Bioinformatics: Problem Solving Paradigms
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| 1. Verfasser: | |
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Berlin [u.a.]
Springer
2008
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| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XVIII, 289 S. |
| ISBN: | 9783540785057 3540785051 9783540785064 |
Internformat
MARC
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| 100 | 1 | |a Sperschneider, Volker |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Bioinformatics |b Problem Solving Paradigms |c Volker Sperschneider |
| 264 | 1 | |a Berlin [u.a.] |b Springer |c 2008 | |
| 300 | |a XVIII, 289 S. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 650 | 4 | |a Mathematik | |
| 650 | 4 | |a Bioinformatics | |
| 650 | 4 | |a Bioinformatics |x Mathematics | |
| 650 | 4 | |a Computer algorithms | |
| 650 | 0 | 7 | |a Bioinformatik |0 (DE-588)4611085-9 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Bioinformatik |0 (DE-588)4611085-9 |D s |
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Datensatz im Suchindex
| _version_ | 1804137738142744576 |
|---|---|
| adam_text | CONTENTS
COR
E BIOINFORMATIC
S PROBLEM
S 1
1.1 DNA MAPPING AN
D SEQUENCING 1
1.1.1
SIZE OF HUMA
N DNA 1
1.1.2 COPYING DNA: POLYMERASE CHAIN REACTION (PCR
) 2
1.1.3 HYBRIDIZATION AN
D MICROARRAYS 2
1.1.4 CUTTIN
G DNA INTO FRAGMENTS 2
1.1.5 PREFIX CUTTIN
G 3
1.1.6 UNIQUE MARKERS 3
1.1.7 MEASURING TH
E LENGTH OF DNA MOLECULES 4
1.1.8 SEQUENCING SHORT DNA MOLECULES 4
1.1.9 MAPPING LONG DNA MOLECULES 4
1.1.10 MAPPING BY SINGLE DIGESTION 5
1.1.11
MAPPING BY DOUBLE DIGESTION 6
1.1.12 MID-SIZED DNA MOLECULES: SHOTGUN SEQUENCING 7
1.2 STRING STORAGE AN
D PATTER
N MATCHING 8
1.2.1 COMPLEXITY OF PATTER
N MATCHING IN BIOINFORMATICS 8
1.2.2 FIRS
T LOOK A
T SUFFIX TREES 9
1.2.3 A COUPLE OF SUFFIX TREE APPLICATIONS 10
1.3 STRIN
G ALIGNMENT 11
1.4 MULTIPLE ALIGNMENT 13
1.5 GENE DETECTION 15
1.6 GENE COMPARISON 16
1.7 RNA STRUCTUR
E PREDICTIO
N 18
1.8 PROTEI
N STRUCTUR
E PREDICTIO
N 19
1.8.1 HOLY GRAIL PROTEI
N STRUCTUR
E PREDICTION 19
1.8.2 SECONDARY STRUCTUR
E 20
1.8.3 TERTIAR
Y STRUCTUR
E PREDICTION WITH CONTAC
T MAPS 20
1.8.4 HP-MODEL APPROACH 21
1.8.5 PROTEI
N THREADIN
G 22
1.9 MOLECULAR NETWORKS 23
1.10 BIBLIOGRAPHIE REMARKS 24
GESCANNT DURCH
BIBLIOGRAFISCHE INFORMATIONEN
HTTP://D-NB.INFO/987806157
DIGITALISIERT DURCH
XIV CONTENTS
2 TURNIN
G T
O ALGORITHMI
C PROBLEM
S
25
2.1 PRESENTATIO
N OF ALGORITHMIC PROBLEMS 25
2.2 DNA MAPPING 26
2.2.1 MAPPIN
G BY HYBRIDIZATION 26
2.2.2 MAPPIN
G BY SINGLE DIGESTION 27
2.2.3 MAPPING BY DOUBLE DIGESTION 30
2.3 SHOTGUN SEQUENCING AN
D SHORTEST COMMON SUPERSTRINGS 31
2.4 EXAC
T PATTER
N MATCHIN
G 35
2.4.1 NAIVE PATTER
N MATCHING 35
2.4.2 KNUTH-MORRIS-PRAT
T ALGORITHM 35
2.4.3 MULTI-TEXT SEARCH 37
2.4.4 MULTI-PATTER
N SEARCH 37
2.4.5 FORMAL DEFINITION OF SUFFIX TREES 38
2.5 SOFT PATTER
N MATCHIN
G = ALIGNMENT 41
2.5.1 ALIGNMENTS RESTATE
D 41
2.5.2 EVALUATIN
G ALIGNMENTS BY SCORING FUNCTIONS 41
2.6 MULTIPLE ALIGNMENT 4
3
2.6.1 SUM-OF-PAIRS MAXIMIZATION 43
2.6.2 MULTIPLE ALIGNMENT ALONG A GUIDE TREE 43
2.6.3 CONSENSUS LINE OPTIMIZATIO
N 45
2.6.4 STEINER STRING 45
2.6.5 EQUIVALENCE OF CONSENSUS LINES AND STEINER STRINGS ...
. 45
2.6.6 GENERALIZATION T
O STEINER TREES/PHYLOGENETIC ALIGNMENT 47
2.6.7 PROFILE ALIGNMENT 47
2.6.8 HIDDEN MARKOV MULTIPLE ALIGNMENT 48
2.6.9 EXAMPLE HEURISTIC FOR OBTAININ
G MULTIPLE ALIGNMENTS.
. 51
2.7 GENE DETECTION 52
2.8 GENOME REARRANGEMENT 53
2.9 RNA STRUCTUR
E PREDICTIO
N 55
2.10 PROTEI
N STRUCTUR
E PREDICTIO
N 56
2.10.1 COMPARATIV
E APPROACHES USING NEURAL NETWORKS 56
2.10.2 PROTEI
N THREADIN
G 57
2.11 BI-CLUSTERING 59
2.12 BIBLIOGRAPHIE REMARKS 60
3 DYNAMI
C
PROGRAMMIN
G
61
3.1 GENERAL PRINCIPLE OF DYNAMIC PROGRAMMIN
G 61
3.2 ALIGNMENT 63
3.2.1 PROBLE
M RESTATE
D 63
3.2.2 PARAMETERIZATIO
N 64
3.2.3 BELLMAN PRINCIPLE 64
3.2.4 RECURSIVE SOLUTION 64
3.2.5 NUMBER OF DIFFERENT SUBCALLS AN
D OVERALL COMPLEXITY .
. 65
3.2.6 TABULA
R ORGANIZATION OF TH
E BOTTOM-U
P COMPUTATION.
. 65
3.3 MULTIPLE ALIGNMENT 67
CONTENTS XV
3.4 AFFINE GA
P ALIGNMENT 67
3.4.1 PROBLEM RESTATE
D 67
3.4.2 PARAMETERIZATIO
N AN
D CONDITIONING 68
3.4.3 BELLMAN PRINCIPLE 68
3.4.4 RECURSIVE SOLUTION 68
3.4.5 NUMBER OF DIFFERENT SUBCALLS AND OVERALL COMPLEXITY .
. 69
3.5 EXON ASSEMBLY 69
3.5.1 PROBLEM RESTATE
D 69
3.5.2 PARAMETERIZATIO
N AND CONDITIONING 70
3.5.3 BELLMAN PRINCIPLE 70
3.5.4 RECURSIVE SOLUTION 71
3.5.5 NUMBER OF DIFFERENT SUBCALLS AN
D OVERALL COMPLEXITY .
. 72
3.5.6 PREPROCESSING OF SIMPLE TERMS 72
3.5.7 PARALLEL COMPUTATIO
N OF RECURSIVE TERMS 72
3.6 RNA STRUCTUR
E PREDICTION 73
3.6.1 PROBLEM RESTATE
D 73
3.6.2 PARAMETERIZATIO
N AND CONDITIONING 74
3.6.3 RECURSIVE SOLUTION AN
D BELLMAN PRINCIPLE 74
3.6.4 NUMBER OF DIFFERENT SUBCALLS AN
D OVERALL COMPLEXITY .
. 75
3.6.5 VARIATION: FREE ENERGY MINIMIZATION 76
3.7 VITERB
I ALGORITHM 83
3.7.1 PROBLEM RESTATE
D 83
3.7.2 PARAMETERIZATIO
N AN
D CONDITIONING 83
3.7.3 BELLMAN PRINCIPLE 83
3.7.4 RECURSIVE COMPUTATIO
N 83
3.7.5 COUNTING AN
D OVERALL COMPLEXITY 84
3.8 BAUM-WELCH ALGORITHM 84
3.8.1 PROBLEM MOTIVATION 84
3.8.2 A COUPLE OF IMPORTAN
T VARIABLES 84
3.8.3 COMPUTING FORWARD VARIABLES 85
3.8.4 COMPUTING BACKWARD VARIABLES 85
3.8.5 COMPUTING TRANSITION VARIABLES 85
3.8.6 COMPUTING STAT
E VARIABLES 85
3.8.7 MODEL IMPROVEMENT 86
3.9 EXPRESSIVENESS OF VITERBI EQUATION
S 86
3.10 LIFTED PHYLOGENETIC ALIGNMENT 91
3.10.1 PROBLEM RESTATE
D 91
3.10.2 PARAMETERIZATIO
N AN
D CONDITIONING 92
3.10.3 BELLMAN PRINCIPLE 92
3.10.4 RECURSIVE SOLUTION 92
3.10.5 COUNTING AN
D OVERALL COMPLEXITY 93
3.10.6 PREPROCESSING OF SIMPLE TERMS 93
3.11 PROTEI
N THREADIN
G 93
3.11.1 PROBLEM RESTATE
D 93
3.11.2 PARAMETERIZATIO
N AN
D CONDITIONING 94
XVI CONTENTS
3.11.3 BELLMAN PRINCIPLE 94
3.11.4 RECURSIVE SOLUTION 94
3.11.5 COUNTING AN
D OVERALL COMPLEXITY 95
3.12 BIBLIOGRAPHIE REMARK
S 95
4
INTELLIGEN
T DAT
A STRUCTURE
S
97
4.1 REPRESENTATION MATTER
S 97
4.1.1 INTELLIGENT DAT
A STRUCTURE
S USED IN COMPUTE
R SCIENCE.
. 97
4.1.2 COVERING A TRUNCATE
D BOARD 97
4.1.3 CHOOSE THRE
E BIT
S AND WI
N 98
4.1.4 CHECK A PROOF 99
4.2 PQ-TREES 101
4.2.1 BASIC NOTIONS 101
4.2.2 TRANSFORMATION RULES FOR MARKED PQ-TREES 103
4.3 SUFFIX TREES 111
4.3.1 OUTLIN
E OF UKKONEN S ALGORITHM AN
D BASIC PROCEDURES .11
1
4.3.2 INSERTIO
N PROCEDUR
E 112
4.3.3 SAVING SINGLE CHARACTE
R INSERTIONS 115
4.3.4 SAVING NAVIGATION STEPS 116
4.3.5 ESTIMATIO
N OF TH
E OVERALL NUMBER OF VISITED NODES ...
. 117
4.3.6 UKKONEN S THEOREM 120
4.3.7 COMMON SUFFIX TREE FOR SEVERAL STRINGS 123
4.3.8 APPLICATIONS OF SUFFIX TREES 124
4.4 LEAST COMMON ANCESTOR 131
4.4.1 MOTIVATION 131
4.4.2 LEAST COMMON ANCESTOR IN FUELL BINARY TREES 131
4.4.3 LEAST COMMON ANCESTOR IN ARBITRAR
Y TREES 135
4.5 SIGNED GENOME REARRANGEMENT 144
4.5.1 REALITY-DESIRE DIAGRAMS OF SIGNED PERMUTATION
S 144
4.5.2 TING-PONG
OF LOWER AN
D UPPER BOUNDS 148
4.5.3 PADDIN
G RD-DIAGRAMS 159
4.5.4 SORTING PADDE
D GOOD COMPONENTS 161
4.5.5 SUMMARY OF TH
E SORTING PROCEDUR
E 164
4.6 BIBLIOGRAPHIE REMARK
S 166
5 NP-HARDNES
S O
F COR
E BIOINFORMATIC
S PROBLEM
S
167
5.1 GETTIN
G FAMILIAER WIT
H BASIC NOTIONS OF COMPLEXITY THEOR
Y ..
. 167
5.2 COOK S THEOREM: PRIME
R NP-COMPLET
E PROBLE
M 171
5.3 ZOO OF NP-COMPLET
E PROBLEMS 175
5.4 BRIDGE T
O BIOINFORMATICS: SHORTEST COMMON SUPER-SEQUENCE .
. 188
5.5 NP-COMPLETENESS OF CORE BIOINFORMATICS PROBLEMS 198
5.5.1 MULTIPLE ALIGNMENT 198
5.5.2 SHORTEST COMMON SUPERSTRIN
G 201
5.5.3 DOUBLE DIGEST 204
5.5.4 PROTEI
N THREADIN
G 204
CONTENTS XVII
5.5.5 BI-CLUSTERING 206
5.5.6 PSEUDOKNOT PREDICTION 209
5.5.7 TH
E PICTUR
E OF PRESENTED REDUCTIONS 213
5.5.8 FURTHE
R NP-COMPLETE BIOINFORMATICS PROBLEMS 213
5.6 BIBLIOGRAPHIE REMARKS 215
APPROXIMATIO
N ALGORITHM
S
217
6.1 BASICS OF APPROXIMATION ALGORITHMS 217
6.1.1 WHA
T IS A
N APPROXIMATION ALGORITHM? 217
6.1.2 FIRS
T EXAMPLE: METRIC TRAVELLING SALESMAN 217
6.1.3 LOWER AN
D UPPER BOUNDING 218
6.1.4 WHA
T ARE APPROXIMATION ALGORITHMS GOOD FOR? 219
6.2 SHORTEST COMMON SUPERSTRING PROBLEM 221
6.2.1 FEASIBLE LOWER BOUND 221
6.2.2 CYCLE COVERS RELATED T
O PERFECT MATCHINGS PROBLEM ..
. 222
6.2.3 COMPUTING MAXIMUM COST PERFECT MATCHINGS 223
6.2.4 CYCLE COVER VERSUS CYCLIC COVERING BY STRINGS 225
6.2.5 SOME COMBINATORIAL STATEMENT
S ON CYCLIC COVERING ..
. 226
6.2.6 4-APPROXIMATION ALGORITHM 228
6.2.7 PUTTIN
G THINGS TOGETHER 229
6.3 STEINER STRING 230
6.3.1 PROBLEM RESTATE
D 230
6.3.2 FEASIBLE LOWER BOUND 230
6.3.3 2-APPROXIMATION ALGORITHM 231
6.4 PHYLOGENETIC ALIGNMENT 232
6.5 MULTIPLE ALIGNMENT 234
6.5.1 FEASIBLE LOWER BOUND 234
6.5.2 2-APPROXIMATION ALGORITHM 235
6.6 SORTING UNSIGNED GENOMES 236
6.6.1 FEASIBLE LOWER BOUND 236
6.6.2 4-APPROXIMATION ALGORITHM 237
6.6.3 2-APPROXIMATION ALGORITHM 239
6.7 HP-MODEL PROTEI
N STRUCTUR
E PREDICTIO
N 243
6.7.1 FEASIBLE UPPER BOUND 243
6.7.2 ASYMPTOTIC 4-APPROXIMATION ALGORITHM 245
6.8 BIBLIOGRAPHIE REMARKS 248
A SELECTIO
N O
F METAHEURISTIC
S AN
D VARIOU
S PROJECT
S
249
7.1 MULTI-LAYER PERCEPTRON 249
7.1.1 ARCHITECTURE OF MULTI-LAYER PERCEPTRONS 250
7.1.2 FORWARD PROPAGATIO
N OF INPU
T SIGNALS 251
7.1.3 INPU
T AND OUTPU
T ENCODING 251
7.1.4 BACKPROPAGATION OF ERRO
R SIGNALS 252
7.1.5 TUNING BACKPROPAGATION 253
7.1.6 GENERALIZATION AND OVERFITTING 253
XVIII CONTENTS
7.1.7 APPLICATION T
O SECONDARY STRUCTUR
E PREDICTIO
N 254
7.1.8 FURTHE
R APPLICATIONS 254
7.2 SUPPOR
T VECTOR MACHINES 255
7.2.1 ARCHITECTUR
E OF SUPPOR
T VECTOR MACHINES 255
7.2.2 MARGIN MAXIMIZATION 256
7.2.3 PRIMA
L PROBLEM 257
7.2.4 DUA
L PROBLE
M 258
7.2.5 KERNEL FUNCTION TRICK 259
7.2.6 GENERALIZATION AN
D OVER-FITTIN
G 260
7.2.7 APPLICATION T
O CONTAC
T MA
P PREDICTIO
N 261
7.2.8 SOFT MARGIN MAXIMIZATION 263
7.2.9 SEMI-SUPERVISED SUPPOR
T VECTOR MACHINES 264
7.3 HIDDEN MARKOV MODELS 265
7.3.1 ARCHITECTUR
E OF HIDDEN MARKOV MODELS 266
7.3.2 CAUSES AN
D EFFECTS 266
7.3.3 BIOINFORMATICS APPLICATION: CG ISLANDS 268
7.3.4 FURTHE
R APPLICATIONS 269
7.4 GENETIC ALGORITHMS 269
7.4.1 BASIC CHARACTERISTICS OF GENETIC ALGORITHMS 269
7.4.2 APPLICATION T
O HP-FOLD OPTIMIZATION 271
7.4.3 FURTHE
R APPLICATIONS 272
7.5 ANT COLONY OPTIMIZATIO
N 272
7.5.1 BASIC CHARACTERISTIC
S OF ANT COLONY ALGORITHMS 273
7.5.2 APPLICATION T
O HP-FOLD OPTIMIZATION 275
7.5.3 FURTHE
R APPLICATIONS 276
7.6 BIBLIOGRAPHIE REMARKS 276
REFERENCES
279
INDEX
285
|
| adam_txt |
CONTENTS
COR
E BIOINFORMATIC
S PROBLEM
S 1
1.1 DNA MAPPING AN
D SEQUENCING 1
1.1.1
SIZE OF HUMA
N DNA 1
1.1.2 COPYING DNA: POLYMERASE CHAIN REACTION (PCR
) 2
1.1.3 HYBRIDIZATION AN
D MICROARRAYS 2
1.1.4 CUTTIN
G DNA INTO FRAGMENTS 2
1.1.5 PREFIX CUTTIN
G 3
1.1.6 UNIQUE MARKERS 3
1.1.7 MEASURING TH
E LENGTH OF DNA MOLECULES 4
1.1.8 SEQUENCING SHORT DNA MOLECULES 4
1.1.9 MAPPING LONG DNA MOLECULES 4
1.1.10 MAPPING BY SINGLE DIGESTION 5
1.1.11
MAPPING BY DOUBLE DIGESTION 6
1.1.12 MID-SIZED DNA MOLECULES: SHOTGUN SEQUENCING 7
1.2 STRING STORAGE AN
D PATTER
N MATCHING 8
1.2.1 COMPLEXITY OF PATTER
N MATCHING IN BIOINFORMATICS 8
1.2.2 FIRS
T LOOK A
T SUFFIX TREES 9
1.2.3 A COUPLE OF SUFFIX TREE APPLICATIONS 10
1.3 STRIN
G ALIGNMENT 11
1.4 MULTIPLE ALIGNMENT 13
1.5 GENE DETECTION 15
1.6 GENE COMPARISON 16
1.7 RNA STRUCTUR
E PREDICTIO
N 18
1.8 PROTEI
N STRUCTUR
E PREDICTIO
N 19
1.8.1 HOLY GRAIL PROTEI
N STRUCTUR
E PREDICTION 19
1.8.2 SECONDARY STRUCTUR
E 20
1.8.3 TERTIAR
Y STRUCTUR
E PREDICTION WITH CONTAC
T MAPS 20
1.8.4 HP-MODEL APPROACH 21
1.8.5 PROTEI
N THREADIN
G 22
1.9 MOLECULAR NETWORKS 23
1.10 BIBLIOGRAPHIE REMARKS 24
GESCANNT DURCH
BIBLIOGRAFISCHE INFORMATIONEN
HTTP://D-NB.INFO/987806157
DIGITALISIERT DURCH
XIV CONTENTS
2 TURNIN
G T
O ALGORITHMI
C PROBLEM
S
25
2.1 PRESENTATIO
N OF ALGORITHMIC PROBLEMS 25
2.2 DNA MAPPING 26
2.2.1 MAPPIN
G BY HYBRIDIZATION 26
2.2.2 MAPPIN
G BY SINGLE DIGESTION 27
2.2.3 MAPPING BY DOUBLE DIGESTION 30
2.3 SHOTGUN SEQUENCING AN
D SHORTEST COMMON SUPERSTRINGS 31
2.4 EXAC
T PATTER
N MATCHIN
G 35
2.4.1 NAIVE PATTER
N MATCHING 35
2.4.2 KNUTH-MORRIS-PRAT
T ALGORITHM 35
2.4.3 MULTI-TEXT SEARCH 37
2.4.4 MULTI-PATTER
N SEARCH 37
2.4.5 FORMAL DEFINITION OF SUFFIX TREES 38
2.5 SOFT PATTER
N MATCHIN
G = ALIGNMENT 41
2.5.1 ALIGNMENTS RESTATE
D 41
2.5.2 EVALUATIN
G ALIGNMENTS BY SCORING FUNCTIONS 41
2.6 MULTIPLE ALIGNMENT 4
3
2.6.1 SUM-OF-PAIRS MAXIMIZATION 43
2.6.2 MULTIPLE ALIGNMENT ALONG A GUIDE TREE 43
2.6.3 CONSENSUS LINE OPTIMIZATIO
N 45
2.6.4 STEINER STRING 45
2.6.5 EQUIVALENCE OF CONSENSUS LINES AND STEINER STRINGS .
. 45
2.6.6 GENERALIZATION T
O STEINER TREES/PHYLOGENETIC ALIGNMENT 47
2.6.7 PROFILE ALIGNMENT 47
2.6.8 HIDDEN MARKOV MULTIPLE ALIGNMENT 48
2.6.9 EXAMPLE HEURISTIC FOR OBTAININ
G MULTIPLE ALIGNMENTS.
. 51
2.7 GENE DETECTION 52
2.8 GENOME REARRANGEMENT 53
2.9 RNA STRUCTUR
E PREDICTIO
N 55
2.10 PROTEI
N STRUCTUR
E PREDICTIO
N 56
2.10.1 COMPARATIV
E APPROACHES USING NEURAL NETWORKS 56
2.10.2 PROTEI
N THREADIN
G 57
2.11 BI-CLUSTERING 59
2.12 BIBLIOGRAPHIE REMARKS 60
3 DYNAMI
C
PROGRAMMIN
G
61
3.1 GENERAL PRINCIPLE OF DYNAMIC PROGRAMMIN
G 61
3.2 ALIGNMENT 63
3.2.1 PROBLE
M RESTATE
D 63
3.2.2 PARAMETERIZATIO
N 64
3.2.3 BELLMAN PRINCIPLE 64
3.2.4 RECURSIVE SOLUTION 64
3.2.5 NUMBER OF DIFFERENT SUBCALLS AN
D OVERALL COMPLEXITY .
. 65
3.2.6 TABULA
R ORGANIZATION OF TH
E BOTTOM-U
P COMPUTATION.
. 65
3.3 MULTIPLE ALIGNMENT 67
CONTENTS XV
3.4 AFFINE GA
P ALIGNMENT 67
3.4.1 PROBLEM RESTATE
D 67
3.4.2 PARAMETERIZATIO
N AN
D CONDITIONING 68
3.4.3 BELLMAN PRINCIPLE 68
3.4.4 RECURSIVE SOLUTION 68
3.4.5 NUMBER OF DIFFERENT SUBCALLS AND OVERALL COMPLEXITY .
. 69
3.5 EXON ASSEMBLY 69
3.5.1 PROBLEM RESTATE
D 69
3.5.2 PARAMETERIZATIO
N AND CONDITIONING 70
3.5.3 BELLMAN PRINCIPLE 70
3.5.4 RECURSIVE SOLUTION 71
3.5.5 NUMBER OF DIFFERENT SUBCALLS AN
D OVERALL COMPLEXITY .
. 72
3.5.6 PREPROCESSING OF SIMPLE TERMS 72
3.5.7 PARALLEL COMPUTATIO
N OF RECURSIVE TERMS 72
3.6 RNA STRUCTUR
E PREDICTION 73
3.6.1 PROBLEM RESTATE
D 73
3.6.2 PARAMETERIZATIO
N AND CONDITIONING 74
3.6.3 RECURSIVE SOLUTION AN
D BELLMAN PRINCIPLE 74
3.6.4 NUMBER OF DIFFERENT SUBCALLS AN
D OVERALL COMPLEXITY .
. 75
3.6.5 VARIATION: FREE ENERGY MINIMIZATION 76
3.7 VITERB
I ALGORITHM 83
3.7.1 PROBLEM RESTATE
D 83
3.7.2 PARAMETERIZATIO
N AN
D CONDITIONING 83
3.7.3 BELLMAN PRINCIPLE 83
3.7.4 RECURSIVE COMPUTATIO
N 83
3.7.5 COUNTING AN
D OVERALL COMPLEXITY 84
3.8 BAUM-WELCH ALGORITHM 84
3.8.1 PROBLEM MOTIVATION 84
3.8.2 A COUPLE OF IMPORTAN
T 'VARIABLES' 84
3.8.3 COMPUTING FORWARD VARIABLES 85
3.8.4 COMPUTING BACKWARD VARIABLES 85
3.8.5 COMPUTING TRANSITION VARIABLES 85
3.8.6 COMPUTING STAT
E VARIABLES 85
3.8.7 MODEL IMPROVEMENT 86
3.9 EXPRESSIVENESS OF VITERBI EQUATION
S 86
3.10 LIFTED PHYLOGENETIC ALIGNMENT 91
3.10.1 PROBLEM RESTATE
D 91
3.10.2 PARAMETERIZATIO
N AN
D CONDITIONING 92
3.10.3 BELLMAN PRINCIPLE 92
3.10.4 RECURSIVE SOLUTION 92
3.10.5 COUNTING AN
D OVERALL COMPLEXITY 93
3.10.6 PREPROCESSING OF SIMPLE TERMS 93
3.11 PROTEI
N THREADIN
G 93
3.11.1 PROBLEM RESTATE
D 93
3.11.2 PARAMETERIZATIO
N AN
D CONDITIONING 94
XVI CONTENTS
3.11.3 BELLMAN PRINCIPLE 94
3.11.4 RECURSIVE SOLUTION 94
3.11.5 COUNTING AN
D OVERALL COMPLEXITY 95
3.12 BIBLIOGRAPHIE REMARK
S 95
4
INTELLIGEN
T DAT
A STRUCTURE
S
97
4.1 REPRESENTATION MATTER
S 97
4.1.1 INTELLIGENT DAT
A STRUCTURE
S USED IN COMPUTE
R SCIENCE.
. 97
4.1.2 COVERING A TRUNCATE
D BOARD 97
4.1.3 CHOOSE THRE
E BIT
S AND WI
N 98
4.1.4 CHECK A PROOF 99
4.2 PQ-TREES 101
4.2.1 BASIC NOTIONS 101
4.2.2 TRANSFORMATION RULES FOR MARKED PQ-TREES 103
4.3 SUFFIX TREES 111
4.3.1 OUTLIN
E OF UKKONEN'S ALGORITHM AN
D BASIC PROCEDURES .11
1
4.3.2 INSERTIO
N PROCEDUR
E 112
4.3.3 SAVING SINGLE CHARACTE
R INSERTIONS 115
4.3.4 SAVING NAVIGATION STEPS 116
4.3.5 ESTIMATIO
N OF TH
E OVERALL NUMBER OF VISITED NODES .
. 117
4.3.6 UKKONEN'S THEOREM 120
4.3.7 COMMON SUFFIX TREE FOR SEVERAL STRINGS 123
4.3.8 APPLICATIONS OF SUFFIX TREES 124
4.4 LEAST COMMON ANCESTOR 131
4.4.1 MOTIVATION 131
4.4.2 LEAST COMMON ANCESTOR IN FUELL BINARY TREES 131
4.4.3 LEAST COMMON ANCESTOR IN ARBITRAR
Y TREES 135
4.5 SIGNED GENOME REARRANGEMENT 144
4.5.1 REALITY-DESIRE DIAGRAMS OF SIGNED PERMUTATION
S 144
4.5.2 "TING-PONG"
' OF LOWER AN
D UPPER BOUNDS 148
4.5.3 PADDIN
G RD-DIAGRAMS 159
4.5.4 SORTING PADDE
D GOOD COMPONENTS 161
4.5.5 SUMMARY OF TH
E SORTING PROCEDUR
E 164
4.6 BIBLIOGRAPHIE REMARK
S 166
5 NP-HARDNES
S O
F COR
E BIOINFORMATIC
S PROBLEM
S
167
5.1 GETTIN
G FAMILIAER WIT
H BASIC NOTIONS OF COMPLEXITY THEOR
Y .
. 167
5.2 COOK'S THEOREM: PRIME
R NP-COMPLET
E PROBLE
M 171
5.3 "ZOO" OF NP-COMPLET
E PROBLEMS 175
5.4 BRIDGE T
O BIOINFORMATICS: SHORTEST COMMON SUPER-SEQUENCE .
. 188
5.5 NP-COMPLETENESS OF CORE BIOINFORMATICS PROBLEMS 198
5.5.1 MULTIPLE ALIGNMENT 198
5.5.2 SHORTEST COMMON SUPERSTRIN
G 201
5.5.3 DOUBLE DIGEST 204
5.5.4 PROTEI
N THREADIN
G 204
CONTENTS XVII
5.5.5 BI-CLUSTERING 206
5.5.6 PSEUDOKNOT PREDICTION 209
5.5.7 TH
E PICTUR
E OF PRESENTED REDUCTIONS 213
5.5.8 FURTHE
R NP-COMPLETE BIOINFORMATICS PROBLEMS 213
5.6 BIBLIOGRAPHIE REMARKS 215
APPROXIMATIO
N ALGORITHM
S
217
6.1 BASICS OF APPROXIMATION ALGORITHMS 217
6.1.1 WHA
T IS A
N APPROXIMATION ALGORITHM? 217
6.1.2 FIRS
T EXAMPLE: METRIC TRAVELLING SALESMAN 217
6.1.3 LOWER AN
D UPPER BOUNDING 218
6.1.4 WHA
T ARE APPROXIMATION ALGORITHMS GOOD FOR? 219
6.2 SHORTEST COMMON SUPERSTRING PROBLEM 221
6.2.1 FEASIBLE LOWER BOUND 221
6.2.2 CYCLE COVERS RELATED T
O PERFECT MATCHINGS PROBLEM .
. 222
6.2.3 COMPUTING MAXIMUM COST PERFECT MATCHINGS 223
6.2.4 CYCLE COVER VERSUS CYCLIC COVERING BY STRINGS 225
6.2.5 SOME COMBINATORIAL STATEMENT
S ON CYCLIC COVERING .
. 226
6.2.6 4-APPROXIMATION ALGORITHM 228
6.2.7 PUTTIN
G THINGS TOGETHER 229
6.3 STEINER STRING 230
6.3.1 PROBLEM RESTATE
D 230
6.3.2 FEASIBLE LOWER BOUND 230
6.3.3 2-APPROXIMATION ALGORITHM 231
6.4 PHYLOGENETIC ALIGNMENT 232
6.5 MULTIPLE ALIGNMENT 234
6.5.1 FEASIBLE LOWER BOUND 234
6.5.2 2-APPROXIMATION ALGORITHM 235
6.6 SORTING UNSIGNED GENOMES 236
6.6.1 FEASIBLE LOWER BOUND 236
6.6.2 4-APPROXIMATION ALGORITHM 237
6.6.3 2-APPROXIMATION ALGORITHM 239
6.7 HP-MODEL PROTEI
N STRUCTUR
E PREDICTIO
N 243
6.7.1 FEASIBLE UPPER BOUND 243
6.7.2 ASYMPTOTIC 4-APPROXIMATION ALGORITHM 245
6.8 BIBLIOGRAPHIE REMARKS 248
A SELECTIO
N O
F METAHEURISTIC
S AN
D VARIOU
S PROJECT
S
249
7.1 MULTI-LAYER PERCEPTRON 249
7.1.1 ARCHITECTURE OF MULTI-LAYER PERCEPTRONS 250
7.1.2 FORWARD PROPAGATIO
N OF INPU
T SIGNALS 251
7.1.3 INPU
T AND OUTPU
T ENCODING 251
7.1.4 BACKPROPAGATION OF ERRO
R SIGNALS 252
7.1.5 TUNING BACKPROPAGATION 253
7.1.6 GENERALIZATION AND OVERFITTING 253
XVIII CONTENTS
7.1.7 APPLICATION T
O SECONDARY STRUCTUR
E PREDICTIO
N 254
7.1.8 FURTHE
R APPLICATIONS 254
7.2 SUPPOR
T VECTOR MACHINES 255
7.2.1 ARCHITECTUR
E OF SUPPOR
T VECTOR MACHINES 255
7.2.2 MARGIN MAXIMIZATION 256
7.2.3 PRIMA
L PROBLEM 257
7.2.4 DUA
L PROBLE
M 258
7.2.5 KERNEL FUNCTION TRICK 259
7.2.6 GENERALIZATION AN
D OVER-FITTIN
G 260
7.2.7 APPLICATION T
O CONTAC
T MA
P PREDICTIO
N 261
7.2.8 SOFT MARGIN MAXIMIZATION 263
7.2.9 SEMI-SUPERVISED SUPPOR
T VECTOR MACHINES 264
7.3 HIDDEN MARKOV MODELS 265
7.3.1 ARCHITECTUR
E OF HIDDEN MARKOV MODELS 266
7.3.2 CAUSES AN
D EFFECTS 266
7.3.3 BIOINFORMATICS APPLICATION: CG ISLANDS 268
7.3.4 FURTHE
R APPLICATIONS 269
7.4 GENETIC ALGORITHMS 269
7.4.1 BASIC CHARACTERISTICS OF GENETIC ALGORITHMS 269
7.4.2 APPLICATION T
O HP-FOLD OPTIMIZATION 271
7.4.3 FURTHE
R APPLICATIONS 272
7.5 ANT COLONY OPTIMIZATIO
N 272
7.5.1 BASIC CHARACTERISTIC
S OF ANT COLONY ALGORITHMS 273
7.5.2 APPLICATION T
O HP-FOLD OPTIMIZATION 275
7.5.3 FURTHE
R APPLICATIONS 276
7.6 BIBLIOGRAPHIE REMARKS 276
REFERENCES
279
INDEX
285 |
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| author | Sperschneider, Volker |
| author_facet | Sperschneider, Volker |
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| callnumber-first | Q - Science |
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| callnumber-raw | QH324.2 |
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| callnumber-subject | QH - Natural History and Biology |
| classification_rvk | ST 640 WC 7700 |
| ctrlnum | (OCoLC)227033870 (DE-599)DNB987806157 |
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| dewey-sort | 3572.80285 |
| dewey-tens | 570 - Biology |
| discipline | Biologie Informatik |
| discipline_str_mv | Biologie Informatik |
| format | Book |
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| id | DE-604.BV023370689 |
| illustrated | Not Illustrated |
| index_date | 2024-07-02T21:12:28Z |
| indexdate | 2024-07-09T21:17:04Z |
| institution | BVB |
| isbn | 9783540785057 3540785051 9783540785064 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016553954 |
| oclc_num | 227033870 |
| open_access_boolean | |
| owner | DE-20 DE-1028 DE-706 DE-11 DE-19 DE-BY-UBM |
| owner_facet | DE-20 DE-1028 DE-706 DE-11 DE-19 DE-BY-UBM |
| physical | XVIII, 289 S. |
| publishDate | 2008 |
| publishDateSearch | 2008 |
| publishDateSort | 2008 |
| publisher | Springer |
| record_format | marc |
| spelling | Sperschneider, Volker Verfasser aut Bioinformatics Problem Solving Paradigms Volker Sperschneider Berlin [u.a.] Springer 2008 XVIII, 289 S. txt rdacontent n rdamedia nc rdacarrier Mathematik Bioinformatics Bioinformatics Mathematics Computer algorithms Bioinformatik (DE-588)4611085-9 gnd rswk-swf Bioinformatik (DE-588)4611085-9 s DE-604 DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016553954&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Sperschneider, Volker Bioinformatics Problem Solving Paradigms Mathematik Bioinformatics Bioinformatics Mathematics Computer algorithms Bioinformatik (DE-588)4611085-9 gnd |
| subject_GND | (DE-588)4611085-9 |
| title | Bioinformatics Problem Solving Paradigms |
| title_auth | Bioinformatics Problem Solving Paradigms |
| title_exact_search | Bioinformatics Problem Solving Paradigms |
| title_exact_search_txtP | Bioinformatics Problem Solving Paradigms |
| title_full | Bioinformatics Problem Solving Paradigms Volker Sperschneider |
| title_fullStr | Bioinformatics Problem Solving Paradigms Volker Sperschneider |
| title_full_unstemmed | Bioinformatics Problem Solving Paradigms Volker Sperschneider |
| title_short | Bioinformatics |
| title_sort | bioinformatics problem solving paradigms |
| title_sub | Problem Solving Paradigms |
| topic | Mathematik Bioinformatics Bioinformatics Mathematics Computer algorithms Bioinformatik (DE-588)4611085-9 gnd |
| topic_facet | Mathematik Bioinformatics Bioinformatics Mathematics Computer algorithms Bioinformatik |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016553954&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT sperschneidervolker bioinformaticsproblemsolvingparadigms |