标题: PDF电子书：Data Analysis and Visualization in Genomics and Proteomics [打印本页] 作者: shinejesse 时间: 2010-9-19 18:29 标题: PDF电子书：Data Analysis and Visualization in Genomics and Proteomics
本帖最后由 细胞海洋 于 2010-9-19 20:20 编辑 b+ P, P2 u( z8 W3 c/ [" T) f( y3 l: O
SECTION I INTRODUCTION – DATA DIVERSITY AND INTEGRATION 1 0 W& f; ]' t3 Y: t- a( ~1 Integrative Data Analysis and Visualization: Introduction / r0 A9 N- T! k1 j5 S2 C& oto Critical Problems, Goals and Challenges 31 j0 H4 y$ c3 n7 ^
Francisco Azuaje and Joaquı´n Dopazo0 E1 t4 ]3 ?# }$ e, c9 W$ U- z
1.1 Data Analysis and Visualization: An Integrative Approach 3 0 `$ U# v5 g& u3 b( V; B1.2 Critical Design and Implementation Factors 5 ) ^/ k6 m; k2 o# P: b2 U3 e1.3 Overview of Contributions 8 . ^! F6 { s1 F9 UReferences 9 " {; M$ L* P2 y, T6 \2 Biological Databases: Infrastructure, Content3 r# I$ }& G) T; _/ \( |, [
and Integration 118 b# W$ y, h8 |% \
Allyson L. Williams, Paul J. Kersey, Manuela Pruess 5 G [) \4 [! P* eand Rolf Apweiler / }6 e# ]( b' {! R# b7 m7 E+ O2.1 Introduction 11 $ q: K& [/ M+ N) J; F# _& }2.2 Data Integration 12 # p5 U% n& W" E1 M2.3 Review of Molecular Biology Databases 17 ) e* Y. B) c9 X( i9 s, p& \& m% X2.4 Conclusion 23 0 K. Y0 x- E9 Q! oReferences 26 . f# x, b: [- |, \* G3 Data and Predictive Model Integration: an Overview: |, V+ \6 G0 m8 D# t8 v
of Key Concepts, Problems and Solutions 29" u- n% W# ^; N# q7 x+ g q
Francisco Azuaje, Joaquı´n Dopazo and Haiying Wang$ P+ f. R& C% b
3.1 Integrative Data Analysis and Visualization: Motivation and Approaches 29 & V( x% I. ?3 _& V3.2 Integrating Informational Views and Complexity for Understanding Function 31* o: j/ b8 O; j7 Q5 @
3.3 Integrating Data Analysis Techniques for Supporting Functional 8 i' O' u4 {2 C8 ^. QSECTION II INTEGRATIVE DATA MINING AND VISUALIZATION – D5 r$ j+ F4 {. O" b
EMPHASIS ON COMBINATION OF MULTIPLE * s) ~* ~7 M! E9 [6 V! M& _DATA TYPES 41 + o$ ]% t+ h+ @4 Applications of Text Mining in Molecular Biology, from Name ; Z; @- s/ E4 u* zRecognition to Protein Interaction Maps 43 ' r: e) M; v% |0 VMartin Krallinger and Alfonso Valencia! H8 C+ ]0 N: ?
4.1 Introduction 449 P# Z/ s& t4 R6 B" B7 ?: b$ R4 p
4.2 Introduction to Text Mining and NLP 45 2 i" J( O3 X6 k4.3 Databases and Resources for Biomedical Text Mining 479 H9 @. G% Z' O+ s* \+ ~
4.4 Text Mining and Protein–Protein Interactions 507 Y4 ?* H: e% E0 o3 U
4.5 Other Text-Mining Applications in Genomics 55 . z+ C; W# p* o4.6 The Future of NLP in Biomedicine 56 ! { V4 j/ i$ V5 uAcknowledgements 56: |# W8 @$ D# s! G2 y. c! }
References 56 ' D! w, o' t& `. Y2 [5 Protein Interaction Prediction by Integrating Genomic: U$ o) _3 c) n% w8 i5 O" w
Features and Protein Interaction Network Analysis 61/ k: k K' S& D' Y4 t' k: a! G
Long J. Lu, Yu Xia, Haiyuan Yu, Alexander Rives, Haoxin Lu, Q5 i# m; s( ]9 t& o6 c
Falk Schubert and Mark Gerstein/ i- {; t( V: l7 a/ e: Q
5.1 Introduction 62( h; F8 o& q. r, V J
5.2 Genomic Features in Protein Interaction Predictions 63/ T+ k; v+ h' D, J) q
5.3 Machine Learning on Protein–Protein Interactions 67 b6 Y5 U2 z9 s% f$ A3 u: B; B
5.4 The Missing Value Problem 73; L3 f) {6 ^4 C/ e: }
5.5 Network Analysis of Protein Interactions 75! t; ^) _9 n4 I8 G5 y+ f
5.6 Discussion 79 $ k7 m! w# ` n6 z4 g2 aReferences 80 ! C, N6 |0 V' ^" h5 a" [1 B- o6 Integration of Genomic and Phenotypic Data 836 f$ d9 P' y/ N+ L
Amanda Clare' ?! l( |, ~/ r
6.1 Phenotype 83( A X( }0 B. \' f' e
6.2 Forward Genetics and QTL Analysis 85 8 N. }5 P5 x. K' \6.3 Reverse Genetics 87 / F& z% i5 G6 Z; b5 C' q7 x6.4 Prediction of Phenotype from Other Sources of Data 88 - I2 s/ m. z" L6.5 Integrating Phenotype Data with Systems Biology 90 & b8 H `2 t) ?+ i0 A3 l. f6.6 Integration of Phenotype Data in Databases 93 9 l1 j) u8 y3 z) l6.7 Conclusions 95 $ x& B' B2 z" U. m: ?6 u& O- {References 956 r% i, u% \: S. `" S' C
7 Ontologies and Functional Genomics 99 : {3 [8 J& t4 L' H7 q# k6 [Fa´tima Al-Shahrour and Joaquı´n Dopazo1 n: B* P2 {0 [2 [7 U, W3 w
7.1 Information Mining in Genome-Wide Functional Analysis 99! r1 M* ?& P7 G5 Y
7.2 Sources of Information: Free Text Versus Curated Repositories 1001 S" u, p) |( t% X, }
7.3 Bio-Ontologies and the Gene Ontology in Functional Genomics 101 ! l% j' z q- }8 l3 H7.4 Using GO to Translate the Results of Functional Genomic Experiments into& F2 y. ~7 V* n# S8 M2 P$ T
Biological Knowledge 103, h4 E) v& ?+ k: |. i N
7.5 Statistical Approaches to Test Significant Biological Differences 104 3 C' h) v$ k+ h+ D8 N; r7.6 Using FatiGO to Find Significant Functional Associations & q/ }, ~0 o4 c7 i1 v: O* J0 win Clusters of Genes 106 $ e/ M3 N4 ?. F7.7 Other Tools 1073 e" _: [1 v# e. ?% X5 S' a
7.8 Examples of Functional Analysis of Clusters of Genes 108# F1 q& R7 f4 @
7.9 Future Prospects 110 1 }8 }4 K1 K( Q. q, `References 110 % x% ]8 T2 b/ w/ `' m: C i8 The C. elegans Interactome: its Generation and Visualization 113 ) A6 j: [3 T9 V+ C& Q3 tAlban Chesnau and Claude Sardet9 H% g' G9 t6 ~% Y4 c% q P6 q' l
8.1 Introduction 113$ F; {/ W& q/ A' X/ V$ |
8.2 The ORFeome: the first step toward the interactome of C. elegans 116 7 I2 X" y; b7 Z8.3 Large-Scale High-Throughput Yeast Two-Hybrid Screens to Map the C. elegans3 i5 p/ [0 ^7 ] S, k
Protein–Protein Interaction (Interactome) Network: Technical Aspects 118 3 s( h; \$ H/ @8 q8.4 Visualization and Topology of Protein–Protein Interaction Networks 121 4 A6 T, p- X$ ^) N& {8 L8.5 Cross-Talk Between the C. elegans Interactome and other Large-Scale' l- J8 s/ o$ u8 ]& E& b8 x; R
Genomics and Post-Genomics Data Sets 123/ ^% O F1 d- K [+ M
8.6 Conclusion: From Interactions to Therapies 129 4 Z/ C2 s& c9 A. B @7 PReferences 130 7 D$ n9 f# L( f2 a4 E* r4 t4 J" qSECTION III INTEGRATIVE DATA MINING AND ( t# s# Z% E* Z g& d5 _" iVISUALIZATION – EMPHASIS ON" s3 ]1 y. q# t* d& s
COMBINATION OF MULTIPLE" q6 M6 e# L) K
PREDICTION MODELS AND METHODS 135. m; E7 _0 t, ?& x: e: y
9 Integrated Approaches for Bioinformatic Data Analysis& |/ I; h7 U x, h
and Visualization – Challenges, Opportunities $ y8 K+ G: ?2 D/ Eand New Solutions 137 : u$ ~" Y9 Q7 v! b3 \# L. TSteve R. Pettifer, James R. Sinnott and Teresa K. Attwood $ `: x9 V8 ^: B8 \3 m+ M9.1 Introduction 137 6 ?9 S2 T" o! L! M, r9.2 Sequence Analysis Methods and Databases 139 3 u" ^( d% E. v9.3 A View Through a Portal 141+ s* k8 K9 P+ v, q$ u* Z( c, H6 ]( I
9.4 Problems with Monolithic Approaches: One Size Does Not Fit All 142! j4 k5 g+ D$ p/ K/ L* Y! e
9.5 A Toolkit View 143 6 \3 R0 W' x4 N/ ?, z9.6 Challenges and Opportunities 145 % v2 N, r, d+ f o9 _, w1 F9.7 Extending the Desktop Metaphor 147 : [2 r( _% f2 z1 p9.8 Conclusions 151 9 ?$ c$ u: b7 RAcknowledgements 151$ {2 G' ?% v5 G0 s! O: B/ U$ m
References 152 " [/ _& _/ o* Z, v( P10 Advances in Cluster Analysis of Microarray Data 153 / `7 v& _0 h* S. XQizheng Sheng, Yves Moreau, Frank De Smet, Kathleen Marchal ' f; f! H; F* S% c- _; [+ rand Bart De Moor; |( e2 _! O4 T' T8 h+ Z
10.1 Introduction 153 8 c4 p6 ~0 T. Z6 j2 H10.2 Some Preliminaries 155( c/ T5 x+ a o5 q- ~
10.3 Hierarchical Clustering 157 ' E% j. v4 \" j1 z! F10.4 k-Means Clustering 159: x& j/ {6 ^; k- `& o
10.5 Self-Organizing Maps 159! G' E2 d2 P( s/ o& k; A
10.6 A Wish List for Clustering Algorithms 160" T. B8 o$ P" Q+ Q, r5 g
10.7 The Self-Organizing Tree Algorithm 161 8 |* o* u4 n& E9 e1 k4 Y% x10.8 Quality-Based Clustering Algorithms 162* Z" G3 a$ D& n, \1 X% r8 S
10.9 Mixture Models 163 - w5 u U+ x, K' F, M! N: o9 V% f2 F0 C10.10 Biclustering Algorithms 166+ u1 k/ U, x; b$ J1 @8 b
10.11 Assessing Cluster Quality 168 ( j2 W7 w) C) D* h. F10.12 Open Horizons 170% l9 c, P% [$ O" {6 n! [, \2 d% [
References 171 0 T3 q; D# j3 q- L u! M, ]11 Unsupervised Machine Learning to Support Functional " P8 U: N9 n$ n: j- p, c# n& r6 s3 KCharacterization of Genes: Emphasis on Cluster0 a2 R/ L0 `7 ]! |2 s8 ?
Description and Class Discovery 1752 s6 n8 ^+ }0 I* N4 E" Z- t
Olga G. Troyanskaya 8 U9 m3 `; B: R: t8 u4 @- O11.1 Functional Genomics: Goals and Data Sources 175 ) w' ?+ m }% F+ t11.2 Functional Annotation by Unsupervised Analysis of Gene 3 {. h1 c/ k- ^; @8 [Expression Microarray Data 1771 x$ w. }1 ~# T4 W, A0 q
11.3 Integration of Diverse Functional Data For Accurate Gene Function5 A0 Y) ^. Z$ Q; y) ~
Prediction 179 ' J7 A+ O3 k; s% n, [11.4 MAGIC – General Probabilistic Integration of Diverse Genomic Data 1800 @1 k! ?3 Y" [- U/ t
11.5 Conclusion 188 ( D E; R* w: e$ wReferences 189 # E1 N, k5 q# y0 V- P1 t12 Supervised Methods with Genomic Data: a Review# M& b- V& [5 x2 e0 e* v+ I
and Cautionary View 1933 ` ]$ H- C& _+ \4 `* Y1 g
Ramo´n Dı´az-Uriarte1 R+ a' G- M& L( V* w
12.1 Chapter Objectives 193 2 S( }" o5 V% N/ \3 N4 Y9 N12.2 Class Prediction and Class Comparison 194 X4 F3 k' g4 G& }- w. O: w" O
12.3 Class Comparison: Finding/Ranking Differentially Expressed Genes 194& q, ^+ B! _0 N7 _
12.4 Class Prediction and Prognostic Prediction 198 + ?6 c4 u% ]1 u2 A12.5 ROC Curves for Evaluating Predictors and Differential Expression 2016 y: y$ U2 E+ e) G4 h! Z
12.6 Caveats and Admonitions 203- Z: }, r0 _: b/ A' K
12.7 Final Note: Source Code Should be Available 209 9 R- B3 K( |. y$ Z4 D7 CAcknowledgements 2103 [' n% J, }; Q _$ K
References 2109 h/ y4 Z( c3 P' n' e B8 [! ?7 D
13 A Guide to the Literature on Inferring Genetic Networks8 Z: m& V3 F6 ?
by Probabilistic Graphical Models 215 . |5 q4 J# t0 P. @) sPedro Larran˜aga, In˜aki Inza and Jose L. Flores& O1 D }' i2 z) n
13.1 Introduction 215 ; _; _5 ~3 a- B; o13.2 Genetic Networks 216 * c6 }- y2 @; e13.3 Probabilistic Graphical Models 218 3 z' `0 |/ U, l2 T13.4 Inferring Genetic Networks by Means of Probabilistic Graphical Models 229+ [- E' ^4 u# i- z
13.5 Conclusions 234 & D; J: c4 E0 G u9 lAcknowledgements 235 4 i1 E( F) k) o3 b0 fReferences 235/ r/ t4 N0 e+ t7 y5 V( _
14 Integrative Models for the Prediction and Understanding % |( b; Q( B% ]2 c3 A. F! f2 ~# Kof Protein Structure Patterns 239 . m* x: L% q5 k* D0 JInge Jonassen2 Y( t# X0 Y0 l* X
14.1 Introduction 2390 x0 B, f' h$ X0 e% @; e" O
14.2 Structure Prediction 241 9 J) l {( C6 g: J% w+ v9 {14.3 Classifications of Structures 244, M2 u( i b8 ?1 F# d, S
14.4 Comparing Protein Structures 246" _8 q8 f! Z# X! T! |
14.5 Methods for the Discovery of Structure Motifs 249 : ~/ ~( t \3 Y+ @( r14.6 Discussion and Conclusions 252 8 S; g" x, o8 i% aReferences 2545 F$ m+ a. `. @' Z. D
7 Z' i) Y" U* ? 作者: madey 时间: 2010-9-25 19:04