Berrar D.P., Dubitzky W., Granzow M. — Practical approach to microarray data analysis :: Электронная библиотека попечительского совета мехмата МГУ

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Berrar D.P., Dubitzky W., Granzow M. — Practical approach to microarray data analysis

Berrar D.P., Dubitzky W., Granzow M. — Practical approach to microarray data analysis

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Название: Practical approach to microarray data analysis

Авторы: Berrar D.P., Dubitzky W., Granzow M.

Аннотация:

A Practical Approach to Microarray Data Analysis is for all life scientists, statisticians, computer experts, technology developers, managers, and other professionals tasked with developing, deploying, and using microarray technology including the necessary computational infrastructure and analytical tools. The book addresses the requirement of scientists and researchers to gain a basic understanding of microarray analysis methodologies and tools. It is intended for students, teachers, researchers, and research managers who want to understand the state of the art and of the presented methodologies and the areas in which gaps in our knowledge demand further research and development. The book is designed to be used by the practicing professional tasked with the design and analysis of microarray experiments or as a text for a senior undergraduate- or graduate level course in analytical genetics, biology, bioinformatics, computational biology, statistics and data mining, or applied computer science.

Язык:

Рубрика: Computer science/Биоинформатика/

Статус предметного указателя: Готов указатель с номерами страниц

ed2k: ed2k stats

Год издания: 2003

Количество страниц: 368

Добавлена в каталог: 10.11.2005

Операции: Положить на полку | Скопировать ссылку для форума | Скопировать ID

Предметный указатель

measures      232
Accuracy      42 68 69 71 72 295 302
Activation function      207
Adaptive single linkage clustering      246
Adjusted Rand index      280 286
Affinity      277
Affymetrix      78 82 88 160 307 310 313
Alien sequences      57
All-pairs      181 182
Alternative polyadenylation      327
Alternative promoter usage      327
Alternative splicing      327
Analysis of variance      59
ANN      see “Artificial neural network”
Annotation software      341
anova      see “Analysis of variance”
AP      see “All-pairs”
application data      44
Application set      27 44
Apriori algorithm      297 301 302
Array profile      22 23 28 32
Artificial neural network      201 215
Association      37 40 42 43 45 289 290 292—298 300—304
Association rules      294 297 303
Audic and Claverie’s test      316
Audit trail      89 346 349 350
Auditability      345
Average linkage clustering      246 250 253
Average linkage hierarchical clustering      133
Backward elimination      112
Bagging      355
Bayes classifier      159 161
Bayes decision rule      197
Bayes rule      138 139 140
Bayesian belief networks      see “Bayesian network”
Bayesian feature selection      111 125 129
Bayesian information criterion      285
Bayesian network      120 150 151 153 154 156—163
Bayesian optimal classifier      126
Best-first search      112
Bias      50—52 54 57 59 77 78 80 84—86 88 136 143—145 148
Bias-variance trade-off      144
BIC      see “Bayesian information criterion”
Biclustering      234
Binomial distribution      313 314
Bioinformatics      91
Black holes      53
Blessing of smoothness      167 184
Bonferroni correction      217
Boosting      146 148
Bottom-up      246 252
Calibration      208 210
CART      see “Classification And Regression Tree”
Cast      see “Cluster affinity search technique”
Category utility      128
CCM      see “Compound covariate method”
cDNA      9 10 14 15 18 46 51 52 56 132 133 136 148 187 200 267
Cell-cycle data      96 97 100 103
Centralization      55 56 57 58 60 61
Chaining effect      251 252
Chebychev metric      240 243
Cheminformatics      348 357
Chi-square      294 297
Chi-square test      314—316
Chromosome      219 221 222 223 224 225 226
Classification      100 106 110 113 114 118 119 122 124 125 127 128 132 133—139 141 142 143—148 150—154 157—163 166—168 174—177 179—185 201—203 205—215
Classification and Regression Tree      66 142 198
Classification Rule with Unbiased Interaction Selection and Estimation      198
Classification task      40
Classification tree      see “Decision tree”
Classifier      134—138 140—146 202 206 210 211
CLICK algorithm      129
Cluster      133 135 230—243 274 277—282 284 287
Cluster affinity search technique      274 277
Clustering      103 105 106 110 111 127—129 186 196 199 230 231 233 235 238 242 274 282
Clustering task      41
Codebook      181 182
Coefficient of variation      87
Committee      159 210 212 213
Compactness      232
Complete linkage clustering      246 250 252
Compound covariate method      186 188 192 193 199
Comprehensive software      330 331 333 334 341
Confidence      13 30 34 42 175 181 182 295—297
Consensus trees      244
Contingency table      293—296
continuous      99 103 106 112 114 128
Control      11
Convex admissible      235
Correlation      36 41 96 100—103 107 290—294 299 300 303
Correlation coefficient      276 278
Covariance      81 127 140 219 248 251
Coverage      42 295 302
Cross-validation      43 44 119 120 122—124 141—146 202 204 209—214
Curse of dimensionality      110 167 184
Cy3      8 29
Cy5      8 29
Dag      see “Directed-acyclic graph”
Data filtering      47 49 61 62
Data inspection      47 60 61
Data Mining      294 297
data transformation      47 59
Decision rule      118 126
Decision surface      209
Decision tree      118 150 158 161 354 355
Dendrogram      250—259
Dependence tree      120 121
DHP      see “Direct Hashing and Pruning”
Diagonal linear discriminant analysis      140
Diagonal quadratic discriminant analysis      140
DIC      see “Dynamic Itemset Counting”
Differential display      7
Dimensional reduction      202
Dimensionality reduction      95 105 330 331
Direct Hashing and Pruning      297
Directed-acyclic graph      152
Discretization      117 154
Discriminant analysis      133 134 135 139 141 146
Dissimilarity      275 276 279
DLDA      see “Diagonal linear discriminant analysis”
DNA      48 58
Double self-organizing map      268
DQDA      see “Diagonal quadratic discriminant analysis”
Dunn’s index      232 238
Dye-swap      78 85 86 89
Dyes      8 29 51 56 57 59
Dynamic Itemset Counting      297
Early stopping      211
Eigenarray      94
Eigenassays      94 96 98 100 102
Eigengenes      67 68 71 74 94 95—101 103 104
Eigenvalues      67 69
Eigenvector      127
Elbow      98 103 104
em      see “Expectation maximization”
Empirical error      174
entropy      115 128 129 189
epochs      208 210 211 271
ESTs      see “Expressed sequence tags”
Euclidean distance      67 141 189 193 220 248 264 276 278
Exons      5
Expectation maximization      68 156 284
Experimental study      298
Experimenter bias      77
Explicit feature selection      111
Expressed sequence tags      327
Expression profile      16 21 22 28 36 40 92 95 96 100 105
Extension/accessory software      330
External criterion analysis      280
False positive      217

False positive rate      190
Fast self-organizing feature map      268
Feature reduction      217
Feature selection      110 111 113 119 122 124—126 128 129 134 136 141—143 145 146
Feature weighting      111
Feed-forward network      201
Figure of merit      281
Filter      111 112 118 122 123 142 176
Filtering      49 61 62 63
Fisher’s exact test      314—316 338
Fitness      219 222—224
Fluorescent      53
FOM      see “Figure of merit”
Forward selection      112
FP-growth      297
FPR      see “False positive rate”
GA      see “Genetic algorithm”
Gaussian distribution      112 114
Gaussian kernel      173 183
Gaussian maximum likelihood discriminant rule      139
Gaussian mixture model      282
Gaussian quadratic classifier      123
GCs      see “Growing cell structures”
Gene profile      16 21 23 35 37 40
Generalization error      138 143 144 174
Generalized linear model      124
Genetic algorithm      219
Genetic risk group      302
Gibbs sampler      126
GLIM      see “Generalized linear model”
Global normalization      81
Gradient descent algorithm      208
Gram — Schmidt orthogonalization      93
Group average linkage clustering      250
Growing cell structure      234 269
Growing self-organizing map      269
GSOM      see “Growing self-organizing map”
Heterogeneity      232 235 241
Heuristic-based clustering      277 280
Hidden layer      206
Hierarchical agglomerative clustering      246 249
Hierarchical clustering      52 58 62 65 133 217 232 233 236 244 246 247 250 251 259 278 284
Hill-climbing      112 120 156
Hinge loss function      173
Homogeneity      281
Housekeeping genes      57 79
hybridization      2 7—10 12 14 26—28 30 34 35 39 49 51 53 58 59 61 62 77 78 80 83 85 86 88
Hypergeometric distribution      313—315
Hyperplane      168—170 171 172 175 176
IgG      see “Incremental grid growing neural network”
Ill-posed problem      167
Image analysis      49 54 56 61—63
Image analysis software      328 329
Image processing      53 77
Incremental grid growing neural network      269
Induction      110—112 118 129
Inductive bias      119 123
Info Score      see “Mutual information scoring”
Information gain      114 115 117 118 122 129
Input layer      206
Intensity dependent methods      83
Intensity independent methods      81
Internal criterion analysis      280
Internet      49
Intra-cluster diameter metrics      232
Introns      5
Isolation      232
K-means      65 217 233 236 261 265 274 277—279 284
k-nearest neighbor      179 194 217 222
Kendall’s tau      291 292 293 294 299
Kernel function      172 173
KNN      see “k-nearest neighbor”
KNNimpute      66—70 72—74
Kohonen map      see “Self-organizing map”
Kohonen Self-Organizing Feature Map      see “Self-organizing map”
Kullback — Leibler divergence      115
Laboratory Information Management System      339
Landing lights      53
Large p, small n-problem      137
Lattice machine      42 43
LDA      see “Linear discriminant analysis”
Learning      110—112 118 119 124 125 129 201 202 209 210 215
Learning cycle      264
Learning rate      264 265 269
Learning set      44 135 136 138 140 141 143—145 216 217 220
Learning Vector Quantization      139
Least squares estimates      66
Leave-one-out cross validation      161
Leave-one-out cross-validation      45 123 124 141 144 194
Leukemia      110 113 115 117 118 123 160—163 166 174 179 180 218 234 242 243 262 265 266
Lift      145
Likelihood      66 113 121 155 156 158 249 250—252 284 285
Linear discriminant analysis      140 217
Locally weighted regression      56 57 84
Log bases      55
Logistic linear classifier      123
Logistic regression      139
Logistic regression classifier      123 124
LOOCV      see “Leave-one-out cross-validation”
Loss function      137 138 146
LOWESS      see “Locally weighted regression”
Lowess smoothing      84 85
M vs. A plot      57 60
M-A plot      83 84 85 86
Machine learning      110 111 202 234 289 299 357
Macro-cluster      271
Mahalanobis distance      140
Manhattan metric      240 243
Margin      167 170 171—174 177 179 184
Market basket analysis      294 301 303
Markov blanket      115—118 122 129 158 159
Markov chain Monte Carlo      126
Matrix stability theory      127
Maximum association algorithm      302
Maximum Entropy Discrimination      126
MDL      see “Minimum description length”
Mean squared error      207 211 212
Medoids      279
membership      234 241 244
Metric variables      290 291 292
Metropolis — Hasting algorithm      126
MIAME      48 63
Microarray      47 48
Microarray Gene Expression Data Group      319
Microarray software      326 339
Microarrayers      20
Minimum description length      156
Minimum Information About a Microarray Experiment      319 331
Misclassification rate      138
Missing value      13 28 31 32 65
Mixture model      114 115
MLP      see “Multilayer perceptron”
Model application      43 44
Model construction      2 43 44
Model validation      43
Model verification      43
Model-based clustering      246 248 249 250—252 274 277 279 282 284 285
Momentum coefficient      208 209
Monotone admissible      235
mRNA      56 57 111 133 134 136
MSE      see “Mean-squared error”
Multiclass      180 181 183 184
Multidimensional scaling      108 127 130
Multilayer perceptron      161 206
Multivariate analysis      52 58
Mutual information      154 155
Mutual-information scoring      187 188
Naive Bayes method      139
Near-optimal      224 225 226
Near-optimal chromosome      224 226

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