Bayesian networks in R: with applications in systems biology
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New York [u.a.]
Springer
2013
|
| Schriftenreihe: | Use R!
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Klappentext |
| Beschreibung: | XIII, 157 S. graph. Darst. |
| ISBN: | 1461464455 9781461464457 |
Internformat
MARC
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| 100 | 1 | |a Nagarajan, Radhakrishnan |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Bayesian networks in R |b with applications in systems biology |c Radhakrishnan Nagarajan ; Marco Scutari ; Sophie Lèbre |
| 264 | 1 | |a New York [u.a.] |b Springer |c 2013 | |
| 300 | |a XIII, 157 S. |b graph. Darst. | ||
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Datensatz im Suchindex
| _version_ | 1804150245790056448 |
|---|---|
| adam_text | Use R!
Rdditökrishnan
ïfegarajan
·
Marco Scutari
·
Sophie Lëbre
Bayesian Networks in
R
with Applications in Systems
í
Bayesian Networks in
R
with Applications in Systems Biology introduces the
reader to the essential concepts in Bayesian network modeling and inference in
conjunction with examples in the open-source statistical environment R. The level of
sophistication is gradually increased across the chapters with exercises and solutions
for enhanced understanding and hands-on experimentation of key concepts.
Applications focus on systems biology with emphasis on modeling pathways and
signaling mechanisms from high throughput molecular data. Bayesian networks
have proven to be especially useful abstractions in this regards as exemplified by
their ability to discover new associations while validating known ones. It is also
expected that the prevalence of publicly available high-throughput biological and
healthcare data sets may encourage the audience to explore investigating novel
paradigms using the approaches presented in the book.
Contents
1
Introduction
................................................... 1
1.1
A Brief
Introduction
to Graph Theory
......................... 1
1.1.1
Graphs, Nodes, and Arcs
.............................. 1
1.1.2
The Structure of a Graph
.............................. 2
1.1 .3
Further Reading
..................................... 4
1.2
The
R
Environment for Statistical Computing
................... 4
1.2.1
Base Distribution and Contributed Packages
.............. 4
1.2.2
A Quick Introduction to
R
............................. 5
1.2.3
Further Reading
..................................... 10
Exercises
...................................................... 11
2
Bayesian Networks in the Absence of Temporal Information
........ 13
2.1
Bayesian Networks: Essential Definitions and Properties
.......... 13
2.1.1
Graph Structure and Probability Factorization
............ 13
2.1.2
Fundamental Connections
............................. 15
2.1.3
Equivalent Structures
................................. 15
2.1.4
Markov Blankets
.................................... 16
2.2
Static Bayesian Networks Modeling
........................... 17
2.2.1
Constraint-Based Structure Learning Algorithms
.......... 17
2.2.2
Score-Based Structure Learning Algorithms
.............. 19
2.2.3
Hybrid Structure Learning Algorithms
.................. 20
2.2.4
Choosing Distributions, Conditional Independence
Tests, and Network Scores
............................. 20
2.2.5
Parameter Learning
.................................. 23
2.2.6
Discretization
....................................... 23
2.3
Static Bayesian Networks Modeling with
R
..................... 24
2.3.1
Popular
R
Packages for Bayesian Network Modeling
...... 24
2.3.2
Creating and Manipulating Network Structures
........... 26
2.3.3
Plotting Network Structures
........................... 34
2.3.4
Structure Learning
................................... 35
X1j Contents
2.3.5
Parameter Learning
.................................. 40
2.3.6
Discretization
....................................... 42
2.4
Pearl s Causality
........................................... 44
2.5
Applications to Gene Expression Profiles
....................... 46
2.5.1
Model Averaging
.................................... 47
2.5.2
Choosing the Significance Threshold
.................... 51
2.5.3
Handling Interventional Data
.......................... 53
Exercises
...................................................... 56
3
Bayesian Networks in the Presence of Temporal Information
........ 59
3.1
Time Series and Vector Auto-Regressive Processes
.............. 59
3.1.1
Univariate Time Series
................................ 59
3.1.2
Multi
variate
Time
Senes..............................
60
3.2
Dynamic Bayesian Networks: Essential Definitions and Properties
. 63
3.2.1
Definitions
.......................................... 63
3.2.2
Dynamic Bayesian Network Representation
of
a VAR
Process
.................................... 66
3.3
Dynamic Bayesian Network Learning Algorithms
............... 67
3.3.1
Least Absolute Shrinkage and Selection Operator
......... 67
3.3.2
James-Stein Shrinkage
............................... 68
3.3.3
First-Order Conditional Dependencies Approximation
..... 68
3.3.4
Modular Networks
................................... 69
3.4
Non-homogeneous Dynamic Bayesian Network Learning
......... 69
3.5
Dynamic Bayesian Network Learning with
R
................... 72
3.5.1
Multivariate Time Series Analysis
...................... 72
3.5.2
LASSO Learning: lars and simone
..................... 74
3.5.3
Other Shrinkage Approaches: GeneNet, G1DBN
......... 78
3.5.4
Non-homogeneous Dynamic Bayesian Network
Learning: ARTIVA
.................................. 80
Exercises
...................................................... 81
4
Bayesian Network Inference Algorithms
.......................... 85
4.1
Reasoning Under Uncertainty
................................ 85
4.1.1
Probabilistic Reasoning and Evidence
................... 85
4.1.2
Algorithms for Belief Updating: Exact and Approximate
Inference
........................................... 87
4.1.3
Causal Inference
..................................... 90
4.2
Inference in Static Bayesian Networks
......................... 91
4.2.1
Exact Inference
...................................... 91
4.2.2
Approximate Inference
............................... 93
4.3
Inference in Dynamic Bayesian Networks
...................... 94
Exercises
......................................................100
Contents
S
Parallel Computing
for Bayesian
Networks ..............
5.1
Foundations of
Parallel Computing...................
5.2 Parallel Programming in
R
..........................
5.3
Applications to Structure and Parameter Learning
......
5.3.1
Constraint-Based Structure Learning Algorithms
.
5.3.2
Score-Based Structure Learning Algorithms
.....
5.3.3
Hybrid Structure Learning Algorithms
.........
5.3.4
Parameter Learning
.........................
5.4
Applications to Inference Procedures
.................
5.4.1
Bootstrap
..................................
5.4.2
Cross-Validation
............................
5.4.3
Conditional Probability Queries
...............
Exercises
.............................................
Solutions
............
References
...........
Index
................
103
103
105
108
109
112
114
115
115
115
117
120
123
125
149
155
|
| any_adam_object | 1 |
| author | Nagarajan, Radhakrishnan Scutari, Marco 1982- Lèbre, Sophie |
| author_GND | (DE-588)1136913076 |
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| author_variant | r n rn m s ms s l sl |
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| bvnumber | BV040942539 |
| classification_rvk | QH 233 SK 830 WC 7700 |
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| discipline | Biologie Mathematik Wirtschaftswissenschaften |
| format | Book |
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| indexdate | 2024-07-10T00:35:52Z |
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| isbn | 1461464455 9781461464457 |
| language | English |
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| title | Bayesian networks in R with applications in systems biology |
| title_auth | Bayesian networks in R with applications in systems biology |
| title_exact_search | Bayesian networks in R with applications in systems biology |
| title_full | Bayesian networks in R with applications in systems biology Radhakrishnan Nagarajan ; Marco Scutari ; Sophie Lèbre |
| title_fullStr | Bayesian networks in R with applications in systems biology Radhakrishnan Nagarajan ; Marco Scutari ; Sophie Lèbre |
| title_full_unstemmed | Bayesian networks in R with applications in systems biology Radhakrishnan Nagarajan ; Marco Scutari ; Sophie Lèbre |
| title_short | Bayesian networks in R |
| title_sort | bayesian networks in r with applications in systems biology |
| title_sub | with applications in systems biology |
| topic | Bayes-Netz (DE-588)4567228-3 gnd Bioinformatik (DE-588)4611085-9 gnd |
| topic_facet | Bayes-Netz Bioinformatik |
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