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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 19 i& q- r6 w& A$ P. O% Z
1 Integrative Data Analysis and Visualization: Introduction
' d, ]; m N4 Z- J- O5 Ito Critical Problems, Goals and Challenges 38 D4 |8 x; Y) ~* E6 A9 V: w4 n7 M6 v
Francisco Azuaje and Joaquı´n Dopazo
- t" l) k( A! q5 U1.1 Data Analysis and Visualization: An Integrative Approach 3
, P2 [# ^* ] m9 K3 s, f8 x1.2 Critical Design and Implementation Factors 5
9 L" b3 l1 x0 |/ r( Y t1.3 Overview of Contributions 8
4 w1 ~* H7 X% W' W. SReferences 9
) K/ b( ^9 M5 x; H+ O: U N/ f2 Biological Databases: Infrastructure, Content. ` S5 E5 H% H. I! r- Q9 E; K- f
and Integration 11
) Y4 s9 R0 L( v% O! T; l% F+ xAllyson L. Williams, Paul J. Kersey, Manuela Pruess
) L. g" a G$ \" Aand Rolf Apweiler8 f: \: i3 d5 P
2.1 Introduction 11
8 g; V. p9 l' t l2.2 Data Integration 12/ E. l# b1 e% `9 o6 G. u8 f
2.3 Review of Molecular Biology Databases 17
. Z1 D! x. b& T) @3 Z+ e1 S2.4 Conclusion 23. e* B6 H% V, K9 {2 }! G7 D
References 26
% F' E/ l. ?' T2 a; q( m, Z! X3 Data and Predictive Model Integration: an Overview( q5 y' G$ b5 S
of Key Concepts, Problems and Solutions 294 I) |8 x/ p1 r
Francisco Azuaje, Joaquı´n Dopazo and Haiying Wang7 [' u' w( n* o; K3 \: y
3.1 Integrative Data Analysis and Visualization: Motivation and Approaches 29
1 x+ A8 a- ]* f1 ^! U3.2 Integrating Informational Views and Complexity for Understanding Function 31
2 ?: E" R" X6 Y; G3.3 Integrating Data Analysis Techniques for Supporting Functional
1 ~, }3 l* T" S8 A! T/ tSECTION II INTEGRATIVE DATA MINING AND VISUALIZATION –
7 v8 L/ I' B, W7 GEMPHASIS ON COMBINATION OF MULTIPLE2 Q- H2 D7 F$ G6 e) T
DATA TYPES 41& y: M' Q( g% \' L+ {+ d3 X( d6 c* V- z
4 Applications of Text Mining in Molecular Biology, from Name" C; n8 n( B# Y3 _
Recognition to Protein Interaction Maps 434 v( Z' t9 v. W$ Z2 G7 e+ H
Martin Krallinger and Alfonso Valencia+ Y' v. s( L a* P3 k
4.1 Introduction 44/ `5 x9 E4 k9 B6 ~/ s. P3 e
4.2 Introduction to Text Mining and NLP 45
- }% T' f3 g. P9 N: h8 ?) ^4.3 Databases and Resources for Biomedical Text Mining 47. Z; O+ h U R& p# j# J" k/ ^7 ^
4.4 Text Mining and Protein–Protein Interactions 50
2 i' X# e" K" ]/ V& ^6 @' s) z4.5 Other Text-Mining Applications in Genomics 55+ T, V; s5 _& A. K! C2 U
4.6 The Future of NLP in Biomedicine 567 ?( B5 R1 Q6 h6 d# w0 s: _1 |
Acknowledgements 56) L' f% a4 }0 r" ~/ Y5 z
References 56
3 K" B* K' q- J) C( _4 P' v( v5 Protein Interaction Prediction by Integrating Genomic7 j% Z5 @3 }0 B3 |" D2 X
Features and Protein Interaction Network Analysis 61
8 V% L; P ]6 Z- V! b- bLong J. Lu, Yu Xia, Haiyuan Yu, Alexander Rives, Haoxin Lu,
J) v. o- O! r# \. l' pFalk Schubert and Mark Gerstein' I% O- R; V" b) m D3 }) }
5.1 Introduction 62& t5 Q$ @. ?- R0 z* |
5.2 Genomic Features in Protein Interaction Predictions 636 |$ }! D2 ]4 m% h
5.3 Machine Learning on Protein–Protein Interactions 678 S- ^# \7 L2 k% f
5.4 The Missing Value Problem 735 }) U! y2 F. |! M% l' a. V& R% [
5.5 Network Analysis of Protein Interactions 75/ T9 N: `8 ?# b2 [9 k' n I
5.6 Discussion 79+ j7 u* C9 d! R4 k
References 80& `( b+ U8 A1 e* n$ ]( A& P
6 Integration of Genomic and Phenotypic Data 834 \+ z! d' z9 D9 ]2 b# m
Amanda Clare
$ j) W5 p' B) k* d6.1 Phenotype 83% A6 N4 t, X2 e" a
6.2 Forward Genetics and QTL Analysis 85& y$ f* y3 O) q$ V5 f
6.3 Reverse Genetics 879 t# D( }2 E5 U8 U0 A
6.4 Prediction of Phenotype from Other Sources of Data 88
. Z1 e) v; x# ]% Z A3 c6.5 Integrating Phenotype Data with Systems Biology 909 A5 W& M4 o& _
6.6 Integration of Phenotype Data in Databases 93% A3 X; a& }4 p/ `! T8 [% ^ {& ^' ^' B
6.7 Conclusions 95
5 z( ?/ P! K# r0 e/ _: \! l fReferences 95% n+ `8 [/ p# w( d% Y/ O" H! W1 Y
7 Ontologies and Functional Genomics 99
; N7 N5 F" X0 K1 h5 yFa´tima Al-Shahrour and Joaquı´n Dopazo
5 A; U/ I- O/ @( x7.1 Information Mining in Genome-Wide Functional Analysis 99- j+ k# `9 Z5 x1 z
7.2 Sources of Information: Free Text Versus Curated Repositories 100
8 V! j$ k7 B( M8 ^) ^% H5 N7.3 Bio-Ontologies and the Gene Ontology in Functional Genomics 101
' P# W2 k/ e% z- y7.4 Using GO to Translate the Results of Functional Genomic Experiments into
6 v1 _2 D% t7 hBiological Knowledge 103& j9 D S5 i/ r! u
7.5 Statistical Approaches to Test Significant Biological Differences 104
" j' t+ Q$ M% A9 H d7.6 Using FatiGO to Find Significant Functional Associations( e4 Z% L. f( [- E3 J4 w
in Clusters of Genes 106
3 X9 T: t! A9 K& o- w7.7 Other Tools 107& X! _; x6 w' V
7.8 Examples of Functional Analysis of Clusters of Genes 1086 W' I1 c. d2 E6 \4 I/ ]
7.9 Future Prospects 110
0 W% D2 ^, ^# U: i: R2 XReferences 1106 R2 k5 D- t& H, [$ O
8 The C. elegans Interactome: its Generation and Visualization 113
# k; J) H) x5 w% [0 YAlban Chesnau and Claude Sardet- d8 H( F1 j2 T: Z6 o4 C
8.1 Introduction 113
2 p9 k4 A/ H ?9 Q C% S! _4 i. p- S8.2 The ORFeome: the first step toward the interactome of C. elegans 116% R% Y& `9 U+ I# _3 h! R4 Z! b( t
8.3 Large-Scale High-Throughput Yeast Two-Hybrid Screens to Map the C. elegans$ k" K, L( @- j( w# E( Q
Protein–Protein Interaction (Interactome) Network: Technical Aspects 118) X& |% h2 ?3 G1 H
8.4 Visualization and Topology of Protein–Protein Interaction Networks 121
# D# j2 k: D$ Z+ X! v# U8.5 Cross-Talk Between the C. elegans Interactome and other Large-Scale
9 p% x8 Y3 |1 }Genomics and Post-Genomics Data Sets 123
1 t8 `( l2 o5 {# G+ Q8.6 Conclusion: From Interactions to Therapies 1293 d. D# Q( `( D# i# d
References 1309 I+ n0 o. [1 C) G% I7 d# e! A0 X
SECTION III INTEGRATIVE DATA MINING AND
# E3 G6 d$ m+ p2 Q/ q9 nVISUALIZATION – EMPHASIS ON
6 \- }" v+ G4 g" z! I/ ~& \COMBINATION OF MULTIPLE
1 h/ i9 Q- t* UPREDICTION MODELS AND METHODS 1353 ?# K% T/ p( z. G1 s: d
9 Integrated Approaches for Bioinformatic Data Analysis
$ ]3 L4 a& M' z4 c- t; tand Visualization – Challenges, Opportunities8 I# E8 J: [5 [3 A
and New Solutions 137/ U- G) }, d/ X4 N3 s
Steve R. Pettifer, James R. Sinnott and Teresa K. Attwood
1 e8 H* v9 j% h; v" x' y/ Z$ i9.1 Introduction 137
, h/ v. [' U7 }& t6 A9.2 Sequence Analysis Methods and Databases 1399 X; w0 K1 w8 s3 `$ Z4 w
9.3 A View Through a Portal 141
' R, h6 D+ V7 ~5 x. i; _* j9.4 Problems with Monolithic Approaches: One Size Does Not Fit All 1420 K/ q5 d" q8 d, K
9.5 A Toolkit View 1433 o( D! {. c! f% Z) B2 M
9.6 Challenges and Opportunities 1450 }! o* t5 i7 i
9.7 Extending the Desktop Metaphor 147
* ^$ H3 L4 m) U r: V9.8 Conclusions 151
' U, |# \* A0 i* }: `* aAcknowledgements 1517 C4 e9 B. m$ A8 C* i' ?
References 152
. ^. U$ t9 I; S! j$ C* d5 `- H10 Advances in Cluster Analysis of Microarray Data 153
6 ^; r, x0 n1 h( I2 d! }2 {Qizheng Sheng, Yves Moreau, Frank De Smet, Kathleen Marchal
7 d% a5 l- S' J" E5 O! H& b% N1 qand Bart De Moor6 d% d+ c; S9 G7 C8 e1 v- E
10.1 Introduction 153
* U2 O& }0 n0 S* d; ~# `' i10.2 Some Preliminaries 155
6 X9 b3 Z2 B9 d# K3 b" w10.3 Hierarchical Clustering 157
+ L, S! E) v* P% R/ J' i5 R1 s10.4 k-Means Clustering 159
: c% K: N0 q# I( v3 m$ M10.5 Self-Organizing Maps 1598 w9 N7 ~9 z' ~+ r+ K1 C
10.6 A Wish List for Clustering Algorithms 160: f T4 o/ f0 ]% W$ k3 X. J2 E
10.7 The Self-Organizing Tree Algorithm 161
: ~/ X3 w. `. t8 f2 V10.8 Quality-Based Clustering Algorithms 162* H% S+ m' Y: d2 k; z
10.9 Mixture Models 163( P. T0 p% g: e. R' o3 \- [
10.10 Biclustering Algorithms 166
- M8 {. V; M& [/ \& J/ L10.11 Assessing Cluster Quality 168
% ~" [! Y. S) F8 i$ q10.12 Open Horizons 1702 z# X) D9 O+ S6 K
References 171
6 D! J: o, r- m E( r) q11 Unsupervised Machine Learning to Support Functional d4 D w$ @- J: A
Characterization of Genes: Emphasis on Cluster
; Q* l$ p$ o. ^, e( w- HDescription and Class Discovery 175. }, D$ r# _: y3 ^
Olga G. Troyanskaya
9 u' M6 @5 ~+ ]2 V8 ?11.1 Functional Genomics: Goals and Data Sources 175) `; _% U, J, h1 G4 i
11.2 Functional Annotation by Unsupervised Analysis of Gene
) a! c& W/ r& s7 x, kExpression Microarray Data 1779 i2 y2 s6 t- } W8 D% W" W
11.3 Integration of Diverse Functional Data For Accurate Gene Function
, |; Q) z. S. u* {Prediction 179
?+ v) v9 d& p2 Z9 C! ?. g* h11.4 MAGIC – General Probabilistic Integration of Diverse Genomic Data 1809 c2 I* h! p4 L& V' ]$ C
11.5 Conclusion 188. [" ~$ ^: { K5 F; V) p$ }
References 189$ u' g+ Z; ^6 l2 p3 B2 g9 e
12 Supervised Methods with Genomic Data: a Review3 _) a7 H3 U* e ]) @: _/ {
and Cautionary View 193' X5 A" x8 _7 c
Ramo´n Dı´az-Uriarte
: ^. w( ~% |! Z& L: i12.1 Chapter Objectives 193
! c9 c2 s' t+ Q. n" B12.2 Class Prediction and Class Comparison 194
! T+ t" d0 E* ? G5 r: H12.3 Class Comparison: Finding/Ranking Differentially Expressed Genes 194
: e7 y% T$ f9 k12.4 Class Prediction and Prognostic Prediction 198% v5 b! d/ d2 @5 R8 R
12.5 ROC Curves for Evaluating Predictors and Differential Expression 201
9 x! u- E" {$ I# s9 r, d( c12.6 Caveats and Admonitions 203% }) @" {; y' M: s" ?7 Y
12.7 Final Note: Source Code Should be Available 209
( g$ e! u: w' }! _8 A6 LAcknowledgements 210
( ]9 ?3 ^; n3 _. b0 sReferences 210
# G, x; `6 s9 `1 x* O+ F13 A Guide to the Literature on Inferring Genetic Networks
+ a4 D0 O6 h5 n- y! l( P. Dby Probabilistic Graphical Models 2152 g& n K% l6 G, e' B/ `6 B
Pedro Larran˜aga, In˜aki Inza and Jose L. Flores
' p4 ^, q$ k8 Q! W) v7 P' O13.1 Introduction 215
$ G8 R9 `0 j1 A: ?' ~# z0 v: Z13.2 Genetic Networks 2167 k3 E. f' d. v0 G6 \- N
13.3 Probabilistic Graphical Models 218/ o* Y. X9 w) s! z, x$ H' s1 T4 v/ M
13.4 Inferring Genetic Networks by Means of Probabilistic Graphical Models 2294 y' h$ d0 P* x: R+ G6 U3 x
13.5 Conclusions 234
. j' ?8 E4 n& `4 rAcknowledgements 235
" ~' }: F \9 @4 j+ LReferences 2358 X8 f; S% w: [" ]) ?
14 Integrative Models for the Prediction and Understanding5 W7 j4 n1 Y0 ]. y2 o1 Z
of Protein Structure Patterns 2397 n- h3 i4 f2 K8 [) `
Inge Jonassen6 H$ E/ W" P1 l: V# M l" @/ E4 h
14.1 Introduction 239' C- ]7 _' \' Q! e
14.2 Structure Prediction 241
5 x' N7 U% |# u2 t, X! w$ f( y7 V4 j% [$ i14.3 Classifications of Structures 244* _9 z$ n5 M* T, _5 ^! X+ |
14.4 Comparing Protein Structures 246
& Q: h, p: F+ P7 l. V- a6 W14.5 Methods for the Discovery of Structure Motifs 249) [9 C' Q8 U/ d& D
14.6 Discussion and Conclusions 252! a, Y' S6 d9 [
References 254
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发表于 2010-9-19 18:29
