Network inference in molecular biology: a hands-on framework
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
New York, NY [u.a.]
Springer
2012
|
| Series: | SpringerBriefs in electrical and computer engineering
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | IX, 100 S. graph. Darst. |
| ISBN: | 9781461431121 |
Staff View
MARC
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| 245 | 1 | 0 | |a Network inference in molecular biology |b a hands-on framework |c Jesse M. Lingeman ; Dennis Shasha |
| 264 | 1 | |a New York, NY [u.a.] |b Springer |c 2012 | |
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Record in the Search Index
| _version_ | 1804150247460438016 |
|---|---|
| adam_text | Contents
Overview of Network Inference
.................................. ]
1.
1 Inferring Causality
......................................... 1
1.1.1 Basics of Genomics
.................................. 2
1.
1
.2
Observation, Intervention, and Inference
................. 3
1.1.3
The
Dala
........................................... 4
1.1.4
Data Sources
........................................ 4
1.1.5
Basic Data
Meines................................... 5
1.1.6
How to Read This Book
............................... 8
1.17
How to Use the Software
.............................. 8
Step
1:
Clustering Data
......................................... 11
2.1
Introduction
............................................... 1 ]
2.1.1
Clustering
.......................................... ]]
2.1.2
Biclustenng
......................................... 12
2.2
cMonkey
.................................................. 12
2.2.1
What it Does
......................................... 12
2.2.2
The Data
............................................ 12
2.2.3
The Strategy
...........,. ,........................... 12
2.2.4
Walkthrough Example on Toy Data
.......................... 16
2.3
Factor Analysis for Biciusier Acquisition (FABIA)
.............. 18
2.3.1
What it Does
........................................... 18
2.3.2
The Data
............................................. 19
2.3.3
The Strategy
......................................... 19
2.3.4
Walkthrough Example on Toy Data
..................... 21
Step
2:
Use Steady State Data for Network Inference
............... 23
3.1
Introduction
............................................... 23
3 2
Median-Corrected Z-Scores (MCZ)
........................... 23
3.2.1
What it Does
........................................ 23
3.2.2
The Data
............................................ 24
3.2.3
The Strategy
........................................ 24
vin
Contents
3.2,4
Performance on Examples
............................. 25
3.3
.Network Identification by Multiple Regression (NIR)
............ 28
3.3. .1
What it Does
........................................ 28
3.3.2
The Data
........................................... 28
3.3.3
The Strategy
........................................ 29
3.3.4
Performance on Examples
............................. 30
3.4
Gene Network Inference with Ensemble of trees (GENTE3)
....... 34
3.4.1
What it Does
........................................ 34
3.4.2
The Data
........................................... 34
3.4.3
The Strategy
........................................ 34
3.4.4
Performance on Examples
............................. 37
3.5
Contexi Likelihood of Relatedness (CLR)
...................... 41
3-5.1
What it Docs
........................................ 41
3.5.2
The Data
........................................... 41
3.5.3
The Strategy
........................................ 41
3.5.4
Performance on Examples
............................. 42
3.6
Semidefinite
Programming
................................... 46
3.6.1
What it does
........................................ 46
3.6.2
The Data
........................................... 46
3.6.3
The Strategy
........................................ 46
3.6.4
Performance on Examples
............................. 48
4
Step
3:
Using Time-Series Data
.................................. 51
4.1
Introduction
............................................... 51
4.2
Time-Delay ARACNE
...................................... 51
4.2.1
Whai It Does
........................................ 5
J
4.2.2
The Data
........................................... 52
4.2.3
The Strategy
........................................ 52
4.2.4
Performance on Examples
............................. 54
4.3
Time-Lagged Context Likelihood of Relatedness (tlCLR)
......... 56
4.3.1
What it Does
..........,.................,............ 56
4.3.2
The Data
........................................... 56
4.3.3
The Strategy
........................................ 56
4.3.4
Performance on Examples
............................. 58
4.4
Inferelator
................................................. 62
4.4.1
What it Does
........................................ 62
4.4.2
The Data
........................................... 62
4.4.3
The Strategy
........................................ 62
4.4.4
Performance on examples
............................. 64
4.5
Dynamic Factor Graphs
(DFG)............................... 65
4.5.1
What
n
does
........................................ 65
4.5 2
The Data
........................................... 65
4.5.3
The Strategy
........................................ 65
4.5.4
Performance on examples
............................. 67
4.6
Bayesian Network Inference with Java Objects (BANJO)
......... 72
Contenls
їх
4.6-
1 What it does
........................................ 72
4.6.2
The
Dau
........................................... 72
4.6.3
The Strategy
....................................... 73
4.6.4
Performance on Examples
............................ 74
5
Step
4:
Pipelines
.............................................. 77
5.1.
Consensus Step: Combining Results of Different Approaches
...... 77
5.2
Creating Pipelines and Ensemble Networks
..................... 77
5.3
Pipeline
.1 :
Steady State algorithm
+
Dynamic Factor Graphs
...... 78
5.3.1
What it. does
........................................ 78
5.3.2
The Strategy
........................................ 78
5.3.3
Performance on examples
............................. 78
5.4
Pipeline
2:
Inferelator
2.0................................... 82
5.4.
J
What
U
does
........................................ 82
5.4.2
The Strategy
........................................ 82
5.4.3
Performance on examples
............................. 83
5.5
Ensemble
1 :
Voting
........................................ 86
5.5.1
What it does
.................................... 86
5.5.2
The Strategy
.................................... 86
5.5.3
Performance on examples
........................... 87
5.6
Ensemble
2:
Simulated Annealing
............................. 90
5.6.1
Whal it does
...................................... 90
5.6.2
The Data
.......................................... 90
5.6.3
The Strategy
...................................... 90
5.6.4
Performance on examples
............................. 91
5.7
Conclusions
............................................... 95
References
..................................................... 97
Index
............................................................. 99
|
| any_adam_object | 1 |
| author | Lingeman, Jesse M. Shasha, Dennis Elliott 1955- |
| author_GND | (DE-588)123736048 |
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| id | DE-604.BV040943796 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T00:35:53Z |
| institution | BVB |
| isbn | 9781461431121 |
| language | English |
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| oclc_num | 844060539 |
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| owner_facet | DE-355 DE-BY-UBR |
| physical | IX, 100 S. graph. Darst. |
| publishDate | 2012 |
| publishDateSearch | 2012 |
| publishDateSort | 2012 |
| publisher | Springer |
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| series2 | SpringerBriefs in electrical and computer engineering |
| spelling | Lingeman, Jesse M. Verfasser aut Network inference in molecular biology a hands-on framework Jesse M. Lingeman ; Dennis Shasha New York, NY [u.a.] Springer 2012 IX, 100 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier SpringerBriefs in electrical and computer engineering Informatik Computer science Computer software Bioinformatics Computer Science Molekularbiologie (DE-588)4039983-7 gnd rswk-swf Bioinformatik (DE-588)4611085-9 gnd rswk-swf Molekularbiologie (DE-588)4039983-7 s Bioinformatik (DE-588)4611085-9 s DE-604 Shasha, Dennis Elliott 1955- Verfasser (DE-588)123736048 aut Erscheint auch als Online-Ausgabe 978-1-4614-3113-8 Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025922494&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Lingeman, Jesse M. Shasha, Dennis Elliott 1955- Network inference in molecular biology a hands-on framework Informatik Computer science Computer software Bioinformatics Computer Science Molekularbiologie (DE-588)4039983-7 gnd Bioinformatik (DE-588)4611085-9 gnd |
| subject_GND | (DE-588)4039983-7 (DE-588)4611085-9 |
| title | Network inference in molecular biology a hands-on framework |
| title_auth | Network inference in molecular biology a hands-on framework |
| title_exact_search | Network inference in molecular biology a hands-on framework |
| title_full | Network inference in molecular biology a hands-on framework Jesse M. Lingeman ; Dennis Shasha |
| title_fullStr | Network inference in molecular biology a hands-on framework Jesse M. Lingeman ; Dennis Shasha |
| title_full_unstemmed | Network inference in molecular biology a hands-on framework Jesse M. Lingeman ; Dennis Shasha |
| title_short | Network inference in molecular biology |
| title_sort | network inference in molecular biology a hands on framework |
| title_sub | a hands-on framework |
| topic | Informatik Computer science Computer software Bioinformatics Computer Science Molekularbiologie (DE-588)4039983-7 gnd Bioinformatik (DE-588)4611085-9 gnd |
| topic_facet | Informatik Computer science Computer software Bioinformatics Computer Science Molekularbiologie Bioinformatik |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025922494&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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