|
- 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
, t$ b4 L8 u4 o" T& m
! P( n3 V1 j8 x; I+ ^4 wSECTION I INTRODUCTION – DATA DIVERSITY AND INTEGRATION 1
2 W A& R& i" u* t0 n1 Integrative Data Analysis and Visualization: Introduction9 G$ _4 K3 H: f- ?& V' E
to Critical Problems, Goals and Challenges 34 A4 e- T7 E! C1 V3 ]
Francisco Azuaje and Joaquı´n Dopazo7 T u# A( v+ Q
1.1 Data Analysis and Visualization: An Integrative Approach 3; s' N6 S! f; p" `6 M3 r
1.2 Critical Design and Implementation Factors 51 A/ u0 b# P! Y3 D7 v# }
1.3 Overview of Contributions 8
) g, Y' L Q7 L( O5 Q% ~References 9
$ `9 z" S1 H) Q1 ]( [$ G2 Biological Databases: Infrastructure, Content
6 Q. g( z- e' {8 Uand Integration 11. F" ~# M' |9 _5 g* w" W m( G. Z
Allyson L. Williams, Paul J. Kersey, Manuela Pruess% g% `# S& c4 v/ e) j- H
and Rolf Apweiler- ?1 |, Y' M+ ^7 V+ v% H6 _
2.1 Introduction 11
! [, N/ X* u- ^# @2.2 Data Integration 12
3 D8 Y3 q; g% S1 ^2.3 Review of Molecular Biology Databases 17/ C$ U3 q5 r( P- Y; t: K/ Y5 d- D0 x
2.4 Conclusion 23! s5 X5 G8 h7 B( s5 c
References 26
" |: e5 V, t: z2 h* {3 Data and Predictive Model Integration: an Overview. _% z' e1 U; A1 g6 F
of Key Concepts, Problems and Solutions 29
. y" L1 B s4 yFrancisco Azuaje, Joaquı´n Dopazo and Haiying Wang' \" |, T) u, [
3.1 Integrative Data Analysis and Visualization: Motivation and Approaches 297 u. B7 p% v- y
3.2 Integrating Informational Views and Complexity for Understanding Function 31: b J3 N- n% ?- S d: t
3.3 Integrating Data Analysis Techniques for Supporting Functional8 W+ ?, O: x0 k( o
SECTION II INTEGRATIVE DATA MINING AND VISUALIZATION –
8 K; ~; w n/ kEMPHASIS ON COMBINATION OF MULTIPLE
/ m3 s' L% N4 WDATA TYPES 41
2 [& a" {$ y: J: Z4 Applications of Text Mining in Molecular Biology, from Name
9 m3 {4 X: B7 C. S7 P& v. d, ERecognition to Protein Interaction Maps 438 z! B' E' W7 U" E- k+ C" C
Martin Krallinger and Alfonso Valencia: w: S1 V# p! r# M2 e0 o' \. Q- r
4.1 Introduction 44
& \* ?/ Y; m U" k: f& ^4.2 Introduction to Text Mining and NLP 458 n6 n8 V6 ^" E& w- V
4.3 Databases and Resources for Biomedical Text Mining 472 p/ j+ I, [* I
4.4 Text Mining and Protein–Protein Interactions 50
/ i' z% \' f7 T4.5 Other Text-Mining Applications in Genomics 55
- u5 u4 l- P y- O4.6 The Future of NLP in Biomedicine 56! W9 h! n" S. e
Acknowledgements 56& O. y5 s8 j. n) V' G- c5 K
References 56( [1 w$ s* c5 b. q( O
5 Protein Interaction Prediction by Integrating Genomic
8 h) V' J, I( I2 \2 \ d( i. {% HFeatures and Protein Interaction Network Analysis 61
8 K+ \! b7 J1 D. y7 a# e% q2 R% D) _Long J. Lu, Yu Xia, Haiyuan Yu, Alexander Rives, Haoxin Lu,$ E, U% f6 N! g( ^/ `6 l
Falk Schubert and Mark Gerstein
1 o0 |! t/ c5 M: r+ v5.1 Introduction 62
( u: c1 n$ s1 o( M3 m* R( @+ H5.2 Genomic Features in Protein Interaction Predictions 63
, S5 `# K7 j. g- P5.3 Machine Learning on Protein–Protein Interactions 67
' x7 k- ^# V) q$ C j5.4 The Missing Value Problem 73
- N# H* B/ b% `! |" p. `5.5 Network Analysis of Protein Interactions 75- j d2 J: J4 o
5.6 Discussion 79
3 K0 O2 c9 @$ z( T6 nReferences 80
! _" z/ l# j9 W& J" E2 A1 K6 Integration of Genomic and Phenotypic Data 83
. A% ^& G; i# A e a jAmanda Clare. @" c, F3 ^# @% U9 b, S7 i# Q
6.1 Phenotype 83
; R- I$ r W6 j( A% Z6.2 Forward Genetics and QTL Analysis 85
7 q' M; j# I- l6 H6.3 Reverse Genetics 87
3 I' p0 m( b8 c* `$ K6.4 Prediction of Phenotype from Other Sources of Data 88+ {7 M; c! A* o P4 e8 k
6.5 Integrating Phenotype Data with Systems Biology 90
4 z! A+ s8 x6 N2 `( R# O- W/ g4 J6.6 Integration of Phenotype Data in Databases 93
0 a& U3 i- ]/ M1 n, M6 N+ u! s6.7 Conclusions 952 a$ K. t# |6 l9 D1 b; T( e
References 952 W& H8 a1 V7 _& X- p, |
7 Ontologies and Functional Genomics 99; c( V; P- L+ j+ ]( z
Fa´tima Al-Shahrour and Joaquı´n Dopazo0 S/ A; G6 R0 p* S3 T |6 G
7.1 Information Mining in Genome-Wide Functional Analysis 99
" a2 e4 Z: P3 V# n7.2 Sources of Information: Free Text Versus Curated Repositories 100/ v' M6 r K/ c+ c/ c b3 v+ F$ h
7.3 Bio-Ontologies and the Gene Ontology in Functional Genomics 101
- i9 |% o; U- G+ s0 r ] m# N7.4 Using GO to Translate the Results of Functional Genomic Experiments into
5 b2 Y/ B( X" t% zBiological Knowledge 103# C9 W" ~& Q' i0 h) W/ N
7.5 Statistical Approaches to Test Significant Biological Differences 104. J! M% c" [* K* Q5 `/ n, F
7.6 Using FatiGO to Find Significant Functional Associations
4 m& E: A X: t( [% N1 rin Clusters of Genes 106
( k+ I6 p8 R& J' }% q+ V, h; ?9 l9 _7.7 Other Tools 107* N# }* o" o: u: p5 ~2 C. W% h. B
7.8 Examples of Functional Analysis of Clusters of Genes 108
6 @& _, x0 `+ X4 P9 v' j2 t7.9 Future Prospects 110( j% a& x3 V5 x3 O0 Y9 t% ?
References 110( h# g# P0 w8 x7 B* ]3 r
8 The C. elegans Interactome: its Generation and Visualization 113
$ ~+ p% S# I9 p% LAlban Chesnau and Claude Sardet
$ e& Q# P1 f7 V' U: W4 Y8.1 Introduction 113; A% R6 ^3 n2 i2 P
8.2 The ORFeome: the first step toward the interactome of C. elegans 116& e" A' _7 S, x" l G0 K
8.3 Large-Scale High-Throughput Yeast Two-Hybrid Screens to Map the C. elegans
* X2 t( w6 C; @- g3 C) j% PProtein–Protein Interaction (Interactome) Network: Technical Aspects 118- T" a' J$ g- K6 w, |7 W( a1 Z# X. x
8.4 Visualization and Topology of Protein–Protein Interaction Networks 121
4 p. n4 c1 B4 |" a3 Z. Z! d) N8 l8.5 Cross-Talk Between the C. elegans Interactome and other Large-Scale
! n3 H/ a0 R; z) D6 C) r3 G' X, pGenomics and Post-Genomics Data Sets 123
# u/ Q6 e, g6 C! b4 ~6 ]! b8.6 Conclusion: From Interactions to Therapies 129' i! ]2 ?. Y1 `" H h. |' [) M
References 130; q$ ]% T X, g) ~) i3 g
SECTION III INTEGRATIVE DATA MINING AND
- ?1 h1 y2 S* H' ?/ H% k) sVISUALIZATION – EMPHASIS ON
6 g- w$ n: U# \3 e3 [+ aCOMBINATION OF MULTIPLE
" \( R( J ~4 {PREDICTION MODELS AND METHODS 135
: O2 _+ N2 H$ B" r" h; p9 S: N* d9 Integrated Approaches for Bioinformatic Data Analysis: T k* z0 o; U: d7 ?
and Visualization – Challenges, Opportunities0 C8 f0 {9 V8 {1 h# A0 B
and New Solutions 137( v5 t' T$ C" }# x$ h# J; v* `: R
Steve R. Pettifer, James R. Sinnott and Teresa K. Attwood
4 ^" c/ b, }+ ^/ |" Y9 ]9.1 Introduction 137) G$ a& w7 e5 p. @
9.2 Sequence Analysis Methods and Databases 139
% D- ?$ A4 U# H7 [" M) |9.3 A View Through a Portal 141/ e- `) d. U0 v+ g
9.4 Problems with Monolithic Approaches: One Size Does Not Fit All 142
- c7 o7 \% F4 a: s# s9.5 A Toolkit View 143. R( Q5 a" {. |2 N# Y& v
9.6 Challenges and Opportunities 145( y, w4 Y! ~; ?5 y7 c' J$ N# u
9.7 Extending the Desktop Metaphor 147
2 Q" `1 D& U4 h; v& H! P. p9.8 Conclusions 1516 c4 L2 y" C2 r$ E
Acknowledgements 151
4 C. l1 k7 t+ x4 v" rReferences 152
# T! b2 [* E2 m& S! [5 @! H! ?8 w# E2 {10 Advances in Cluster Analysis of Microarray Data 153 |. |* [4 f! k; B4 I% E& \4 v
Qizheng Sheng, Yves Moreau, Frank De Smet, Kathleen Marchal; y* l& [% P( L$ A5 h5 j
and Bart De Moor
h. R J+ a, v5 y7 }9 X6 W10.1 Introduction 153, `9 z4 O/ w( \+ z" Y: k
10.2 Some Preliminaries 155: j7 Q2 X* g- ^* M8 A) C
10.3 Hierarchical Clustering 1578 p1 ^* T+ E, [: ?
10.4 k-Means Clustering 159
$ _6 S: T1 K. u0 s: A10.5 Self-Organizing Maps 159
# S! ^6 q6 i6 @4 j. K, _ u) [10.6 A Wish List for Clustering Algorithms 160
* z! n7 y2 u* _ A' m% l10.7 The Self-Organizing Tree Algorithm 161
: ?: t' c& o' ^& I( k! n+ I1 p10.8 Quality-Based Clustering Algorithms 162
) w$ V0 o3 c9 K3 |10.9 Mixture Models 163
/ }. z6 e$ d+ C& r7 m% S10.10 Biclustering Algorithms 166
9 M& r+ D1 X$ q3 j, a0 B9 s& J10.11 Assessing Cluster Quality 168
, ], h! V. x) K; w' N* `10.12 Open Horizons 1700 n" z7 N& [9 M: I) M5 Z4 [, I
References 171
, j$ h, T; i8 V" g- S* ~! [; q11 Unsupervised Machine Learning to Support Functional
5 h& V$ o! M1 n9 E8 k3 K3 P- ^Characterization of Genes: Emphasis on Cluster$ R8 _1 G/ g* z4 r& K$ H+ V; [$ _
Description and Class Discovery 175
' l9 o4 m0 K' Q: ?* A4 f A! b- hOlga G. Troyanskaya
3 g Z/ j( V2 @' i9 Q11.1 Functional Genomics: Goals and Data Sources 175
5 _6 I1 J. t' P7 Y2 w- ^9 U' x! Y11.2 Functional Annotation by Unsupervised Analysis of Gene7 o! h3 ], q% k
Expression Microarray Data 177
3 h$ y# Z5 `0 y* C11.3 Integration of Diverse Functional Data For Accurate Gene Function
0 y' G8 g* B: }. a4 I5 u. L0 }Prediction 179
9 Y. i5 ] w( b4 X! u11.4 MAGIC – General Probabilistic Integration of Diverse Genomic Data 1800 Q1 D( Y. d; X" L% f. U! x
11.5 Conclusion 188
% A5 [$ A! t9 _, UReferences 1891 f2 f2 `1 X5 ]5 X- n
12 Supervised Methods with Genomic Data: a Review
3 ^% k# Y6 J" r2 V3 W. Tand Cautionary View 193( i/ R" w6 d. R2 D- ^" [
Ramo´n Dı´az-Uriarte
/ R# j4 h7 h0 c. C6 A12.1 Chapter Objectives 193& z* K. H1 N; W" }# v
12.2 Class Prediction and Class Comparison 194
7 Y. J; M/ f3 {2 l/ G- e# |6 E+ w12.3 Class Comparison: Finding/Ranking Differentially Expressed Genes 194
# d7 b* C8 r/ z12.4 Class Prediction and Prognostic Prediction 198
- u; ?9 }) {3 N) O( i12.5 ROC Curves for Evaluating Predictors and Differential Expression 2012 m3 d% ^9 O1 v
12.6 Caveats and Admonitions 203' z/ s) N; ~& F; H" y" I2 H
12.7 Final Note: Source Code Should be Available 209
: _: p' |# G+ d7 }* C! b! ?/ f" YAcknowledgements 210
) i7 \6 ?, \. ~0 j2 L$ ?2 zReferences 2108 `% C. U/ H2 C# [! V, Y
13 A Guide to the Literature on Inferring Genetic Networks
; V* h2 i: c9 Y* _. pby Probabilistic Graphical Models 2151 ^( F ?$ w* q! F7 ?) A G! B
Pedro Larran˜aga, In˜aki Inza and Jose L. Flores& e7 f% {) x8 g. Q m& w7 D9 k4 Q
13.1 Introduction 215
5 E7 X! R0 }% `. b: ~13.2 Genetic Networks 216- K- Q5 ?/ z5 ]& q# B3 U" E6 p8 c7 g8 J
13.3 Probabilistic Graphical Models 2182 w, V" k- D. O+ c* B! h( j7 B8 Y
13.4 Inferring Genetic Networks by Means of Probabilistic Graphical Models 229, g: q$ A9 [3 \# c8 R; f
13.5 Conclusions 2342 j4 ~1 z' N- ~& Q/ |( B. s6 I* Q
Acknowledgements 2351 C- A3 }/ d3 w/ O$ l: y
References 235
8 x2 I2 A8 r8 m; t D) h; ^14 Integrative Models for the Prediction and Understanding
( L# p! C% W0 D8 T! I& uof Protein Structure Patterns 2393 w) x% Y# O. \$ m" d. r
Inge Jonassen
& i5 V1 P2 y2 x7 h) y14.1 Introduction 239
5 T5 @! {0 u- ] \# D6 Z14.2 Structure Prediction 241" u; U) F7 H% y+ x. x
14.3 Classifications of Structures 244
7 f! y/ v' i7 G9 j4 L2 B14.4 Comparing Protein Structures 246
8 Q% K& f# R# A; j14.5 Methods for the Discovery of Structure Motifs 249
; r9 l c2 a# L- n! W$ g {14.6 Discussion and Conclusions 252
; W+ p( S) j7 S# v) R7 RReferences 254% k f1 h& P# }' ]+ c
7 k8 q" A3 S6 l# }1 Q+ m. w8 `1 H
[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
