n* T2 U
EMPHASIS ON COMBINATION OF MULTIPLE
H4 N1 P" I. |' G) K/ |. aDATA TYPES 41# g5 M) ?# |9 v1 Y( d6 _
4 Applications of Text Mining in Molecular Biology, from Name0 w7 O5 [+ t' i3 m0 K
Recognition to Protein Interaction Maps 43
2 ^# f. u8 B8 LMartin Krallinger and Alfonso Valencia6 Y1 V4 E3 f! m$ ?: X
4.1 Introduction 44
+ f( g d- [" N7 q/ v$ Q4.2 Introduction to Text Mining and NLP 45
* [; ~% N; H8 j* A& W; t3 k: v4.3 Databases and Resources for Biomedical Text Mining 47
0 N, b8 M0 F& T% {' ]4.4 Text Mining and Protein–Protein Interactions 50
6 N# W+ t7 D6 W6 ~3 x2 J2 Y5 k/ k4.5 Other Text-Mining Applications in Genomics 55; ?! q, i. _( Q7 b) d" p! b5 Y/ b& y( s! L
4.6 The Future of NLP in Biomedicine 56
4 R9 V8 Q7 Q' XAcknowledgements 56! {+ S! |1 j0 D' U. `1 q1 {* ?, p
References 56* u; G) |3 `8 j9 @2 C+ A
5 Protein Interaction Prediction by Integrating Genomic
6 O: m' w+ Z0 N# LFeatures and Protein Interaction Network Analysis 61
; W6 G K6 Q7 [4 v2 C% uLong J. Lu, Yu Xia, Haiyuan Yu, Alexander Rives, Haoxin Lu,9 ? u; l- { A
Falk Schubert and Mark Gerstein
, E- T: Y3 r, q. b% M1 @: O5.1 Introduction 62
( q/ N2 r/ B3 R% X/ q5.2 Genomic Features in Protein Interaction Predictions 63, X v9 C, n; R) ^& w
5.3 Machine Learning on Protein–Protein Interactions 67
2 }) l( R1 _2 D' m7 P5.4 The Missing Value Problem 731 ?% m, Q" C3 s9 m
5.5 Network Analysis of Protein Interactions 75
9 c- U$ o: T$ i1 o9 ~5.6 Discussion 79
. m; ^9 h2 f" v1 ?! E) i3 e) {References 80
* v3 e! F& I" P, I. S3 \6 Integration of Genomic and Phenotypic Data 83
- H$ C& Z- L3 I1 N0 WAmanda Clare
1 }1 [0 E7 v6 C7 B3 w7 I+ a6.1 Phenotype 83
1 f5 K' Y( F. L/ [ J- L/ `6.2 Forward Genetics and QTL Analysis 85
7 _9 n6 |, |* L% Y, C6.3 Reverse Genetics 87
1 h2 t7 Y7 H" |7 K: m6 e6.4 Prediction of Phenotype from Other Sources of Data 88
0 P) u1 Q2 T: c& ?* ?5 ]6.5 Integrating Phenotype Data with Systems Biology 90" y9 c7 Q( k/ q6 r$ M8 [
6.6 Integration of Phenotype Data in Databases 93
5 m( I3 p5 a( [3 L6.7 Conclusions 95
" b: ]+ ^$ ~+ I& h0 {9 q" LReferences 951 b8 _0 Y. L5 i9 o! H
7 Ontologies and Functional Genomics 99
/ Y ~) J+ a# Z+ OFa´tima Al-Shahrour and Joaquı´n Dopazo( s, V, ^( M# G* |
7.1 Information Mining in Genome-Wide Functional Analysis 99; o, ?8 }* N6 J* T" N+ h
7.2 Sources of Information: Free Text Versus Curated Repositories 100" B& i$ K3 F2 J# M, H! p9 o, J$ T8 [0 ^
7.3 Bio-Ontologies and the Gene Ontology in Functional Genomics 101. K* A0 l9 C; |9 t$ f: y
7.4 Using GO to Translate the Results of Functional Genomic Experiments into
3 J- E3 N* V/ V- ]5 U. ]: ?4 lBiological Knowledge 1038 p% V% Z6 ?: M; J
7.5 Statistical Approaches to Test Significant Biological Differences 1044 j0 N1 x1 z( R# N! T5 H" q; P
7.6 Using FatiGO to Find Significant Functional Associations/ _6 F+ C+ ?' H- W
in Clusters of Genes 1060 j$ p( r6 S3 n: Q; W* F3 X
7.7 Other Tools 107* l8 r6 L3 E$ A3 l! M" a
7.8 Examples of Functional Analysis of Clusters of Genes 108
* p7 @! T. J: i- v3 O7.9 Future Prospects 110
+ q/ r; e' v1 e8 g" X# n, a. lReferences 110% G3 n- u$ F# M) h6 {, [
8 The C. elegans Interactome: its Generation and Visualization 113; K2 c$ o2 g% k2 s" v
Alban Chesnau and Claude Sardet- P% r/ E. _2 k- @0 Y3 n' P" ^
8.1 Introduction 113
( M2 g4 H" J8 @) l0 C! n: E8.2 The ORFeome: the first step toward the interactome of C. elegans 116
8 K; N8 @3 \( L, @# F9 \8.3 Large-Scale High-Throughput Yeast Two-Hybrid Screens to Map the C. elegans
6 T' `7 a$ d; y" D& @- V U7 o, ~Protein–Protein Interaction (Interactome) Network: Technical Aspects 118
0 u0 A* _% b& o" o4 a) D8.4 Visualization and Topology of Protein–Protein Interaction Networks 1219 e2 v& ?3 @: T6 I
8.5 Cross-Talk Between the C. elegans Interactome and other Large-Scale9 V4 |5 y- [. J! B: a$ H
Genomics and Post-Genomics Data Sets 123$ @) { _5 a0 z! a& O' p B0 b
8.6 Conclusion: From Interactions to Therapies 129
2 a& P0 Z& s( m5 Z/ B( DReferences 1303 \, m7 N5 J' ?8 P6 Z* b! b
SECTION III INTEGRATIVE DATA MINING AND: f0 o2 D# x* ]" C6 l
VISUALIZATION – EMPHASIS ON
1 E" Q" Y1 w! I# t, ]COMBINATION OF MULTIPLE
% P) |; Z# O9 a+ P& I5 S |( L ]PREDICTION MODELS AND METHODS 135$ D+ c) W. s& ^$ S" {: O
9 Integrated Approaches for Bioinformatic Data Analysis! N, R# T/ F2 v5 l u! |6 G, w1 g
and Visualization – Challenges, Opportunities0 Y; m2 p4 X% }, u6 K) j
and New Solutions 137
, L7 j9 C' a n, Z0 I) N' dSteve R. Pettifer, James R. Sinnott and Teresa K. Attwood
# U, |2 a6 W5 p9.1 Introduction 137
( ?, e+ D- J; [ m1 r' f2 Q% ~4 Q7 s9.2 Sequence Analysis Methods and Databases 139
. \ Q" s" t) u8 A9 Y# ]9.3 A View Through a Portal 141
$ ^5 a0 h1 @8 e1 C) e/ z% o9.4 Problems with Monolithic Approaches: One Size Does Not Fit All 142
- q5 p7 `+ `4 c" i9.5 A Toolkit View 143
2 H) J, M3 _. Y3 ~( b* @9.6 Challenges and Opportunities 145
1 t v. R1 \( n1 ^9.7 Extending the Desktop Metaphor 1475 P0 e: {. K3 s5 F1 }0 O C
9.8 Conclusions 151
& e$ j" E) o# F& |+ ^, R* UAcknowledgements 1519 @; Z" e, }: {- b% Q7 M
References 152
1 u; r; T) a- x$ z1 x, A; ~10 Advances in Cluster Analysis of Microarray Data 153
k* ~% V' x$ |% h6 F- {2 j" AQizheng Sheng, Yves Moreau, Frank De Smet, Kathleen Marchal
9 i; e+ ?) D) u! v% ~and Bart De Moor
9 r" @# i9 G4 j; x10.1 Introduction 153) G4 E" c& I1 C* W! l
10.2 Some Preliminaries 1558 Y1 v |" B# {+ ?: {& k( f6 @7 X; @
10.3 Hierarchical Clustering 1576 Z) Q; N/ Y! r' r; W& V k" H! d
10.4 k-Means Clustering 159
1 v; j9 }" A4 ]7 e0 m; j10.5 Self-Organizing Maps 159
. o J; w+ W. \10.6 A Wish List for Clustering Algorithms 160
1 `! r% ~( F4 X9 [1 p+ i5 ^10.7 The Self-Organizing Tree Algorithm 161
7 N/ m) z5 F! ^10.8 Quality-Based Clustering Algorithms 162$ |. Y/ ]& a# I0 |; z6 r
10.9 Mixture Models 163, Z+ r) @# y5 V
10.10 Biclustering Algorithms 1665 R1 v* s9 {( q% X
10.11 Assessing Cluster Quality 168
' V, Y# k: k8 p! `8 Q4 k3 }4 _( E10.12 Open Horizons 170
- y$ d( S+ |0 g8 f* [, SReferences 171
$ \' V& C2 h2 ~11 Unsupervised Machine Learning to Support Functional' J5 y) \) L5 f% l& ~+ M8 w k; o& K
Characterization of Genes: Emphasis on Cluster+ M& |! C' \2 f. Q5 ?# ^) W
Description and Class Discovery 175
/ N/ I5 o3 V- O: N/ X' X+ Y* R3 J) MOlga G. Troyanskaya, ^# |4 x1 _0 d, e
11.1 Functional Genomics: Goals and Data Sources 175, I2 n- G' `; s# r& O
11.2 Functional Annotation by Unsupervised Analysis of Gene
7 i9 c' m }, a8 u& E/ tExpression Microarray Data 177
6 x% e4 e' r+ \5 H' K8 j11.3 Integration of Diverse Functional Data For Accurate Gene Function" {6 m8 L/ r( x+ W
Prediction 179
& t2 P: ?$ o# l8 ~: e z# {( `11.4 MAGIC – General Probabilistic Integration of Diverse Genomic Data 180& s' P( x, U7 h! O! x" o
11.5 Conclusion 188
, R3 g/ D: B8 C: z8 Z9 \7 d2 I, s# }References 189
# |/ l, S0 H5 L) t12 Supervised Methods with Genomic Data: a Review" I- S7 N/ [# P
and Cautionary View 193: J) p& u( z ~. A) T1 p6 J
Ramo´n Dı´az-Uriarte
+ p6 R% B$ A1 O1 P1 o12.1 Chapter Objectives 1936 \' S. Z; Y& z3 u" {
12.2 Class Prediction and Class Comparison 194
. E8 }( A" u5 U+ i12.3 Class Comparison: Finding/Ranking Differentially Expressed Genes 194
0 S/ d+ I- G R$ f12.4 Class Prediction and Prognostic Prediction 198( }3 X: @2 F, y6 v1 G
12.5 ROC Curves for Evaluating Predictors and Differential Expression 201 I0 t7 v4 n5 E6 l, ?
12.6 Caveats and Admonitions 203/ c# j. v h6 \% r/ g" v; z' A
12.7 Final Note: Source Code Should be Available 209 `% j# Y1 q& H0 |9 @' B" F
Acknowledgements 210' K- Q6 m0 ]# P8 J5 S* W
References 2100 I% `5 J" L- N! ?% a
13 A Guide to the Literature on Inferring Genetic Networks. _3 ?( k4 P* l. f3 E% h
by Probabilistic Graphical Models 2158 M& |- j- x$ Q& N: ` _: w
Pedro Larran˜aga, In˜aki Inza and Jose L. Flores# M3 f; t& g$ _8 Y
13.1 Introduction 2155 J4 ~* p' h. d- g5 H& d3 s
13.2 Genetic Networks 216
: X' C# Y m i5 z13.3 Probabilistic Graphical Models 2188 e: F! K, F1 {4 c4 F9 W
13.4 Inferring Genetic Networks by Means of Probabilistic Graphical Models 2295 }/ `9 W; V8 \# o
13.5 Conclusions 234
& A6 n5 C; \' s2 S2 J0 W5 {Acknowledgements 235
; ]/ |9 D0 y! f: JReferences 235
! B* |# Q4 H( ^3 M* o14 Integrative Models for the Prediction and Understanding
) q; t4 ` b8 ?$ M5 e% L4 hof Protein Structure Patterns 239# J( s. t0 \2 D' X
Inge Jonassen+ ~: |: M. c; k D* A
14.1 Introduction 2395 C8 Q6 v; x, S, J. @% a
14.2 Structure Prediction 241
# e' ]* j, p& J) }9 w14.3 Classifications of Structures 2441 i% T( r1 U& Z' O& Q1 f
14.4 Comparing Protein Structures 2469 H$ G& ~% K+ O! v- Q
14.5 Methods for the Discovery of Structure Motifs 249
' H* h) e3 f, {0 z2 d" }14.6 Discussion and Conclusions 252( L7 V, }! v- B4 C
References 2549 J# R% O( K) m; V- H
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发表于 2010-9-19 18:29
