PDF电子书：Data Analysis and Visualization in Genomics and Proteomics

shinejesse

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* O# Z6 t3 J5 hSECTION I INTRODUCTION – DATA DIVERSITY AND INTEGRATION 1' b9 _9 i" {& [' \8 M" p! Y
1 Integrative Data Analysis and Visualization: Introduction- d8 a. ^3 d3 i! i& S6 Z
to Critical Problems, Goals and Challenges 3
7 [$ m, L; O+ YFrancisco Azuaje and Joaquı´n Dopazo" c: f% i1 h4 e. o
1.1 Data Analysis and Visualization: An Integrative Approach 3
# T6 y! |. P/ N, U7 q. N1.2 Critical Design and Implementation Factors 5
: i4 @' Z1 _8 I/ ?1.3 Overview of Contributions 8
% ^' i1 C3 a! m; y0 [8 D0 gReferences 9
/ U$ ?$ Y' H: M0 S' ~5 @; E2 Biological Databases: Infrastructure, Content+ {* r' Z% M+ ]+ `5 \4 i
and Integration 11# \' z  j7 b5 w4 Z, S& p% U- ]0 a
Allyson L. Williams, Paul J. Kersey, Manuela Pruess
+ [2 }2 L( T; \( D* m" a2 |$ nand Rolf Apweiler5 n% F& w" I  N/ S
2.1 Introduction 11
5 x( x2 G0 R1 |! ?( b7 n: R# C2 b2.2 Data Integration 12
- e: r& F9 W  Q. S& y" u2.3 Review of Molecular Biology Databases 171 ^, @5 |/ m$ `7 r% ]; n+ m
2.4 Conclusion 23
9 O* n" c6 {% N" OReferences 26
& B4 a2 \) z" d( o/ R3 Data and Predictive Model Integration: an Overview* v5 q/ J7 ~: y" P( M  [
of Key Concepts, Problems and Solutions 29# Y; i5 b/ }% k9 o7 b
Francisco Azuaje, Joaquı´n Dopazo and Haiying Wang
# X5 k/ r& e( G+ c- j" A3.1 Integrative Data Analysis and Visualization: Motivation and Approaches 29
. Q+ c/ c7 @6 G* s9 G# P! x3.2 Integrating Informational Views and Complexity for Understanding Function 31
1 T; w0 ^7 M  k0 B4 Q! ~9 G3.3 Integrating Data Analysis Techniques for Supporting Functional
. g0 Y% y* h0 G1 W9 LSECTION II INTEGRATIVE DATA MINING AND VISUALIZATION –+ l+ ?1 S, ^2 f4 e: _
EMPHASIS ON COMBINATION OF MULTIPLE' m& ?: y5 W: }+ F# A
DATA TYPES 41
& l! I( R3 Q5 @; T* D- o4 Applications of Text Mining in Molecular Biology, from Name3 E  }/ Z2 S3 D- h8 ?# ~1 F
Recognition to Protein Interaction Maps 43
+ Q  \! X- d" F& w* W" K7 ~; z3 BMartin Krallinger and Alfonso Valencia0 o& ?5 Q1 U, L  ^& _
4.1 Introduction 44
# `5 X0 M! {) T. u- q4.2 Introduction to Text Mining and NLP 45  t' Q' c5 O: Z" ~
4.3 Databases and Resources for Biomedical Text Mining 47; l% K0 d7 g0 O. R, X0 f+ U
4.4 Text Mining and Protein–Protein Interactions 50
. Z% r! Y) W6 I- L' `* _* D0 M* Q4.5 Other Text-Mining Applications in Genomics 55
  x' N  ^* {4 t( {+ Y* \6 N4.6 The Future of NLP in Biomedicine 56
/ Z+ \5 Z1 l6 |+ U2 r& q  |# iAcknowledgements 56- k0 Q3 W! [8 T2 `
References 567 n$ r2 h* @4 b$ ?# [1 N' O1 I
5 Protein Interaction Prediction by Integrating Genomic
) ~* q0 Z! G4 X9 C5 jFeatures and Protein Interaction Network Analysis 61
) o, [$ h, J* t& q) U. i2 O6 q0 u# BLong J. Lu, Yu Xia, Haiyuan Yu, Alexander Rives, Haoxin Lu,  ?( N$ A3 g6 \6 I4 U1 y' a" @6 C
Falk Schubert and Mark Gerstein( G0 A  v) C4 g
5.1 Introduction 62
% F/ G. X& O% ~% d9 ~1 O5 ]5.2 Genomic Features in Protein Interaction Predictions 63
9 c8 I8 W8 u3 X% y( ]: P$ \2 m+ n5.3 Machine Learning on Protein–Protein Interactions 67
9 D0 V2 ^( A; j  y- ~& l0 ?5.4 The Missing Value Problem 73* K! u" O. G& B# w* a
5.5 Network Analysis of Protein Interactions 75, w' i3 ?) B: E
5.6 Discussion 79
. r2 y4 J, ~1 U0 }References 806 Y3 j+ i3 ~1 T& [: [+ T. \
6 Integration of Genomic and Phenotypic Data 837 [! v4 D) J. f
Amanda Clare
6 B5 O0 N$ J. Q' K6.1 Phenotype 83
2 ]4 [  w& w) i6 ^6.2 Forward Genetics and QTL Analysis 858 e; S+ p& u6 h3 |2 h
6.3 Reverse Genetics 870 U* a( i' ~. L1 G: a+ D# a
6.4 Prediction of Phenotype from Other Sources of Data 88/ W, ^7 |9 J2 g5 [. d: o8 c  |+ n3 y) _
6.5 Integrating Phenotype Data with Systems Biology 90, r' O, r( G0 A! K# C. x
6.6 Integration of Phenotype Data in Databases 93  U  q  n* x& f, r: E. g
6.7 Conclusions 95
5 K; ^  {1 X* ?8 HReferences 95
) ~# O, A0 l/ f, ~( S4 ^; U1 I7 Ontologies and Functional Genomics 99% j& n" Q7 F1 F( m
Fa´tima Al-Shahrour and Joaquı´n Dopazo# Q% W$ E( m( Q
7.1 Information Mining in Genome-Wide Functional Analysis 99
: T# c4 C) y! e. G0 U8 V7.2 Sources of Information: Free Text Versus Curated Repositories 100
) A# J/ q% \, Z/ f7.3 Bio-Ontologies and the Gene Ontology in Functional Genomics 1017 {" |8 y% W9 L
7.4 Using GO to Translate the Results of Functional Genomic Experiments into- X# @. S. t$ I& V7 X
Biological Knowledge 1035 v" S* v8 S$ Z( G7 l! ~* `
7.5 Statistical Approaches to Test Significant Biological Differences 1041 `5 X+ h2 r5 C7 B3 A$ x
7.6 Using FatiGO to Find Significant Functional Associations
* f/ \. [  P' l8 {in Clusters of Genes 106* Q- _6 g* ^+ |. I; N
7.7 Other Tools 107
) R+ [% w& e' I6 A1 Q* ]4 ]7.8 Examples of Functional Analysis of Clusters of Genes 1081 T) l6 V7 t6 n- M+ w* M7 B: j
7.9 Future Prospects 110
. `/ V" \- |' B) n8 j; s) oReferences 110% e3 d' c+ }/ S2 N
8 The C. elegans Interactome: its Generation and Visualization 1130 u% M9 u# ~( y7 X- a7 f
Alban Chesnau and Claude Sardet! s  y8 B- V# |) U& h2 a
8.1 Introduction 113
* Y7 B  j+ d3 b" I& f' A! C8 j6 G3 D! x8.2 The ORFeome: the first step toward the interactome of C. elegans 116. t. D. i3 L4 U( S3 I: l' ]
8.3 Large-Scale High-Throughput Yeast Two-Hybrid Screens to Map the C. elegans
7 `0 ^2 A" F6 q: [Protein–Protein Interaction (Interactome) Network: Technical Aspects 118
- R1 z6 p) }  X' L3 G9 A1 W8.4 Visualization and Topology of Protein–Protein Interaction Networks 121
6 E! P3 C1 m6 B( f$ u4 H8.5 Cross-Talk Between the C. elegans Interactome and other Large-Scale* U, ^6 m& e+ v- a
Genomics and Post-Genomics Data Sets 123
7 p5 V, C4 Q0 K' {8 |. C( J5 T8.6 Conclusion: From Interactions to Therapies 129  I' E$ h' P4 Z5 B+ r3 n8 N+ p
References 130
! y6 S+ M$ ]$ P6 P4 BSECTION III INTEGRATIVE DATA MINING AND' |0 S. d- V. w, j6 x* D
VISUALIZATION – EMPHASIS ON& g6 V$ _$ S& {! [, N
COMBINATION OF MULTIPLE& ~8 m( J+ v+ E3 J1 Y
PREDICTION MODELS AND METHODS 135
3 o% h. F1 G7 Z* d. z8 t, H0 r9 Integrated Approaches for Bioinformatic Data Analysis
/ l7 J9 a. X" D# E9 X: dand Visualization – Challenges, Opportunities* G; M- c! Q) T+ g1 B+ [
and New Solutions 137! `: E$ a) |3 e
Steve R. Pettifer, James R. Sinnott and Teresa K. Attwood% P. Y) C6 j: R4 R! J9 G
9.1 Introduction 137
- s8 X$ f  `& X4 H, `, R0 q4 S9.2 Sequence Analysis Methods and Databases 1396 I( ^7 D4 \! Y7 A
9.3 A View Through a Portal 141
* M: \1 A' a. Z9.4 Problems with Monolithic Approaches: One Size Does Not Fit All 142% I# r1 V' c2 s( T7 X1 ~2 n
9.5 A Toolkit View 1431 h) I7 P! b4 j3 H2 I1 E: C
9.6 Challenges and Opportunities 1453 M" L2 x8 v- P% _
9.7 Extending the Desktop Metaphor 147
/ i" t" G3 h; E1 C2 w9.8 Conclusions 151
. A) B# M4 [% ~5 Z+ g0 B- T! B  jAcknowledgements 151
$ m/ N0 I" N) Z: Z5 c% o' wReferences 152
0 \0 h7 n( M/ q+ N9 M8 B10 Advances in Cluster Analysis of Microarray Data 153
5 |7 d0 D: @( q) q6 gQizheng Sheng, Yves Moreau, Frank De Smet, Kathleen Marchal, A) a2 s$ I' r
and Bart De Moor
5 J4 b! }2 O4 b* q10.1 Introduction 1537 m7 F' w1 I3 A9 s
10.2 Some Preliminaries 155: c" r: B, A9 Q3 c3 Q$ T' M" \
10.3 Hierarchical Clustering 157
9 l3 y! I, v1 s+ a, a10.4 k-Means Clustering 1590 @8 I( O0 r' r5 D% \! _% E/ D3 J3 b! U
10.5 Self-Organizing Maps 159
6 i  x0 r- @0 U10.6 A Wish List for Clustering Algorithms 1601 [9 h4 n8 r6 ^  X* w' o, V
10.7 The Self-Organizing Tree Algorithm 161
% ?2 {4 [/ ]: `10.8 Quality-Based Clustering Algorithms 162
5 E/ l8 k; Q- X10.9 Mixture Models 163
$ W+ H9 P7 Z/ F, V& M* B) W10.10 Biclustering Algorithms 166
5 d1 [( c0 q" |/ e- D/ }10.11 Assessing Cluster Quality 168
- ^2 B! I6 @% H& g# }10.12 Open Horizons 170
0 I5 h& ?8 I) @/ }1 ]2 N0 hReferences 171: c' W' v( F- U2 m, n7 t1 K* J3 ?
11 Unsupervised Machine Learning to Support Functional! s) ]( S5 r+ s. R7 E) ^; u
Characterization of Genes: Emphasis on Cluster
' R% m, ?1 c/ p7 h( PDescription and Class Discovery 175/ Y- y% M9 c+ |( z1 j8 e
Olga G. Troyanskaya' D7 }9 \( d. C& t6 P
11.1 Functional Genomics: Goals and Data Sources 175& a( G. A5 s2 e) a
11.2 Functional Annotation by Unsupervised Analysis of Gene
1 R! D" }+ M3 E: mExpression Microarray Data 177
7 `% G* V+ S; V( h2 Y3 U11.3 Integration of Diverse Functional Data For Accurate Gene Function
5 D9 z! @3 M1 CPrediction 1796 `: z" H: K8 ]* G
11.4 MAGIC – General Probabilistic Integration of Diverse Genomic Data 180
. p. f6 A( E6 a9 E7 Q$ C/ S11.5 Conclusion 188! C! \: P! m1 U
References 1894 c7 a; H( Z: ?* j1 O. q1 j4 k
12 Supervised Methods with Genomic Data: a Review
2 K0 R/ q3 _6 x" H! S, p, Land Cautionary View 193
8 U7 A7 l: D  G2 vRamo´n Dı´az-Uriarte$ @' R/ e% b3 U
12.1 Chapter Objectives 193
4 H% h7 i5 g5 u& I) e# y12.2 Class Prediction and Class Comparison 194
/ K" Q9 L- v4 x7 z0 E12.3 Class Comparison: Finding/Ranking Differentially Expressed Genes 194
+ i* N- v7 I* k9 `4 P12.4 Class Prediction and Prognostic Prediction 198$ Y3 P  n( N/ ^8 m; r6 r0 J
12.5 ROC Curves for Evaluating Predictors and Differential Expression 2011 x) ^# O) ~3 q1 O1 P" G
12.6 Caveats and Admonitions 2035 e, b/ [! D7 I/ E  F  e0 Z0 R6 l
12.7 Final Note: Source Code Should be Available 209& w% }% L# r" l% }) D0 u$ q+ x7 m
Acknowledgements 210' p* `* v* Z4 K' \. W) N, \
References 210
& t; {, W: t. W5 }: `13 A Guide to the Literature on Inferring Genetic Networks
8 a: V  X- h' ]0 K- \by Probabilistic Graphical Models 215' z  e9 P/ N/ }# J+ A8 |- o
Pedro Larran˜aga, In˜aki Inza and Jose L. Flores
5 K; `6 a, u: P13.1 Introduction 2151 ?, D" K) r+ M3 j2 @+ u
13.2 Genetic Networks 216
/ U. x) y& U" t. u+ f, V' m13.3 Probabilistic Graphical Models 218
( j0 {* {$ ?3 @13.4 Inferring Genetic Networks by Means of Probabilistic Graphical Models 229/ K2 ^' }5 b* Z
13.5 Conclusions 234) g2 b  H3 B+ s3 s6 v( X+ a; N* n  r
Acknowledgements 235
0 [, t# \! J' K" ZReferences 235
& k$ O9 c8 Z  N( c14 Integrative Models for the Prediction and Understanding2 l6 o. k; Z/ Z3 t
of Protein Structure Patterns 239
/ {' i/ c) X# \- {3 `4 ~Inge Jonassen
4 d6 r+ y$ ?4 A: N8 V' X14.1 Introduction 239
8 T% Z; k4 H3 q, _2 h14.2 Structure Prediction 241
7 k, h- z, j  q! W14.3 Classifications of Structures 244  G: Q3 i, {. [* ?- Y% l
14.4 Comparing Protein Structures 246- j' v7 L: D0 b0 a+ Z+ |- w
14.5 Methods for the Discovery of Structure Motifs 249
/ L7 _" [- c- h; m14.6 Discussion and Conclusions 252+ h/ ^* L, ^3 Z, I, W* P0 u! P
References 2545 w) Y1 |) Y9 v! b6 x" c$ M" S2 G

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