Structural bioinformatics: an algorithmic approach
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Boca Raton, Fla.[u.a.]
CRC Press
2009
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| Schriftenreihe: | Chapman & Hall/CRC mathematical and computational biology series
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XXIII, 406 S. Ill., graph. Darst. |
| ISBN: | 9781584886839 |
Internformat
MARC
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| 100 | 1 | |a Burkowski, Forbes J. |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Structural bioinformatics |b an algorithmic approach |c Forbes J. Burkowski |
| 264 | 1 | |a Boca Raton, Fla.[u.a.] |b CRC Press |c 2009 | |
| 300 | |a XXIII, 406 S. |b Ill., graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Chapman & Hall/CRC mathematical and computational biology series | |
| 650 | 4 | |a Computational Biology | |
| 650 | 4 | |a Models, Molecular | |
| 650 | 4 | |a Protein Conformation | |
| 650 | 4 | |a Structural bioinformatics | |
| 650 | 0 | 7 | |a Algorithmische Programmierung |0 (DE-588)4293504-0 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Quartärstruktur |0 (DE-588)4400205-1 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Bioinformatik |0 (DE-588)4611085-9 |2 gnd |9 rswk-swf |
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| 689 | 0 | 1 | |a Quartärstruktur |0 (DE-588)4400205-1 |D s |
| 689 | 0 | 2 | |a Algorithmische Programmierung |0 (DE-588)4293504-0 |D s |
| 689 | 0 | |C b |5 DE-604 | |
| 856 | 4 | 2 | |m Digitalisierung UB Regensburg |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015606595&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
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Datensatz im Suchindex
| _version_ | 1804136458462691328 |
|---|---|
| adam_text | Table
of
Contents
Preface
xix
Acknowledgments
xxiii
Chapter
1 ■
The Study of Structural Bioinformatics
1
1.1
MOTIVATION
1
1.2
SMALL BEGINNINGS
4
1.3
STRUCTURAL BIOINFORMATICS AND THE
SCIENTIFIC METHOD
5
1.3.1
Three Realms: Nature, Science,
and Computation
6
1.3.2
Hypothesis, Model, and Theory
8
1.3.3
Laws, Postulates, and Assumptions
12
1.3.4
Model Theory and Computational Theory
13
1.3.5
Different Assumptions for Different Models
14
1.4
A MORE DETAILED PROBLEM ANALYSIS:
FORCE FIELDS
15
1.4.1
Nature
16
1.4.2
Science
16
1.4.2.1
Energy Terms for Bonded Atoms
16
1.4.2.2
Energy Terms for Nonbonded Atoms
19
1.4.2.3
Total Potential Energy
21
1.4.3
Computation
21
■
Table
of Contents
1.5
MODELING ISSUES
25
1.5.1
Rashomon
26
1.5.2
Ockham
26
1.5.3
Bellman
27
1.5.4
Interpretability
28
1.5.5
Refutability
29
1.5.6
Complexity and Approximation
29
1.6
SOURCES OF ERROR
32
1.7
SUMMARY
33
1.8
EXERCISES
34
REFERENCES
36
Chapter
2 ■
Introduction to Macromolecular Structure
37
2.1
MOTIVATION
37
2.2
OVERVIEW OF PROTEIN STRUCTURE
38
2.2.1
Amino
Acids and Primary Sequence
38
2.2.2
Secondary Structure
44
2.2.2.1
Alpha Helices
44
2.2.2.2
Beta Strands
47
2.2.2.3
Loops
52
2.2.3
Tertiary Structure
53
2.2.3.1
What Is Tertiary Structure?
54
2.2.3.2
The Tertiary Structure ofMyoglobin
54
2.2.3.3
Tertiary Structure Beyond the Binding
Pocket
58
2.2.4
Quaternary Structure
64
2.2.5
Protein Functionality
67
2.2.6
Protein Domains
68
2.3
AN OVERVIEW OF
RNA
STRUCTURE
70
2.3.1
Nucleotides and
RNA
Primary Sequence
71
2.3.2
RNA
Secondary Structure
72
2.3.3
RNA
Tertiary Structure
75
Table
of Contents
■ xi
2.4
EXERCISES
78
REFERENCES
80
Chapter
3
»Data Sources, Formats, and Applications
_________83
3.1
MOTIVATION
83
3.2
SOURCES OF STRUCTURAL DATA
84
3.2.1
PDB: The Protein Data Bank
84
3.2.2
PDBsum: The PDB Summary
86
3.2.3
SCOP: Structural Classification of Proteins
86
3.2.4
CATH: The CATH Hierarchy
88
3.2.5
PubChem
92
3.2.6
DrugBank
94
3.3
PDB FILE FORMAT
95
3.4
VISUALIZATION OF MOLECULAR DATA
98
3.4.1
Plug-In versus Stand-Alone
99
3.4.2
Change of Viewing Perspective
99
3.4.3
Graphical Representation
99
3.4.4
Visual Effects
101
3.4.5
Selection Abilities
101
3.4.6
Computational Tools
102
3.4.7
Extras
102
3.5
SOFTWARE FOR STRUCTURAL BIOINFORMATICS
103
3.5.1
PyMOL
103
3.5.2
Eclipse
103
3.5.3
MarvinSketch
104
3.5.4
ACD/ChemSketch
104
3.5.5
JOELib2
105
3.5.6
Chemistry Development Kit (CDK)
105
3.5.7
BioPython
105
3.6
EXERCISES
106
REFERENCES
109
xii ■
Table of Contents
Chapter
4
Dynamic Programming
______________________
]V _
4.1
MOTIVATION
111
4.2
INTRODUCTION
112
4.3
A DP EXAMPLE: THE
AL GORE
RHYTHM FOR
GIVING TALKS
112
4.3.1
Problem Statement
112
4.3.2
Terminology: Configurations and Scores
113
4.3.3
Analysis of Our Given Problem
113
4.4
A RECIPE FOR DYNAMIC PROGRAMMING
116
4.5
LONGEST COMMON SUBSEQUENCE
116
4.5.1
Problem Statement
117
4.5.2
Prefixes
118
4.5.3
Relations Among Subproblems
118
4.5.4
A Recurrence for the LCS
119
4.6
EXERCISES
123
Chapter
5
RNA
Secondary Structure Prediction
125
5.1
MOTIVATION
126
5.2
INTRODUCTION TO THE PROBLEM
128
5.2.1
Nature
129
5.2.1.1
Where Do Hydrogen Bonds Form?
129
5.2.1.2
Thermodynamic Issues
130
5.2.1.3
Consensus Sequence Patterns
132
5.2.1.4
Complications
133
5.2.2
Science
133
5.2.2.1
Modeling Secondary Structure
133
5.2.2.2
Single Base Pairs
134
5.2.2.3
Stacking Energy Models
134
5.2.3
Computation
138
5.2.3.1
Display of Secondary Structure
139
5.2.4
Restating the Problem
145
Table of Contents
■ xiii
5.3
THE NUSSINOV DYNAMIC PROGRAMMING
ALGORITHM
146
5.3.1
Execution Time
155
5.4
THE MFOLD ALGORITHM: TERMINOLOGY
155
5.4.1
The MFOLD Algorithm: Recursion
160
5.4.2
MFOLD Extensions
162
5.4.3
MFOLD Execution Time
162
5.5
EXERCISES
163
REFERENCES
164
Chapter
6 ■
Protein Sequence Alignment
167
6.1
PROTEIN HOMOLOGY
167
6.1.1
Nature
168
6.1.2
Science
170
6.1.2.1
Partial Matches
172
6.1.2.2
Building a BLOSUM Matrix
173
6.1.2.3
Gaps
179
6.1.2.4
Summary
180
6.1.3
Computation
180
6.1.3.1
Subproblem Specification
181
6.1.3.2
Scoring Alignments
181
6.1.3.3
Suitability of the Subproblem
182
6.1.3.4
A Global Alignment Example
186
6.2
VARIATIONS IN THE GLOBAL ALIGNMENT
ALGORITHM
186
6.3
THE SIGNIFICANCE OF A GLOBAL ALIGNMENT
187
6.3.1
Computer-Assisted Comparison
188
6.3.2
Percentage Identity Comparison
189
6.4
LOCAL ALIGNMENT
190
6.5
EXERCISES
193
REFERENCES
195
xiv ■
Table
of Contents
Chapter
7 ■
Protein Geometry
____________________________197
7.1
MOTIVATION
197
7.2
INTRODUCTION
198
7.3
CALCULATIONS RELATED TO PROTEIN GEOMETRY
198
7.3.1
Interatomic Distance
198
7.3.2
Bond Angle
198
7.3.3
Dihedral Angles
199
7.3.3.1
Defining Dihedral Angles
199
7.3.3.2
Computation of a Normal
201
7.3.3.3
Calculating the Phi Dihedral Angle
204
7.3.3.4
Sign of the Dihedral Angle
204
7.3.3.5
Calculating the
Psi
Dihedral Angle
206
7.4
RAMACHANDRAN PLOTS
206
7.5
INERŢIAL
AXES
212
7.6
EXERCISES
216
REFERENCES
220
Chapter
8
«Coordinate Transformations
223
8.1
MOTIVATION
223
8.2
INTRODUCTION
224
8.3
TRANSLATION TRANSFORMATIONS
224
8.3.1
Translation to Find Centroid at the Origin
224
8.4
ROTATION TRANSFORMATIONS
225
8.4.1
Rotation Transformations in the Plane
226
8.4.2
Rotations in
3-D
Space
227
8.5
ISOMETRIC TRANSFORMATIONS
231
8.5.1
Our Setting Is a Euclidean Vector Space
232
8.5.2
Orthogonality of A Implies Isometry of
Τ
232
8.5.3
Isometry of
Τ
Implies Orthogonality of A
233
8.5.4
Preservation of Angles
234
8.5.5
More Isometries
234
Table
of Contents
■ xv
8.5.6
Back to Rotations in the Plane
235
8.5.7
Rotations in the
3-D
Space: A Summary
238
8.6
EXERCISES
238
REFERENCES
239
Chapter
9
»Structure Comparison Alignment, and
__________
Superposition
________________________________241^
9.1
MOTIVATION
242
9.2
INTRODUCTION
245
9.2.1
Specifying the Problem
245
9.3
TECHNIQUES FOR STRUCTURAL COMPARISON
246
9.4
SCORING SIMILARITIES AND OPTIMIZING SCORES
247
9.5
SUPERPOSITION ALGORITHMS
247
9.5.1
Overview
247
9.5.2
Characterizing the Superposition Algorithm
249
9.5.3
Formal Problem Description
249
9.5.4
Computations to Achieve Maximal Overlap
251
9.5.5
Summary
257
9.5.6
Measuring Overlap
259
9.5.6.1
Calculation of the Root Mean Square
Deviation (RMSD)
259
9.5.6.2
RMSD Issues
259
9.5.7
Dealing with Weaker Sequence Similarity
260
9.5.8
Strategies Based on a Distance Matrix
261
9.6
ALGORITHMS COMPARING RELATIONSHIPS
WITHIN PROTEINS
263
9.6.1
Dali
263
9.6.2
SSAP
267
9.6.2.1
Motivation
267
9.6.2.2
Introduction to SSAP
269
9.6.2.3
Overview of SSAP
271
9.6.2.4
Calculating the Views
272
xvi ■
Table
of Contents
9.6.2.5
Building the Consensus Matrix
272
9.6.2.6
Compute the Optimal Path in the
Consensus Matrix
278
9.7
EXERCISES
279
REFERENCES
282
Chapter
10
Machine Learning
___________________________285
10.1
MOTIVATION
285
10.2
ISSUES OF COMPLEXITY
287
10.2.1
Computational Scalability
287
10.2.2
Intrinsic Complexity
287
10.2.3
Inadequate Knowledge
288
10.3
PREDICTION VIA MACHINE LEARNING
289
10.3.1
Training and Testing
291
10.4
TYPES OF LEARNING
292
10.4.1
Types of Supervised Learning
293
10.4.2
Supervised Learning: Notation and Formal
Definitions
293
10.5
OBJECTIVES OF THE LEARNING ALGORITHM
294
10.6
LINEAR REGRESSION
295
10.7
RIDGE REGRESSION
297
10.7.1
Predictors and Data Recording
299
10.7.2
Underfitting and Overfitting
300
10.8
PREAMBLE FOR KERNEL METHODS
300
10.9
KERNEL FUNCTIONS
303
10.9.1
The Kernel Trick
304
10.9.2
Design Issues
305
10.9.3
Validation Data Sets
306
10.9.3.1
Holdout Validation
307
10.9.3.2
N-Fold Cross Validation
307
10.10
CLASSIFICATION
308
10.10.1
Classification as Machine Learning
309
10.10.2
Ad Hoc Classification
310
Table
of Contents
■ xv»
10.11
HEURISTICS FOR CLASSIFICATION
311
10.11.1
Feature Weighting
311
10.12
NEAREST NEIGHBOR CLASSIFICATION
312
10.12.1
Delaunay and Voronoi
313
10.12.2
Nearest Neighbor Time and Space Issues
315
10.13
SUPPORT VECTOR MACHINES
315
10.13.1
Linear Discrimination
315
10.13.2
Margin of Separation
318
10.13.3
Support Vectors
319
10.13.4
The SVM as an Optimization Problem
320
10.13.5
The Karush-Kuhn-Tucker Condition
322
10.13.6
Evaluation of w0
322
10.14
LINEARLY NONSEPARABLE DATA
323
10.14.1
Parameter Values
326
10.14.2
Evaluation of w0 (Soft Margin Case)
327
10.14.3
Classification with Soft Margin
327
10.15
SUPPORT VECTOR MACHINES AND KERNELS
328
10.16
EXPECTED TEST ERROR
328
10.17
TRANSPARENCY
329
10.18
EXERCISES
331
REFERENCES
334
APPENDICES
337
INDEX
385
|
| adam_txt |
Table
of
Contents
Preface
xix
Acknowledgments
xxiii
Chapter
1 ■
The Study of Structural Bioinformatics
1
1.1
MOTIVATION
1
1.2
SMALL BEGINNINGS
4
1.3
STRUCTURAL BIOINFORMATICS AND THE
SCIENTIFIC METHOD
5
1.3.1
Three Realms: Nature, Science,
and Computation
6
1.3.2
Hypothesis, Model, and Theory
8
1.3.3
Laws, Postulates, and Assumptions
12
1.3.4
Model Theory and Computational Theory
13
1.3.5
Different Assumptions for Different Models
14
1.4
A MORE DETAILED PROBLEM ANALYSIS:
FORCE FIELDS
15
1.4.1
Nature
16
1.4.2
Science
16
1.4.2.1
Energy Terms for Bonded Atoms
16
1.4.2.2
Energy Terms for Nonbonded Atoms
19
1.4.2.3
Total Potential Energy
21
1.4.3
Computation
21
■
Table
of Contents
1.5
MODELING ISSUES
25
1.5.1
Rashomon
26
1.5.2
Ockham
26
1.5.3
Bellman
27
1.5.4
Interpretability
28
1.5.5
Refutability
29
1.5.6
Complexity and Approximation
29
1.6
SOURCES OF ERROR
32
1.7
SUMMARY
33
1.8
EXERCISES
34
REFERENCES
36
Chapter
2 ■
Introduction to Macromolecular Structure
37
2.1
MOTIVATION
37
2.2
OVERVIEW OF PROTEIN STRUCTURE
38
2.2.1
Amino
Acids and Primary Sequence
38
2.2.2
Secondary Structure
44
2.2.2.1
Alpha Helices
44
2.2.2.2
Beta Strands
47
2.2.2.3
Loops
52
2.2.3
Tertiary Structure
53
2.2.3.1
What Is Tertiary Structure?
54
2.2.3.2
The Tertiary Structure ofMyoglobin
54
2.2.3.3
Tertiary Structure Beyond the Binding
Pocket
58
2.2.4
Quaternary Structure
64
2.2.5
Protein Functionality
67
2.2.6
Protein Domains
68
2.3
AN OVERVIEW OF
RNA
STRUCTURE
70
2.3.1
Nucleotides and
RNA
Primary Sequence
71
2.3.2
RNA
Secondary Structure
72
2.3.3
RNA
Tertiary Structure
75
Table
of Contents
■ xi
2.4
EXERCISES
78
REFERENCES
80
Chapter
3
»Data Sources, Formats, and Applications
_83
3.1
MOTIVATION
83
3.2
SOURCES OF STRUCTURAL DATA
84
3.2.1
PDB: The Protein Data Bank
84
3.2.2
PDBsum: The PDB Summary
86
3.2.3
SCOP: Structural Classification of Proteins
86
3.2.4
CATH: The CATH Hierarchy
88
3.2.5
PubChem
92
3.2.6
DrugBank
94
3.3
PDB FILE FORMAT
95
3.4
VISUALIZATION OF MOLECULAR DATA
98
3.4.1
Plug-In versus Stand-Alone
99
3.4.2
Change of Viewing Perspective
99
3.4.3
Graphical Representation
99
3.4.4
Visual Effects
101
3.4.5
Selection Abilities
101
3.4.6
Computational Tools
102
3.4.7
Extras
102
3.5
SOFTWARE FOR STRUCTURAL BIOINFORMATICS
103
3.5.1
PyMOL
103
3.5.2
Eclipse
103
3.5.3
MarvinSketch
104
3.5.4
ACD/ChemSketch
104
3.5.5
JOELib2
105
3.5.6
Chemistry Development Kit (CDK)
105
3.5.7
BioPython
105
3.6
EXERCISES
106
REFERENCES
109
xii ■
Table of Contents
Chapter
4'
Dynamic Programming
_
]V\_
4.1
MOTIVATION
111
4.2
INTRODUCTION
112
4.3
A DP EXAMPLE: THE
AL GORE
RHYTHM FOR
GIVING TALKS
112
4.3.1
Problem Statement
112
4.3.2
Terminology: Configurations and Scores
113
4.3.3
Analysis of Our Given Problem
113
4.4
A RECIPE FOR DYNAMIC PROGRAMMING
116
4.5
LONGEST COMMON SUBSEQUENCE
116
4.5.1
Problem Statement
117
4.5.2
Prefixes
118
4.5.3
Relations Among Subproblems
118
4.5.4
A Recurrence for the LCS
119
4.6
EXERCISES
123
Chapter
5
"RNA
Secondary Structure Prediction
125
5.1
MOTIVATION
126
5.2
INTRODUCTION TO THE PROBLEM
128
5.2.1
Nature
129
5.2.1.1
Where Do Hydrogen Bonds Form?
129
5.2.1.2
Thermodynamic Issues
130
5.2.1.3
Consensus Sequence Patterns
132
5.2.1.4
Complications
133
5.2.2
Science
133
5.2.2.1
Modeling Secondary Structure
133
5.2.2.2
Single Base Pairs
134
5.2.2.3
Stacking Energy Models
134
5.2.3
Computation
138
5.2.3.1
Display of Secondary Structure
139
5.2.4
Restating the Problem
145
Table of Contents
■ xiii
5.3
THE NUSSINOV DYNAMIC PROGRAMMING
ALGORITHM
146
5.3.1
Execution Time
155
5.4
THE MFOLD ALGORITHM: TERMINOLOGY
155
5.4.1
The MFOLD Algorithm: Recursion
160
5.4.2
MFOLD Extensions
162
5.4.3
MFOLD Execution Time
162
5.5
EXERCISES
163
REFERENCES
164
Chapter
6 ■
Protein Sequence Alignment
167
6.1
PROTEIN HOMOLOGY
167
6.1.1
Nature
168
6.1.2
Science
170
6.1.2.1
Partial Matches
172
6.1.2.2
Building a BLOSUM Matrix
173
6.1.2.3
Gaps
179
6.1.2.4
Summary
180
6.1.3
Computation
180
6.1.3.1
Subproblem Specification
181
6.1.3.2
Scoring Alignments
181
6.1.3.3
Suitability of the Subproblem
182
6.1.3.4
A Global Alignment Example
186
6.2
VARIATIONS IN THE GLOBAL ALIGNMENT
ALGORITHM
186
6.3
THE SIGNIFICANCE OF A GLOBAL ALIGNMENT
187
6.3.1
Computer-Assisted Comparison
188
6.3.2
Percentage Identity Comparison
189
6.4
LOCAL ALIGNMENT
190
6.5
EXERCISES
193
REFERENCES
195
xiv ■
Table
of Contents
Chapter
7 ■
Protein Geometry
_197
7.1
MOTIVATION
197
7.2
INTRODUCTION
198
7.3
CALCULATIONS RELATED TO PROTEIN GEOMETRY
198
7.3.1
Interatomic Distance
198
7.3.2
Bond Angle
198
7.3.3
Dihedral Angles
199
7.3.3.1
Defining Dihedral Angles
199
7.3.3.2
Computation of a Normal
201
7.3.3.3
Calculating the Phi Dihedral Angle
204
7.3.3.4
Sign of the Dihedral Angle
204
7.3.3.5
Calculating the
Psi
Dihedral Angle
206
7.4
RAMACHANDRAN PLOTS
206
7.5
INERŢIAL
AXES
212
7.6
EXERCISES
216
REFERENCES
220
Chapter
8
«Coordinate Transformations
223
8.1
MOTIVATION
223
8.2
INTRODUCTION
224
8.3
TRANSLATION TRANSFORMATIONS
224
8.3.1
Translation to Find Centroid at the Origin
224
8.4
ROTATION TRANSFORMATIONS
225
8.4.1
Rotation Transformations in the Plane
226
8.4.2
Rotations in
3-D
Space
227
8.5
ISOMETRIC TRANSFORMATIONS
231
8.5.1
Our Setting Is a Euclidean Vector Space
232
8.5.2
Orthogonality of A Implies Isometry of
Τ
232
8.5.3
Isometry of
Τ
Implies Orthogonality of A
233
8.5.4
Preservation of Angles
234
8.5.5
More Isometries
234
Table
of Contents
■ xv
8.5.6
Back to Rotations in the Plane
235
8.5.7
Rotations in the
3-D
Space: A Summary
238
8.6
EXERCISES
238
REFERENCES
239
Chapter
9
»Structure Comparison Alignment, and
_
Superposition
_241^
9.1
MOTIVATION
242
9.2
INTRODUCTION
245
9.2.1
Specifying the Problem
245
9.3
TECHNIQUES FOR STRUCTURAL COMPARISON
246
9.4
SCORING SIMILARITIES AND OPTIMIZING SCORES
247
9.5
SUPERPOSITION ALGORITHMS
247
9.5.1
Overview
247
9.5.2
Characterizing the Superposition Algorithm
249
9.5.3
Formal Problem Description
249
9.5.4
Computations to Achieve Maximal Overlap
251
9.5.5
Summary
257
9.5.6
Measuring Overlap
259
9.5.6.1
Calculation of the Root Mean Square
Deviation (RMSD)
259
9.5.6.2
RMSD Issues
259
9.5.7
Dealing with Weaker Sequence Similarity
260
9.5.8
Strategies Based on a Distance Matrix
261
9.6
ALGORITHMS COMPARING RELATIONSHIPS
WITHIN PROTEINS
263
9.6.1
Dali
263
9.6.2
SSAP
267
9.6.2.1
Motivation
267
9.6.2.2
Introduction to SSAP
269
9.6.2.3
Overview of SSAP
271
9.6.2.4
Calculating the Views
272
xvi ■
Table
of Contents
9.6.2.5
Building the Consensus Matrix
272
9.6.2.6
Compute the Optimal Path in the
Consensus Matrix
278
9.7
EXERCISES
279
REFERENCES
282
Chapter
10
'Machine Learning
_285
10.1
MOTIVATION
285
10.2
ISSUES OF COMPLEXITY
287
10.2.1
Computational Scalability
287
10.2.2
Intrinsic Complexity
287
10.2.3
Inadequate Knowledge
288
10.3
PREDICTION VIA MACHINE LEARNING
289
10.3.1
Training and Testing
291
10.4
TYPES OF LEARNING
292
10.4.1
Types of Supervised Learning
293
10.4.2
Supervised Learning: Notation and Formal
Definitions
293
10.5
OBJECTIVES OF THE LEARNING ALGORITHM
294
10.6
LINEAR REGRESSION
295
10.7
RIDGE REGRESSION
297
10.7.1
Predictors and Data Recording
299
10.7.2
Underfitting and Overfitting
300
10.8
PREAMBLE FOR KERNEL METHODS
300
10.9
KERNEL FUNCTIONS
303
10.9.1
The "Kernel Trick"
304
10.9.2
Design Issues
305
10.9.3
Validation Data Sets
306
10.9.3.1
Holdout Validation
307
10.9.3.2
N-Fold Cross Validation
307
10.10
CLASSIFICATION
308
10.10.1
Classification as Machine Learning
309
10.10.2
Ad Hoc Classification
310
Table
of Contents
■ xv»
10.11
HEURISTICS FOR CLASSIFICATION
311
10.11.1
Feature Weighting
311
10.12
NEAREST NEIGHBOR CLASSIFICATION
312
10.12.1
Delaunay and Voronoi
313
10.12.2
Nearest Neighbor Time and Space Issues
315
10.13
SUPPORT VECTOR MACHINES
315
10.13.1
Linear Discrimination
315
10.13.2
Margin of Separation
318
10.13.3
Support Vectors
319
10.13.4
The SVM as an Optimization Problem
320
10.13.5
The Karush-Kuhn-Tucker Condition
322
10.13.6
Evaluation of w0
322
10.14
LINEARLY NONSEPARABLE DATA
323
10.14.1
Parameter Values
326
10.14.2
Evaluation of w0 (Soft Margin Case)
327
10.14.3
Classification with Soft Margin
327
10.15
SUPPORT VECTOR MACHINES AND KERNELS
328
10.16
EXPECTED TEST ERROR
328
10.17
TRANSPARENCY
329
10.18
EXERCISES
331
REFERENCES
334
APPENDICES
337
INDEX
385 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Burkowski, Forbes J. |
| author_facet | Burkowski, Forbes J. |
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| dewey-tens | 570 - Biology |
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| id | DE-604.BV022397883 |
| illustrated | Illustrated |
| index_date | 2024-07-02T17:17:11Z |
| indexdate | 2024-07-09T20:56:43Z |
| institution | BVB |
| isbn | 9781584886839 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-015606595 |
| oclc_num | 276643427 |
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| physical | XXIII, 406 S. Ill., graph. Darst. |
| publishDate | 2009 |
| publishDateSearch | 2009 |
| publishDateSort | 2009 |
| publisher | CRC Press |
| record_format | marc |
| series2 | Chapman & Hall/CRC mathematical and computational biology series |
| spelling | Burkowski, Forbes J. Verfasser aut Structural bioinformatics an algorithmic approach Forbes J. Burkowski Boca Raton, Fla.[u.a.] CRC Press 2009 XXIII, 406 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Chapman & Hall/CRC mathematical and computational biology series Computational Biology Models, Molecular Protein Conformation Structural bioinformatics Algorithmische Programmierung (DE-588)4293504-0 gnd rswk-swf Quartärstruktur (DE-588)4400205-1 gnd rswk-swf Bioinformatik (DE-588)4611085-9 gnd rswk-swf Bioinformatik (DE-588)4611085-9 s Quartärstruktur (DE-588)4400205-1 s Algorithmische Programmierung (DE-588)4293504-0 s b DE-604 Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015606595&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Burkowski, Forbes J. Structural bioinformatics an algorithmic approach Computational Biology Models, Molecular Protein Conformation Structural bioinformatics Algorithmische Programmierung (DE-588)4293504-0 gnd Quartärstruktur (DE-588)4400205-1 gnd Bioinformatik (DE-588)4611085-9 gnd |
| subject_GND | (DE-588)4293504-0 (DE-588)4400205-1 (DE-588)4611085-9 |
| title | Structural bioinformatics an algorithmic approach |
| title_auth | Structural bioinformatics an algorithmic approach |
| title_exact_search | Structural bioinformatics an algorithmic approach |
| title_exact_search_txtP | Structural bioinformatics an algorithmic approach |
| title_full | Structural bioinformatics an algorithmic approach Forbes J. Burkowski |
| title_fullStr | Structural bioinformatics an algorithmic approach Forbes J. Burkowski |
| title_full_unstemmed | Structural bioinformatics an algorithmic approach Forbes J. Burkowski |
| title_short | Structural bioinformatics |
| title_sort | structural bioinformatics an algorithmic approach |
| title_sub | an algorithmic approach |
| topic | Computational Biology Models, Molecular Protein Conformation Structural bioinformatics Algorithmische Programmierung (DE-588)4293504-0 gnd Quartärstruktur (DE-588)4400205-1 gnd Bioinformatik (DE-588)4611085-9 gnd |
| topic_facet | Computational Biology Models, Molecular Protein Conformation Structural bioinformatics Algorithmische Programmierung Quartärstruktur Bioinformatik |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015606595&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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