Data mining for genomics and proteomics: analysis of gene and protein expression data
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Hoboken, N.J.
Wiley
2010
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| Schriftenreihe: | Wiley series on methods and applications in data mining
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| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XV, 319, [6] S. Ill., graph. Darst. |
| ISBN: | 9780470163733 |
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| adam_text | DATA MINING FOR GENOMICS AND PROTEOMICS ANALYSIS OF*GENE AND PROTEIN
EXPRESSION DATA DARIUS M. DZIUDA (WILEY A JOHN WILEY & SONS, INC.,
PUBLICATION CONTENTS PREFACE ACKNOWLEDGMENTS 1 INTRODUCTION 1.1 BASIC
TERMINOLOGY 1.1.1 THE CENTRAL DOGMA OF MOLECULAR BIOLOGY 1.1.2 GENOME
1.1.3 PROTEOME 1.1.4 DNA (DEOXYRIBONUCLEIC ACID) 1.1.5 RNA (RIBONUCLEIC
ACID) 1.1.6 MRNA (MESSENGER RN A) 1.1.7 GENETIC CODE 1.1.8 GENE 1.1.9
GENE EXPRESSION AND THE GENE EXPRESSION LEVEL 1.1.10 PROTEIN 1.2
OVERLAPPING AREAS OF RESEARCH 1.2.1 GENOMICS 1.2.2 PROTEOMICS 1.2.3
BIOINFORMATICS 1.2.4 TRANSCRIPTOMICS AND OTHER -OMICS ... 1.2.5 DATA
MINING 2 BASIC ANALYSIS OF GENE EXPRESSION MICROARRAY DATA 2.1
INTRODUCTION 2.2 MICROARRAY TECHNOLOGY 2.2.1 SPOTTED MICROARRAYS 2.2.2
AFFYMETRIX GENECHIP MICROARRAYS 2.2.3 BEAD-BASED MICROARRAYS 2.3
LOW-LEVEL PREPROCESSING OF AFFYMETRIX MICROARRAYS 2.3.1 MAS5 2.3.2 RMA
2.3.3 GCRMA 2.3.4 PLIER 2.4 PUBLIC REPOSITORIES OF MICROARRAY DATA 2.4.1
MICROARRAY GENE EXPRESSION DATA SOCIETY (MGED) STANDARDS 2.4.2 PUBLIC
DATABASES 2.4.2.1 GENE EXPRESSION OMNIBUS (GEO) 2.4.2.2 ARRAYEXPRESS
XVII 1 2 2 3 4 5 6 7 7 9 12 13 14 14 14 14 14 15 17 17 18 19 20 24 25 27
31 33 34 34 34 37 37 38 VII VIII CONTENTS 2.5 GENE EXPRESSION MATRIX 38
2.5.1 ELEMENTS OF GENE EXPRESSION MICROARRAY DATA ANALYSIS 42 2.6
ADDITIONAL PREPROCESSING, QUALITY ASSESSMENT, AND FILTERING 43 2.6.1
QUALITY ASSESSMENT 45 2.6.2 FILTERING 50 2.7 BASIC EXPLORATORY DATA
ANALYSIS 52 2.7.1 FTEST 54 2.7.1.1 T TEST FOR EQUAL VARIANCES 55 2.7.1.2
/ TEST FOR UNEQUAL VARIANCES 55 2.7.2 ANOVA F TEST 56 2.7.3 SAM T
STATISTIC 57 2.7.4 LIMMA . 59 2.7.5 ADJUSTMENT FOR MULTIPLE COMPARISONS
59 2.7.5.1 SINGLE-STEP BONFERRONI PROCEDURE 61 2.7.5.2 SINGLE-STEP SIDAK
PROCEDURE 61 2.7.5.3 STEP-DOWN HOLM PROCEDURE 61 2.7.5.4 STEP-UP
BENJAMINI AND HOCHBERG PROCEDURE 62 2.7.5.5 PERMUTATION BASED
MULTIPLICITY ADJUSTMENT 63 2.8 UNSUPERVISED LEARNING {TAXONOMY-RELATED
ANALYSIS) 64 2.8.1 CLUSTER ANALYSIS 65 2.8.1.1 MEASURES OF SIMILARITY OR
DISTANCE 67 2.8.1.2 A -MEANS CLUSTERING 70 2.8.1.3 HIERARCHICAL
CLUSTERING 71 2.8.1.4 TWO-WAY CLUSTERING AND RELATED METHODS 78 2.8.2
PRINCIPAL COMPONENT ANALYSIS 80 2.8.3 SELF-ORGANIZING MAPS 85 EXERCISES
90 3 B10MARKER DISCOVERY AND CLASSIFICATION 95 3.1 OVERVIEW 95 3.1.1
GENE EXPRESSION MATRIX ... AGAIN 98 3.1.2 BIOMARKER DISCOVERY 100 3.1.3
CLASSIFICATION SYSTEMS 105 3.1.3.1 PARAMETRIC AND NONPARAMETRIC LEARNING
ALGORITHMS 106 3.1.3.2 TERMS ASSOCIATED WITH COMMON ASSUMPTIONS
UNDERLYING PARAMETRIC LEARNING ALGORITHMS 106 3.1.3.3 VISUALIZATION OF
CLASSIFICATION RESULTS 110 3.1.4 VALIDATION OF THE CLASSIFICATION MODEL
111 3.1.4.1 RECLASSIFICATION 111 3.1.4.2 LEAVE-ONE-OUT AND K-FOLD
CROSS-VALIDATION 111 3.1.4.3 EXTERNAL AND INTERNAL CROSS-VALIDATION 112
3.1.4.4 HOLDOUT METHOD OF VALIDATION 113 3.1.4.5 ENSEMBLE-BASED
VALIDATION (USING OUT-OF-BAG SAMPLES) 113 3.1.4.6 VALIDATION ON AN
INDEPENDENT DATA SET 114 3.1.5 REPORTING VALIDATION RESULTS 114 3.1.5.1
BINARY CLASSIFIERS 115 3.1.5.2 MULTICLASS CLASSIFIERS 117 3.1.6
IDENTIFYING BIOLOGICAL PROCESSES UNDERLYING THE CLASS DIFFERENTIATION
119 3.2 FEATURE SELECTION ; 119 3.2.1 INTRODUCTION 119 CONTENTS IX 3.2.2
UNIVARIATE VERSUS MULTIVARIATE APPROACHES 121 3.2.3 SUPERVISED VERSUS
UNSUPERVISED METHODS 123 3.2.4 TAXONOMY OF FEATURE SELECTION METHODS 126
3.2.4.1 FILTERS, WRAPPERS, HYBRID, AND EMBEDDED MODELS 126 3.2.4.2
STRATEGY: EXHAUSTIVE, COMPLETE, SEQUENTIAL, RANDOM, AND HYBRID SEARCHES
131 3.2.4.3 SUBSET EVALUATION CRITERIA 133 3.2.4.4 SEARCH-STOPPING
CRITERIA 133 3.2.5 FEATURE SELECTION FOR MULTICLASS DISCRIMINATION 133
3.2.6 REGULARIZATION AND FEATURE SELECTION 134 3.2.7 STABILITY OF
BIOMARKERS 135 3.3 DISCRIMINANT ANALYSIS 136 3.3.1 INTRODUCTION 136
3.3.2 LEARNING ALGORITHM 139 3.3.3 A STEPWISE HYBRID FEATURE SELECTION
WITH T 2 147 3.4 SUPPORT VECTOR MACHINES 149 3.4.1 HARD-MARGIN SUPPORT
VECTOR MACHINES 150 3.4.2 SOFT-MARGIN SUPPORT VECTOR MACHINES 157 3.4.3
KERNELS 160 3.4.4 SVMS AND MULTICLASS DISCRIMINATION 165 3.4.4.1
ONE-VERSUS-THE-REST APPROACH 165 3.4.4.2 PAIRWISE APPROACH 165 3.4.4.3
ALL-CLASSES-SIMULTANEOUSLY APPROACH 166 3.4.5 SVMS AND FEATURE
SELECTION: RECURSIVE FEATURE ELIMINATION 166 3.4.6 SUMMARY 167 3.5
RANDOM FORESTS 168 3.5.1 INTRODUCTION 168 3.5.2 RANDOM FORESTS LEARNING
ALGORITHM 172 3.5.3 RANDOM FORESTS AND FEATURE SELECTION 174 3.5.4
SUMMARY 176 3.6 ENSEMBLE CLASSIFIERS, BOOTSTRAP METHODS, AND THE
MODIFIED BAGGING SCHEMA 177 3.6.1 ENSEMBLE CLASSIFIERS 177 3.6.1.1
PARALLEL APPROACH 177 3.6.1.2 SERIAL APPROACH 177 3.6.1.3 ENSEMBLE
CLASSIFIERS AND BIOMARKER DISCOVERY 177 3.6.2 BOOTSTRAP METHODS 178
3.6.3 BOOTSTRAP AND LINEAR DISCRIMINANT ANALYSIS 179 3.6.4 THE MODIFIED
BAGGING SCHEMA 180 3.7 OTHER LEARNING ALGORITHMS 182 3.7.1 FC-NEAREST
NEIGHBOR CLASSIFIERS 183 3.7.2 ARTIFICIAL NEURAL NETWORKS 185 3.7.2.1
PERCEPTRON 186 3.7.2.2 MULTILAYER FEEDFORWARD NEURAL NETWORKS 187
3.7.2.3 TRAINING THE NETWORK (SUPERVISED LEARNING) 192 3.8 EIGHT
COMMANDMENTS OF GENE EXPRESSION ANALYSIS (FOR BIOMARKER DISCOVERY) 197
EXERCISES 198 4 THE INFORMATIVE SET OF GENES 201 4.1 INTRODUCTION * 201
4.2 DEFINITIONS 202 X CONTENTS 4.3 THE METHOD ** 202 4.3.1
IDENTIFICATION OF THE INFORMATIVE SET OF GENES 203 4.3.2 PRIMARY
EXPRESSION PATTERNS OF THE INFORMATIVE SET OF GENES 208 4.3.3 THE MOST
FREQUENTLY USED GENES OF THE PRIMARY EXPRESSION PATTERNS 211 4.4 USING
THE INFORMATIVE SET OF GENES TO IDENTIFY ROBUST MULTIVARIATE BIOMARKERS
211 4.5 SUMMARY 212 EXERCISES 215 5 ANALYSIS OF PROTEIN EXPRESSION DATA
219 5.1 INTRODUCTION 219 5.2 PROTEIN CHIP TECHNOLOGY 222 5.2.1 ANTIBODY
MICROARRAYS 223 5.2.2 PEPTIDE MICROARRAYS 225 5.2.3 PROTEIN MICROARRAYS
225 5.2.4 REVERSE PHASE MICROARRAYS 226 5.3 TWO-DIMENSIONAL GEL
ELECTROPHORESIS 226 5.4 MALDI-TOF AND SELDI-TOF MASS SPECTROMETRY 228
5.4.1 MALDI-TOF MASS SPECTROMETRY 229 5.4.2 SELDI-TOF MASS SPECTROMETRY
230 5.5 PREPROCESSING OF MASS SPECTROMETRY DATA 232 5.5.1 INTRODUCTION
232 5.5.2 ELEMENTS OF PREPROCESSING OF SELDI-TOF MASS SPECTROMETRY DATA
234 5.5.2.1 QUALITY ASSESSMENT 234 5.5.2.2 CALIBRATION 235 5.5.2.3
BASELINE CORRECTION 235 5.5.2.4 NOISE REDUCTION AND SMOOTHING 235
5.5.2.5 PEAK DETECTION 235 5.5.2.6 INTENSITY NORMALIZATION 236 5.5.2.7
PEAK ALIGNMENT ACROSS SPECTRA 237 5.6 ANALYSIS OF PROTEIN EXPRESSION
DATA 237 5.6.1 ADDITIONAL PREPROCESSING 239 5.6.2 BASIC EXPLORATORY DATA
ANALYSIS 239 5.6.3 UNSUPERVISED LEARNING 240 5.6.4 SUPERVISED
LEARNING*FEATURE SELECTION AND BIOMARKER DISCOVERY 242 5.6.5 SUPERVISED
LEARNING*CLASSIFICATION SYSTEMS 243 5.7 ASSOCIATING BIOMARKER PEAKS WITH
PROTEINS 244 5.7.1 INTRODUCTION 244 5.7.2 THE UNIVERSAL PROTEIN RESOURCE
(UNIPROT) 246 5.7.3 SEARCH PROGRAMS 247 5.7.4 TANDEM MASS SPECTROMETRY
249 5.8 SUMMARY 251 6 SKETCHES FOR SELECTED EXERCISES 253 6.1
INTRODUCTION 253 6.2 MULTICLASS DISCRIMINATION (EXERCISE 3.2) 254 6.2.1
DATA SET SELECTION, DOWNLOADING, AND CONSOLIDATION 254 6.2.2 FILTERING
PROBE SETS 256 6.2.3 DESIGNING A MULTISTAGE CLASSIFICATION SCHEMA 257
CONTENTS XI 6.3 IDENTIFYING THE INFORMATIVE SET OF GENES (EXERCISES
4.2-4.6) 265 6.3.1 THE INFORMATIVE SET OF GENES 266 6.3.2 PRIMARY
EXPRESSION PATTERNS OF THE INFORMATIVE SET 267 6.3.3 THE MOST FREQUENTLY
USED GENES OF THE PRIMARY EXPRESSION PATTERNS 270 6.4 USING THE
INFORMATIVE SET OF GENES TO IDENTIFY ROBUST MULTIVARIATE MARKERS
(EXERCISE 4.8) 271 6.5 VALIDATING BIOMARKERS ON AN INDEPENDENT TEST DATA
SET (EXERCISE 4.8) 272 6.6 USING A TRAINING SET THAT COMBINES MORE THAN
ONE DATA SET (EXERCISES 3.5 AND 4.1 -4.8) 274 6.6.1 COMBINING THE TWO
DATA SETS INTO A SINGLE TRAINING SET 275 6.6.2 FILTERING PROBE SETS OF
THE COMBINED DATA 276 6.6.3 ASSESSING THE DISCRIMINATORY POWER OF THE
BIOMARKERS AND THEIR GENERALIZATION 276 6.6.4 IDENTIFYING THE
INFORMATIVE SET OF GENES 276 6.6.5 PRIMARY EXPRESSION PATTERNS OF THE
INFORMATIVE SET OF GENES 280 6.6.6 THE MOST FREQUENTLY USED GENES OF THE
PRIMARY EXPRESSION PATTERNS 281 6.6.7 USING THE INFORMATIVE SET OF GENES
TO IDENTIFY ROBUST MULTIVARIATE MARKERS 285 6.6.8 VALIDATING BIOMARKERS
ON AN INDEPENDENT TEST DATA SET 287 REFERENCES 289 INDEX 307
|
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| id | DE-604.BV036493831 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T22:41:36Z |
| institution | BVB |
| isbn | 9780470163733 |
| language | English |
| lccn | 2009052129 |
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| physical | XV, 319, [6] S. Ill., graph. Darst. |
| publishDate | 2010 |
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| series2 | Wiley series on methods and applications in data mining |
| spelling | Dziuda, Darius M. Verfasser aut Data mining for genomics and proteomics analysis of gene and protein expression data Darius M. Dziuda Hoboken, N.J. Wiley 2010 XV, 319, [6] S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Wiley series on methods and applications in data mining Datenverarbeitung Genomics Data processing Proteomics Data processing Data mining Datenverarbeitung (DE-588)4011152-0 gnd rswk-swf Genanalyse (DE-588)4200230-8 gnd rswk-swf Proteomanalyse (DE-588)4596545-6 gnd rswk-swf Genanalyse (DE-588)4200230-8 s Datenverarbeitung (DE-588)4011152-0 s DE-604 Proteomanalyse (DE-588)4596545-6 s HEBIS Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020416398&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Dziuda, Darius M. Data mining for genomics and proteomics analysis of gene and protein expression data Datenverarbeitung Genomics Data processing Proteomics Data processing Data mining Datenverarbeitung (DE-588)4011152-0 gnd Genanalyse (DE-588)4200230-8 gnd Proteomanalyse (DE-588)4596545-6 gnd |
| subject_GND | (DE-588)4011152-0 (DE-588)4200230-8 (DE-588)4596545-6 |
| title | Data mining for genomics and proteomics analysis of gene and protein expression data |
| title_auth | Data mining for genomics and proteomics analysis of gene and protein expression data |
| title_exact_search | Data mining for genomics and proteomics analysis of gene and protein expression data |
| title_full | Data mining for genomics and proteomics analysis of gene and protein expression data Darius M. Dziuda |
| title_fullStr | Data mining for genomics and proteomics analysis of gene and protein expression data Darius M. Dziuda |
| title_full_unstemmed | Data mining for genomics and proteomics analysis of gene and protein expression data Darius M. Dziuda |
| title_short | Data mining for genomics and proteomics |
| title_sort | data mining for genomics and proteomics analysis of gene and protein expression data |
| title_sub | analysis of gene and protein expression data |
| topic | Datenverarbeitung Genomics Data processing Proteomics Data processing Data mining Datenverarbeitung (DE-588)4011152-0 gnd Genanalyse (DE-588)4200230-8 gnd Proteomanalyse (DE-588)4596545-6 gnd |
| topic_facet | Datenverarbeitung Genomics Data processing Proteomics Data processing Data mining Genanalyse Proteomanalyse |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020416398&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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