Machine learning approaches to bioinformatics:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New Jersey [u.a.]
World Scientific
2010
|
| Schriftenreihe: | Science, engineering, and biology informatics
4 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Klappentext |
| Beschreibung: | XIV, 322 S. graph. Darst. |
| ISBN: | 9789814287302 981428730X |
Internformat
MARC
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| 100 | 1 | |a Yang, Zheng Rong |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Machine learning approaches to bioinformatics |c Zheng Rong Yang |
| 264 | 1 | |a New Jersey [u.a.] |b World Scientific |c 2010 | |
| 300 | |a XIV, 322 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 1 | |a Science, engineering, and biology informatics |v 4 | |
| 650 | 0 | 7 | |a Bioinformatik |0 (DE-588)4611085-9 |2 gnd |9 rswk-swf |
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| 689 | 0 | 1 | |a Bioinformatik |0 (DE-588)4611085-9 |D s |
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Datensatz im Suchindex
| _version_ | 1804143829794684930 |
|---|---|
| adam_text | ítíacHine
learning ^approaches
to bioinfòrmatiesp
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This book covers swide range of subjects in
àpplyingmachine
learning approaches ,
for bioinformatics projects. The book succeeds on two key unique features .
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it
introduces the· most widely used
màchinelearning
approaches in
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and discusses, with evaluations from real
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introduces state-of-the-art bioinformatics
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Unlike most of the bioinformatics books on the market the content coverage is not
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limited to just one subject. A broad spectrum of relevant topics in bioinformatics
І?
r including systematic data mining arid computational systems biology researches
are brought together in this book, thereby offering an
efficient
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Contents
Preface
v
1
Introduction
1
1.1
Brief history of
bioinformatics
З
1.2
Database application in bioinformatics
6
1.3
Web tools and services for sequence homology
,8
Alignment
1.3.1
Web tools and services for protein functional
9
site identification
1.3.2
Web tools and services for other biological data
10
1.4
Pattern analysis
10
1.5
The contribution of information technology
11
1.6
Chapters
, 12
2
Introduction to Unsupervised Learning
15
3
Probability Density Estimation Approaches
24
3.1
Histogram approach
24
3.2
Parametric approach
25
3.3
Non-parametric approach
28
3.3.1
K-nearest neighbour approach
28
3.3.2
Kernel approach
29
Summary
36
4
Dimension Reduction
38
4.1
General
38
к
Machine
Learning Approaches to Bioinformatics
4.2
Principal component analysis
39
4.3
An application of PCA
42
4.4
Multi-dimensional scaling
46
4.5
Application of the Sammon algorithm to gene data
48
Summary
50
5
Cluster Analysis
52
5.1
Hierarchical clustering
52
5.2
K-means
55
5.3
Fuzzy C-means
58
5.4
Gaussian mixture models
60
5.5
Application of clustering algorithms to the Burkholderia
64
pseudomallei gene expression data
Summary
67
6
Self-organising Map
69
6.1
Vector quantization
69
6.2
SOM
structure
73
6.3
SOM
learning algorithm
75
6.4
Using
SOM
for classification
79
6.5
Bioinformatics applications of VQ and
SOM
81
6.5.1
Sequence analysis
81
6.5.2
Gene expression data analysis
83
6.5.3
Metabolite data analysis
· 86
6.6
A case study of gene expression data analysis
86
6.7
A case study of sequence data analysis
88
Summary
90
7
Introduction to Supervised Learning
92
7.1
General concepts
92
7.2
General definition
94
7.3
Model evaluation
96
7.4
Data organisation
101
7.5
Bayes
rule for classification
103
Summary
103
Contents
xi
8
Linear/Quadratic
Discriminant
Analysis and K-nearest
104
Neighbour
8.1
Linear discriminant analysis
104
8.2
Generalised discriminant analysis
109
8.3
K-nearest neighbour 111
8.4
KNN for gene data analysis
118
Summary
118
9
Classification and Regression Trees, Random Forest
120
Algorithm
9.1
Introduction
120
9.2
Basic principle for constructing a classification tree
121
9.3
Classification and regression tree
125
9.4
CART for compound pathway involvement prediction
126
9.5
The random forest algorithm
128
9.6
RF for analyzing Burkholderia pseudomallei gene
129
expression profiles
Summary
132
10
Multi-layer Perceptron
133
10.1
Introduction
133
10.2
Learning theory
137
10.2.1
Parameterization of a neural network
137
10.2.2
Learning rales
137
10.3
Learning algorithms
145
10.3.1
Regression
145
10.3.2
Classification
146
10.3.3
Procedure
147
10.4
Applications to bioinformatics
148
10.4.1
Bio-chemical data analysis
148
10.4.2
Gene expression data analysis
149
10.4.3
Protein structure data analysis
149
10.4.4
Bio-marker identification
150
10.5
A case study on Burkholderia pseudomallei
150
gene expression data
Summary
153
xii
Machine
Learning Approaches to Bioinformatics
11
Basis Function Approach and Vector Machines
· 154
11.1
Introduction
154
11.2
Radial-basis function neural network (RBFNN)
156
11.3
Bio-basis function neural network
162
11.4
Support vector machine
168
11.5
Relevance vector machine
173
Summary
176
12
Hidden Markov Model
177
12.1
Markov model
177
12.2
Hidden Markov model
179
12.2.1
General definition
179
12.2.2
Handling
HMM
183
12.2.3
Evaluation
184
12.2.4
Decoding
188
12.2.5
Learning
189
12.3
HMM
for sequence classification
191
Summary
194
13
Feature Selection
195
13.1
Built-in strategy.
195
13.1.1
Lasso regression
196
13.1.2
Ridge regression
199
13.1.3
Partial least square regression (PLS) algorithm
200
13.2
Exhaustive strategy
204
13.3
Heuristic strategy
-
orthogonal least square approach
204
13.4
Criteria for feature selection
208
13.4.1
Correlation measure
209
13.4.2
Fisher ratio measure
■ 210
13.4.3
Mutual information approach
210
Summary
· 212
14
Feature Extraction (Biological Data Coding)
213
14.1
Molecular sequences
214
14.2
Chemical compounds
215
Contents xiii
14.3 General
definition
216
14.4
Sequence analysis
216
14.4.1
Peptide
feature extraction
216
14.4.2
Whole sequence feature extraction
222
Summary
224
15
Sequence/Structural Bioinformatics Foundation
- 225
Peptide
Classification
15.1
Nitration site prediction
225
15.2
Plant promoter region prediction
230
Summary
237
16
Gene Network
-
Causal Network and Bayesian
238
Networks
16.1
Gene regulatory network
238
16.2
Causal networks, networks, graphs
241
16.3
A brief review of the probability
242
16.4
Discrete Bayesian network
245
16.5
Inference with discrete Bayesian network
246
16.6
Learning discrete Bayesian network
247
16.7
Bayesian networks for gene regulartory networks
247
16.8
Bayesian networks for discovering
peptide
patterns
248
16.9
Bayesian networks for analysing Burkholderia
249
pseudomallei gene data
Summary
252
17
S-Systems
253
17.1
Michealis-Menten change law
253
17.2
S-system
256
17.3
Simplification of an S-system
259
17.4
Approaches for structure identification and parameter
260
estimation
17.4.1
Neural network approach
260
17.4.2
Simulated annealing approach
261
17.4.3
Evolutionary computation approach
262
XIV
Machine
Learning Approaches to Bioinformatics
17.5
Steady-state analysis of an S-system
· · -262
17.6
Sensitivity of an S-system
■>· 267
Summary
■ - : :: ■...:,.·-. ■/ :■ ■ 268
18
Future Directions
и
·■ 269
18.1
Multi-source data
270
18.2
Gene regulatory network construction
r
-272
18.3
Building models using incomplete data
-,
š
274
18.4
Biomarker
detection from gene expression data
· 275
Summary
>■ 278
References
279
Index
319
|
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| author | Yang, Zheng Rong |
| author_facet | Yang, Zheng Rong |
| author_role | aut |
| author_sort | Yang, Zheng Rong |
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| bvnumber | BV037226239 |
| classification_rvk | WC 7700 |
| ctrlnum | (OCoLC)731236039 (DE-599)BVBBV037226239 |
| discipline | Biologie |
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| spelling | Yang, Zheng Rong Verfasser aut Machine learning approaches to bioinformatics Zheng Rong Yang New Jersey [u.a.] World Scientific 2010 XIV, 322 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Science, engineering, and biology informatics 4 Bioinformatik (DE-588)4611085-9 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf 1\p (DE-588)4522595-3 Fallstudiensammlung gnd-content Maschinelles Lernen (DE-588)4193754-5 s Bioinformatik (DE-588)4611085-9 s b DE-604 Erscheint auch als Online-Ausgabe 978-981-4287-31-9 Science, engineering, and biology informatics 4 (DE-604)BV022401500 4 Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=021140016&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=021140016&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Yang, Zheng Rong Machine learning approaches to bioinformatics Science, engineering, and biology informatics Bioinformatik (DE-588)4611085-9 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4611085-9 (DE-588)4193754-5 (DE-588)4522595-3 |
| title | Machine learning approaches to bioinformatics |
| title_auth | Machine learning approaches to bioinformatics |
| title_exact_search | Machine learning approaches to bioinformatics |
| title_full | Machine learning approaches to bioinformatics Zheng Rong Yang |
| title_fullStr | Machine learning approaches to bioinformatics Zheng Rong Yang |
| title_full_unstemmed | Machine learning approaches to bioinformatics Zheng Rong Yang |
| title_short | Machine learning approaches to bioinformatics |
| title_sort | machine learning approaches to bioinformatics |
| topic | Bioinformatik (DE-588)4611085-9 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Bioinformatik Maschinelles Lernen Fallstudiensammlung |
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| volume_link | (DE-604)BV022401500 |
| work_keys_str_mv | AT yangzhengrong machinelearningapproachestobioinformatics |