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本帖最后由 细胞海洋 于 2010-9-19 20:20 编辑 $ ]$ ?% @' d4 ^
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SECTION I INTRODUCTION – DATA DIVERSITY AND INTEGRATION 1. C# Q' E& I9 @+ o
1 Integrative Data Analysis and Visualization: Introduction# h0 C) s0 j/ m1 F
to Critical Problems, Goals and Challenges 3
1 Y9 |% N- B4 ?4 F4 g: QFrancisco Azuaje and Joaquı´n Dopazo' k9 R6 M* S) k5 b7 R
1.1 Data Analysis and Visualization: An Integrative Approach 3+ O# S, ~3 m2 H1 H0 W1 e
1.2 Critical Design and Implementation Factors 5& |' T8 l7 h' w. h- k% e
1.3 Overview of Contributions 81 F: ?' y* T; m) g
References 9$ T9 @' ]4 b' i. C6 ~3 ]0 g
2 Biological Databases: Infrastructure, Content
; Y" Q8 D7 C7 }1 V) o6 h! {and Integration 11! u7 a, b; X' S* `, V6 _: A
Allyson L. Williams, Paul J. Kersey, Manuela Pruess
# X2 h: I L& Rand Rolf Apweiler
$ n9 L- M F0 M, \# l4 u2.1 Introduction 11
( x3 X" N# z. |, M3 @+ F2.2 Data Integration 12
' J+ P2 s5 D* o$ ~4 q% h2.3 Review of Molecular Biology Databases 17- d3 d+ `+ z0 A6 }$ n$ m6 u3 Z& p
2.4 Conclusion 23
7 n( p: s/ {# U% e) g, zReferences 26; K K3 o z0 N4 `, c8 V+ |
3 Data and Predictive Model Integration: an Overview' ]1 f. _1 e! y1 }5 \( k
of Key Concepts, Problems and Solutions 290 i/ S$ H5 ^* p5 n
Francisco Azuaje, Joaquı´n Dopazo and Haiying Wang
' N& F9 w- ?8 a- `! S8 n3.1 Integrative Data Analysis and Visualization: Motivation and Approaches 298 M7 f( P* d. }- h% o5 N; w
3.2 Integrating Informational Views and Complexity for Understanding Function 31
; J/ e4 t9 n7 O3.3 Integrating Data Analysis Techniques for Supporting Functional9 y! ~. m: ?8 o$ b+ X
SECTION II INTEGRATIVE DATA MINING AND VISUALIZATION –! l2 Q: X- t) x0 N2 k+ ]
EMPHASIS ON COMBINATION OF MULTIPLE
2 a1 C9 E7 G/ O" v, _9 CDATA TYPES 41
) X! W e) Q1 ~1 q0 `7 @4 Applications of Text Mining in Molecular Biology, from Name
4 S7 k# j5 ^" K+ k. \Recognition to Protein Interaction Maps 43' ] _ Z7 b9 }
Martin Krallinger and Alfonso Valencia
0 w8 w- L8 j' q$ L1 i4.1 Introduction 44, o, }4 ~6 _7 c" w0 K
4.2 Introduction to Text Mining and NLP 45* B3 Y0 N1 U$ u4 Z5 o( h
4.3 Databases and Resources for Biomedical Text Mining 47
* D. u* n: Q& c3 O4.4 Text Mining and Protein–Protein Interactions 50
5 Z( x# t4 y$ P' P4 D" K$ R. Q9 W4.5 Other Text-Mining Applications in Genomics 55
+ |6 p3 ] X" E1 K4.6 The Future of NLP in Biomedicine 56. |0 w2 n4 Q5 v' g: g- E
Acknowledgements 56& A( e2 I) S) Z: `/ ~) z
References 56
7 L+ o$ F1 g9 [5 Protein Interaction Prediction by Integrating Genomic2 S+ b: U1 m8 m1 e' K
Features and Protein Interaction Network Analysis 61. ~. Y% @, q7 R/ z: H
Long J. Lu, Yu Xia, Haiyuan Yu, Alexander Rives, Haoxin Lu,
/ e+ r) ?3 Q ?; E: C* Q' t2 b" uFalk Schubert and Mark Gerstein/ C V( S. Y+ K
5.1 Introduction 625 b# m! w( _# m3 z
5.2 Genomic Features in Protein Interaction Predictions 63
- s4 i8 I# E s: B# `5.3 Machine Learning on Protein–Protein Interactions 67
: i, }$ \5 a/ N7 a n: R2 Z5.4 The Missing Value Problem 73" U. R: }& I- t
5.5 Network Analysis of Protein Interactions 755 v6 I/ H) b( z! f' q
5.6 Discussion 792 C9 e! r8 C" g7 B2 P1 K
References 80
& |! i4 V! z! U4 r3 K' o6 Integration of Genomic and Phenotypic Data 83
! f2 w) B! d' f; @7 N- YAmanda Clare
3 G# N2 a) l0 i+ O7 G6.1 Phenotype 832 O4 I" T+ }6 Y- |& @
6.2 Forward Genetics and QTL Analysis 859 G5 M3 h( R' x/ A! m1 N5 I
6.3 Reverse Genetics 87
' I, m# m8 z* ^. s/ ?4 C( X8 W( L6.4 Prediction of Phenotype from Other Sources of Data 88
* R. {$ |$ Y6 }3 ]3 ?, p6.5 Integrating Phenotype Data with Systems Biology 90
# k* u+ d3 o% }$ U+ z5 L6.6 Integration of Phenotype Data in Databases 93
% @% ~7 r# c7 P" O6.7 Conclusions 958 ^. @2 K. z; g$ ]7 R
References 95( g U& i, g) A+ t9 ^3 `' _9 g
7 Ontologies and Functional Genomics 99
) G" a+ M8 Z" NFa´tima Al-Shahrour and Joaquı´n Dopazo( x; i5 e& J) L
7.1 Information Mining in Genome-Wide Functional Analysis 99
h% Z0 N4 j* W. A r `/ G7.2 Sources of Information: Free Text Versus Curated Repositories 1008 ]( M( U* T* F
7.3 Bio-Ontologies and the Gene Ontology in Functional Genomics 1017 R6 d3 z# @$ `
7.4 Using GO to Translate the Results of Functional Genomic Experiments into) Y& X+ q2 \" [
Biological Knowledge 103+ o( t1 x2 R) T9 P5 {
7.5 Statistical Approaches to Test Significant Biological Differences 104
/ M( S% X+ }$ O: }9 l7.6 Using FatiGO to Find Significant Functional Associations6 y0 Y3 Q3 j# b: n+ D* \1 W
in Clusters of Genes 106, g& ]) R& A( i% a% P& Z
7.7 Other Tools 107
& d) k. t1 T* }$ i9 y) A( Z# a7.8 Examples of Functional Analysis of Clusters of Genes 108, N% q' T* G' N' p2 m; v
7.9 Future Prospects 110
6 s& ]7 R! g& ^, P4 M- v* L3 e) Y& XReferences 110( v& Y" r! O5 y7 ]& K; j
8 The C. elegans Interactome: its Generation and Visualization 113
7 }7 F2 g ]' f! lAlban Chesnau and Claude Sardet5 m6 l5 n/ G9 m; `) }& k1 m
8.1 Introduction 113
9 [1 m7 c2 Y, }$ Q0 N8.2 The ORFeome: the first step toward the interactome of C. elegans 116& h0 ]. O/ t, W& D0 ~# D7 s' X: Z
8.3 Large-Scale High-Throughput Yeast Two-Hybrid Screens to Map the C. elegans
w% I& L, ~$ j# P" u, s; LProtein–Protein Interaction (Interactome) Network: Technical Aspects 118
) G8 g( b$ l4 x5 i# j0 |' J8.4 Visualization and Topology of Protein–Protein Interaction Networks 121
7 |% C Y' q: y' P8.5 Cross-Talk Between the C. elegans Interactome and other Large-Scale) j; K! n8 q/ J! w# ?9 ~5 v
Genomics and Post-Genomics Data Sets 123" I8 G+ v9 l2 p# h& w0 p8 g; m
8.6 Conclusion: From Interactions to Therapies 129/ j! J1 Z( a$ B$ D: w
References 130
( p1 ~% q/ w6 \9 ]SECTION III INTEGRATIVE DATA MINING AND
6 p" w* d3 I7 N7 {VISUALIZATION – EMPHASIS ON
! y5 `7 l( \0 z7 ~# n6 B7 I+ tCOMBINATION OF MULTIPLE, G6 n3 e5 T* b) |+ I& e, F
PREDICTION MODELS AND METHODS 135
5 a& q. \- Q5 k- y9 Integrated Approaches for Bioinformatic Data Analysis; B( F% k5 g+ d
and Visualization – Challenges, Opportunities: W4 k* x5 R4 G& D) j) D
and New Solutions 137: C8 D2 Z9 c$ G2 }: X- u! h2 a d
Steve R. Pettifer, James R. Sinnott and Teresa K. Attwood
* j1 k: e, S1 k3 d* G9.1 Introduction 137
; M7 p. H6 n, c+ c* u9.2 Sequence Analysis Methods and Databases 1393 f/ a- C D h- o" O
9.3 A View Through a Portal 141: S; R& Q! U) X1 ?; D
9.4 Problems with Monolithic Approaches: One Size Does Not Fit All 142$ n, V2 D' P% W* _, R4 E* R: [" y
9.5 A Toolkit View 143$ B. E5 x6 Y0 `9 w5 m, C5 O$ P
9.6 Challenges and Opportunities 1451 i8 N5 `0 z9 L
9.7 Extending the Desktop Metaphor 147, P1 q U; a( x- i$ R
9.8 Conclusions 1515 R" v' }7 `- M" G/ ~ e, \" B
Acknowledgements 151. d6 q& t3 t' `# P0 ^
References 152! N3 g6 m( e! ~* E/ M- ]& f, L5 n
10 Advances in Cluster Analysis of Microarray Data 153% m. K5 x& a3 t5 f
Qizheng Sheng, Yves Moreau, Frank De Smet, Kathleen Marchal
, o% z# L1 z* O u) o3 }and Bart De Moor) ~$ `' E3 L, q& Z* \
10.1 Introduction 153- u1 x) S; y# f6 y6 L$ y! O4 r+ t4 z
10.2 Some Preliminaries 155
% V5 L% F" o% o9 A10.3 Hierarchical Clustering 157
. S' h( l. c) A6 b$ y: G# X. f/ z- Q10.4 k-Means Clustering 159, c8 m4 W+ a0 j4 K/ p: w
10.5 Self-Organizing Maps 159. k+ ^5 Z- J- R% _! v, K1 ?
10.6 A Wish List for Clustering Algorithms 160# t0 H" P) l) [7 v9 k1 c' \
10.7 The Self-Organizing Tree Algorithm 161 j; G1 o5 O8 v: p* L' w5 O- e
10.8 Quality-Based Clustering Algorithms 162
: h' \0 w9 }: @ o! A. q10.9 Mixture Models 163
" B$ }5 [2 K2 g9 E A4 J10.10 Biclustering Algorithms 166+ q6 u o7 C; X, a9 Z& F* \- |
10.11 Assessing Cluster Quality 168
- V% B; D- k7 }7 p7 @# l10.12 Open Horizons 170
$ D7 s6 \+ ^. ]1 ]8 h+ g* p' Y9 kReferences 171
5 D. x+ d( e# y" _0 \11 Unsupervised Machine Learning to Support Functional
5 |. h' k6 _' ` [2 N- lCharacterization of Genes: Emphasis on Cluster
& T ?% V/ z {+ v5 _$ t- bDescription and Class Discovery 175
$ r1 Q2 e; M& s9 l+ m) J4 j3 mOlga G. Troyanskaya; ?: _8 y# @ g+ f+ l; j. N
11.1 Functional Genomics: Goals and Data Sources 175
, z o0 t" k1 n11.2 Functional Annotation by Unsupervised Analysis of Gene! H7 M8 b& J) Q8 N8 a1 \' g7 t$ s
Expression Microarray Data 177" o, \4 M4 G% D# K7 E8 y. q% z
11.3 Integration of Diverse Functional Data For Accurate Gene Function
, A9 f2 `) R- o2 w& v# y+ {& `8 P0 W* YPrediction 179- v8 h, k: V! h/ q& n- N
11.4 MAGIC – General Probabilistic Integration of Diverse Genomic Data 180
6 i4 C* b6 B. f: K11.5 Conclusion 188! T8 l7 b2 x0 c4 i' M/ s6 D Y9 b
References 189
1 K+ q- r$ G4 C: I0 Y12 Supervised Methods with Genomic Data: a Review
, K8 O, E/ Z8 ~# b6 N. Xand Cautionary View 193
) o) f1 A U$ z- v$ E; _Ramo´n Dı´az-Uriarte ^( w6 E: b( D6 [7 d
12.1 Chapter Objectives 193% ~- r3 D/ m! l& u# P. W
12.2 Class Prediction and Class Comparison 194
! S+ m( @9 ]) Y% f& w' k2 L12.3 Class Comparison: Finding/Ranking Differentially Expressed Genes 194
) @! e# J3 f$ o+ d4 e# a' m) G12.4 Class Prediction and Prognostic Prediction 198
& o7 j9 j& l7 A) v+ P5 {2 x12.5 ROC Curves for Evaluating Predictors and Differential Expression 201
1 \2 v; i8 Z J+ Q( I( g5 C12.6 Caveats and Admonitions 203! f% A ?+ B) w
12.7 Final Note: Source Code Should be Available 209
' F- ], O1 Z/ z3 mAcknowledgements 210
# V/ {# ?* |5 |2 ^, [ L1 S% ]References 210
4 e# F) Q$ ~* f) g( y% j13 A Guide to the Literature on Inferring Genetic Networks
+ C( W/ f* f$ o% Y6 ^* N' ]by Probabilistic Graphical Models 215
& g9 Q# [+ h8 ~3 TPedro Larran˜aga, In˜aki Inza and Jose L. Flores
0 E$ F i+ l0 T5 H' r; D13.1 Introduction 215
$ p9 R4 W2 p7 y) q: r6 u13.2 Genetic Networks 2167 L; k* H# k) z+ g* G, T8 N; R; y
13.3 Probabilistic Graphical Models 218' q+ \5 M+ F2 d x% Y
13.4 Inferring Genetic Networks by Means of Probabilistic Graphical Models 229* d" L2 F3 R" d8 c9 v7 I
13.5 Conclusions 234) m& b) u- @3 a) U3 i! C: \) w
Acknowledgements 235
5 A! C" {3 }) _8 o# D. a3 LReferences 235
4 e8 @" f/ v8 e' b: }/ H7 v( ]6 {14 Integrative Models for the Prediction and Understanding- h" p" [ V# R- u+ H0 t6 j% `9 Q
of Protein Structure Patterns 239
! `* N- k E" l* KInge Jonassen
% s1 m7 L W; D7 C3 k4 t5 `* q6 H14.1 Introduction 239
+ q5 y% M& |7 c+ o6 r% {- \: |14.2 Structure Prediction 241
# G8 z/ l8 J/ ]% v6 L1 o0 y14.3 Classifications of Structures 244+ B! e; v7 v9 P' q5 @* d5 d
14.4 Comparing Protein Structures 2464 R- K) Q- c: C, \8 @/ n* l8 O
14.5 Methods for the Discovery of Structure Motifs 249; `. v+ t2 k+ Y$ M ^8 M. P" T! E
14.6 Discussion and Conclusions 252
. w" L; _3 o/ x+ o" tReferences 254( T# s4 [, F( p9 V3 l6 b1 G8 l
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发表于 2010-9-19 18:29
