|
- 积分
- 133
- 威望
- 133
- 包包
- 316
|
本帖最后由 细胞海洋 于 2010-9-19 20:20 编辑 4 I' y% X G3 ?: B8 o7 o/ I
0 z% }7 J; W8 U" _' q- e* ?3 s1 w
SECTION I INTRODUCTION – DATA DIVERSITY AND INTEGRATION 1: C v1 p. r- M6 j
1 Integrative Data Analysis and Visualization: Introduction+ f" C4 h5 V) P% z) ]
to Critical Problems, Goals and Challenges 33 i. l+ o$ Z$ A+ [- Y
Francisco Azuaje and Joaquı´n Dopazo
0 b b' s; U2 D% Z1.1 Data Analysis and Visualization: An Integrative Approach 32 z" u% d2 e/ D( K9 g. m1 r
1.2 Critical Design and Implementation Factors 5! L% @( x+ l' [$ n: R
1.3 Overview of Contributions 8
- K# `* @6 h" O+ S% OReferences 92 S9 d& P( e; w; x/ n
2 Biological Databases: Infrastructure, Content5 N' {$ |4 h$ n3 @2 i
and Integration 11
. F. y: r7 f/ M& |Allyson L. Williams, Paul J. Kersey, Manuela Pruess
+ H0 [( Z6 y0 ]" U8 v, v! n9 mand Rolf Apweiler2 J! N- C6 B$ a
2.1 Introduction 117 n: Y1 B+ B( D% `" F3 v
2.2 Data Integration 12
( c$ M5 d1 A& B0 l7 d2.3 Review of Molecular Biology Databases 17+ E# H0 s: F; i+ G5 B% _
2.4 Conclusion 23
+ E% G F& V i. FReferences 266 D8 P- C1 ~0 s% X( U
3 Data and Predictive Model Integration: an Overview G' o U, y4 Q3 Q' v5 m
of Key Concepts, Problems and Solutions 29, q2 V, _( F+ Z" J, B4 Y' T
Francisco Azuaje, Joaquı´n Dopazo and Haiying Wang
0 N# }1 w, p0 G- G$ }3 G- E3.1 Integrative Data Analysis and Visualization: Motivation and Approaches 292 @6 [* s4 d' A& @, }% P
3.2 Integrating Informational Views and Complexity for Understanding Function 31
& `* |6 i" s( [& Y3.3 Integrating Data Analysis Techniques for Supporting Functional8 y; J: O: t) n; P. \ g1 D
SECTION II INTEGRATIVE DATA MINING AND VISUALIZATION –
2 F/ \) E% f1 f8 c2 b( G+ S( D; h8 REMPHASIS ON COMBINATION OF MULTIPLE. z! {$ M9 o* \9 t: X9 s
DATA TYPES 41
" q# g. `3 T# _5 F" X4 Applications of Text Mining in Molecular Biology, from Name, G- W1 G; V7 E8 B* t
Recognition to Protein Interaction Maps 43
0 f5 ?6 l* `4 tMartin Krallinger and Alfonso Valencia! _ g# Q5 R8 z8 s, d" ]7 U
4.1 Introduction 44: y" L' s( w, F1 ]7 G2 q& u
4.2 Introduction to Text Mining and NLP 45
) I/ i7 x7 y2 H j4.3 Databases and Resources for Biomedical Text Mining 47
7 l3 U. m; a0 \7 ^7 W2 m4.4 Text Mining and Protein–Protein Interactions 50
8 A" }7 F; O( }0 a* D4.5 Other Text-Mining Applications in Genomics 55
4 z* O/ T/ `3 H4.6 The Future of NLP in Biomedicine 566 F2 ]2 @/ r4 b* P& }
Acknowledgements 562 Y( y5 K4 ]% W
References 56
$ E' e3 T E4 G4 d* X5 Protein Interaction Prediction by Integrating Genomic# A5 H: i, K6 J/ J5 t, c) d
Features and Protein Interaction Network Analysis 61 R* Y3 c" a2 @
Long J. Lu, Yu Xia, Haiyuan Yu, Alexander Rives, Haoxin Lu,4 u4 v, ^" N: B- U4 J' w
Falk Schubert and Mark Gerstein. g# b: {& s3 z! U4 `) [: L' g
5.1 Introduction 62
7 r" Q( J( y; n7 D1 _5.2 Genomic Features in Protein Interaction Predictions 63; n$ D* u1 ]& w: p: L: T( b
5.3 Machine Learning on Protein–Protein Interactions 67/ b, m6 i" A; j; U7 e" C0 a
5.4 The Missing Value Problem 73
4 j/ G2 a: e/ t4 U% d- _5.5 Network Analysis of Protein Interactions 757 G: w2 }, s8 }; H9 @5 @% N
5.6 Discussion 79. X/ V7 z P# F% z
References 80
; M- Y# u' w. [6 Integration of Genomic and Phenotypic Data 832 x/ r! k, c g6 F/ s
Amanda Clare3 H4 G! Q. Q ?3 i+ P
6.1 Phenotype 83
6 J9 M `! N, {6.2 Forward Genetics and QTL Analysis 85. u$ f, C- t& X9 T
6.3 Reverse Genetics 87& j3 W+ C9 i3 [, J. C2 v! K
6.4 Prediction of Phenotype from Other Sources of Data 883 |7 I3 b' H5 R B T
6.5 Integrating Phenotype Data with Systems Biology 90
7 k+ k/ s; j4 t7 h, u& K* _+ W9 k* F6.6 Integration of Phenotype Data in Databases 93/ A& P% X& L6 ~, \8 |
6.7 Conclusions 95# |3 t t. W/ H9 W
References 95
/ l! q9 E6 d; ]8 @/ H5 _: g7 Ontologies and Functional Genomics 99/ a* \% L0 z) i' Z
Fa´tima Al-Shahrour and Joaquı´n Dopazo% W) S7 o m+ v; K6 P3 o
7.1 Information Mining in Genome-Wide Functional Analysis 99% T# U6 |. i/ f4 ~5 h
7.2 Sources of Information: Free Text Versus Curated Repositories 100) X% \0 I5 Z9 T, _+ K! x
7.3 Bio-Ontologies and the Gene Ontology in Functional Genomics 101+ C, {" e. l" q4 X, |9 _5 P- l
7.4 Using GO to Translate the Results of Functional Genomic Experiments into
2 t( [3 Z3 N, r6 F; u lBiological Knowledge 103* {2 n/ L# D y/ m b1 i, p
7.5 Statistical Approaches to Test Significant Biological Differences 104
* q; {) v& [7 x7.6 Using FatiGO to Find Significant Functional Associations
0 Y0 A% B/ F1 }: V2 f- L* N: Zin Clusters of Genes 1067 D- T8 E! y$ r4 m8 G- Z
7.7 Other Tools 1079 A# ~4 b; N: I5 p+ @0 S
7.8 Examples of Functional Analysis of Clusters of Genes 108
! B; K! v; p g1 i5 ?+ T6 a# }7.9 Future Prospects 110
" Y2 M2 x$ u: M8 aReferences 110% t% i/ w& R l( X
8 The C. elegans Interactome: its Generation and Visualization 1132 ?2 l" c7 [1 [3 c
Alban Chesnau and Claude Sardet
$ J6 @8 p$ U0 X' Q- @8.1 Introduction 1137 l9 K8 u8 @% \3 C% [* t- c& e F
8.2 The ORFeome: the first step toward the interactome of C. elegans 116
. q; H. A# ~" M4 W$ J, F, T8.3 Large-Scale High-Throughput Yeast Two-Hybrid Screens to Map the C. elegans* r& L- H- X, A( E- e
Protein–Protein Interaction (Interactome) Network: Technical Aspects 118
: @+ Z3 B! W5 e) Q0 O/ {4 ^8.4 Visualization and Topology of Protein–Protein Interaction Networks 1212 N, i, q* v7 a# I
8.5 Cross-Talk Between the C. elegans Interactome and other Large-Scale
" a5 V) P2 n" Y# mGenomics and Post-Genomics Data Sets 123! ~2 b2 F% s$ a6 R6 E
8.6 Conclusion: From Interactions to Therapies 129( L- k! R& n# U( I$ t2 B
References 130
+ q! e# v+ @1 u% P7 @SECTION III INTEGRATIVE DATA MINING AND
, A# O" ^! z8 X# k {$ z8 zVISUALIZATION – EMPHASIS ON
0 \! `/ v# _ {. O G# d& |COMBINATION OF MULTIPLE9 ^2 A3 w" v9 ?6 p8 N
PREDICTION MODELS AND METHODS 135
! z( S6 N8 l+ P G4 S3 t; z9 Integrated Approaches for Bioinformatic Data Analysis& F& R W3 X3 K( N8 ]3 `
and Visualization – Challenges, Opportunities
" A c* X$ d6 ^" p5 Oand New Solutions 137
. @$ m/ L% n9 |, V2 o2 S4 tSteve R. Pettifer, James R. Sinnott and Teresa K. Attwood0 D4 R' `5 z G" Q- ]
9.1 Introduction 137
. b. h; C( [! s/ R c9.2 Sequence Analysis Methods and Databases 139/ W8 _( l a/ a# G
9.3 A View Through a Portal 141) o# O4 k, j1 b9 Z- h
9.4 Problems with Monolithic Approaches: One Size Does Not Fit All 142
# x+ y" R5 h" |0 ]9.5 A Toolkit View 143
$ z7 Q8 ^1 O9 p9.6 Challenges and Opportunities 145! M, W6 O5 D% ^7 }, j
9.7 Extending the Desktop Metaphor 147) M3 o, h8 R% a4 K* E( R
9.8 Conclusions 1513 {( ^6 t- m% D; }0 V( o9 Z s
Acknowledgements 151
$ b- ~ |" W& CReferences 152
0 ^+ M, P6 S3 J. M8 _3 ^10 Advances in Cluster Analysis of Microarray Data 153" @6 n+ G) W# R: @! ?
Qizheng Sheng, Yves Moreau, Frank De Smet, Kathleen Marchal
y3 O: E8 Q7 band Bart De Moor; ~+ _: T8 @0 i" B8 E+ x2 ^: o
10.1 Introduction 153/ e7 x3 i9 ~$ l3 ~0 z
10.2 Some Preliminaries 155% G( c9 Y1 L' D' {0 q1 `
10.3 Hierarchical Clustering 157
( j, U, R M) Y, u: l10.4 k-Means Clustering 159
4 Y1 M; K" H y T10.5 Self-Organizing Maps 159
5 a9 Y; ^$ b5 D, M10.6 A Wish List for Clustering Algorithms 160
) [% _# y* b% G- G10.7 The Self-Organizing Tree Algorithm 161
7 v, v) \% C. O10.8 Quality-Based Clustering Algorithms 162% D& G# f u1 F8 }+ g/ i0 ~
10.9 Mixture Models 163
1 ~- P& A2 }# C" Z4 k10.10 Biclustering Algorithms 1667 v0 `; {% z) i0 `2 p8 `
10.11 Assessing Cluster Quality 1689 h8 L" {! H+ m& N B
10.12 Open Horizons 170, ~4 d) _& [) `1 h
References 171
6 S( y$ }4 C3 V, V! k11 Unsupervised Machine Learning to Support Functional
- O+ q8 i% k$ ?+ g: eCharacterization of Genes: Emphasis on Cluster9 N, n& h5 {4 y0 M# u2 O
Description and Class Discovery 175. N+ R* w6 ]6 J2 }4 {
Olga G. Troyanskaya, y! w/ c7 ~3 B( [% c
11.1 Functional Genomics: Goals and Data Sources 175
5 J: I+ F7 w' A+ B6 i11.2 Functional Annotation by Unsupervised Analysis of Gene8 \$ Z8 C# ~# y& K
Expression Microarray Data 177+ C( d/ L& T, f) B
11.3 Integration of Diverse Functional Data For Accurate Gene Function6 a, y2 M9 h- w# n; `% c8 s
Prediction 1799 o# P: R& T# h% K
11.4 MAGIC – General Probabilistic Integration of Diverse Genomic Data 180# D- c$ z: O: M
11.5 Conclusion 188
2 }( |* b; l/ ]* D: f7 V4 E- [References 1894 A3 q6 r2 R( J% d% |
12 Supervised Methods with Genomic Data: a Review3 z: B6 W& Z" Z* \3 I% @9 w+ G! W" r
and Cautionary View 193- o \4 u# a4 ^/ Z/ H
Ramo´n Dı´az-Uriarte
* C( l# |& v! U% X4 g12.1 Chapter Objectives 193
. @, H1 p! N2 g( N12.2 Class Prediction and Class Comparison 194
h% ~6 N; Y- X0 o0 A/ C12.3 Class Comparison: Finding/Ranking Differentially Expressed Genes 194. P( M6 ?* A' Z, y/ K, k- v7 y
12.4 Class Prediction and Prognostic Prediction 1987 c; g' A- p O" P
12.5 ROC Curves for Evaluating Predictors and Differential Expression 201
: W K' T4 l7 J7 F12.6 Caveats and Admonitions 203
) x5 y6 V1 b4 ?; w12.7 Final Note: Source Code Should be Available 209! N4 m) n) N# g5 u
Acknowledgements 210
9 e% x9 g3 F6 B& z4 AReferences 2100 F3 ]! s; k! H" _9 Q' X! X+ Y
13 A Guide to the Literature on Inferring Genetic Networks8 n3 }9 S8 }7 j5 Y% t1 I
by Probabilistic Graphical Models 215
3 a a; s% M/ q& `4 mPedro Larran˜aga, In˜aki Inza and Jose L. Flores
2 t( |- |6 x3 h7 F6 R' m' o" R2 f13.1 Introduction 215! j- S& m, R0 n$ H6 j' z) p% T
13.2 Genetic Networks 216
& m$ o) n" D7 I. D( |13.3 Probabilistic Graphical Models 218
* ?3 z2 n0 v2 [5 f1 F) {5 T# E5 v$ O. i13.4 Inferring Genetic Networks by Means of Probabilistic Graphical Models 2293 I7 Q9 W( M4 n! \4 Y
13.5 Conclusions 234 K! q- i8 J1 Q6 h9 \8 g
Acknowledgements 235
/ w# n |( `) wReferences 235
9 m+ r# s S$ ]1 V, I14 Integrative Models for the Prediction and Understanding
2 k" H, ~. M6 B% T# u* X, W `of Protein Structure Patterns 2399 X2 z+ \" k$ c6 l- z9 e
Inge Jonassen
& l( k0 g# K; h5 j. b14.1 Introduction 239
* U% n8 a3 q( j0 n3 c; t14.2 Structure Prediction 241) z) R7 q) X/ z8 y% A9 \& L: |
14.3 Classifications of Structures 244# ?3 F/ M, j* Z6 ?8 ?+ J# `4 y' W
14.4 Comparing Protein Structures 246& v$ r" x- [ _
14.5 Methods for the Discovery of Structure Motifs 249
' D9 p; Y& O2 q. x# Y* x. \! a" D14.6 Discussion and Conclusions 252; {! a9 r" i$ v8 q* U
References 2541 @$ n& H7 |* D% v
G X5 D2 Z/ X[hide][/hide] |
附件: 你需要登录才可以下载或查看附件。没有帐号？注册
-
总评分: 威望 + 2
包包 + 5
查看全部评分
|
发表于 2010-9-19 18:29
