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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: en

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
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Предметный указатель
Nearest neighbor      66 123 134 137 139 141 142 145 146 148
nearest neighbors      68 70 73 74
Nearest shrunken centroids      217
Neural networks      see “Artificial neural networks”
Neurone      263
Niche      221 222 224
Noise      97
Noise-tolerant      235
Noisy data      74
Nominal variables      290
Normality      55 61
Normalization      54 56—59 76—89
Northern blots      7
NP-hard      156 157
Observational study      298
Oligonucleotide chips      132 133
One-versus-all      181
ORDERED-FS      122 123 129
Ordinal      99 106
Ordinal variables      290
Orthogonality      203
Output layer      206 207
OVA      see “One-versus-all”
Over-expressed      154 155 160
Overfitting      44 198 207 210 211 213 285
P-metric      154 160 176
Paired-slide normalization      85
PAM      see “Partitioning around medoids”
Partial derivative      212
Partitioning around medoids      274
Pathway      308 317—320
Pathway reconstruction software      330 338
Pattern recognition      111 217
Pattern-detection      1 40
PCA      see “Principal component analysis”
Pearson correlation      67 128
Pearson’s contingency coefficient      291 293
Pearson’s product-moment correlation coefficient      290 291 294
Pearson’s rho      see “Pearson’s product-moment correlation coefficient”
Perceptron      206 207 215
Permutation test      178
Point proportion admissible      235
Poisson distribution      316
Polymerase chain reaction      45
Population      50 219 221
Power      50
pre-processing      47 49 61 63 77
Prediction set      see “Application set”
Predictive modeling      1 27
Predictor      see “Classifier”
Principal component analysis      65 91 93 127 203 214
Probe      8—10 12 15 18 19 26 30 31
Projection      101—103 107
Projection pursuit      139
Protein      48
Prototype      263—269
QDA      see “Quadratic discriminant analysis”
Quadratic discriminant analysis      139 140
Quest      see “Quick Unbiased Efficient
Quick, Unbiased, Efficient Statistical Tree      198
Rand index      see “Adjusted Rand index”
Random error      78
Random permutation tests      202 211
Randomization      50 51
Randomized inference method      66
Ratio statistics      87
Re-scaling      55 58 61
Receiver operating characteristic      159
Recursive feature elimination      176
Recursive partitioning      217
Reference      10—14 17 34
Reference chip      82 88
Regression methods      81
Regularization      166 173 174 183
Regulatory networks      112 299
Relevance machine      42
Reporter molecules      8 11 12
Residual effects      50
Response function      124—126
Reverse-engineering of the genetic networks      300
Reverse-labelling      51
RFE      see “Recursive feature elimination”
RMS      see “Root mean squared”
RNA      52 56
ROC      see “Receiver operating characteristic”
Root mean squared      68
Roulette-wheel selection      222
Rule induction      354
S2N      see “Signal-to-noise”
SAM      see “Significance analysis of microarrays”
Sample preparation      77
SANN      see “Self-adaptive and incremental learning neural network”
Scalability      247
Scanner settings      77
Scatter plots      100 102
Scoring metric      156 160
Scree plots      98
Search tree      119—121
Second order polynomial kernel      173
Self-adaptive and incremental learning neural network      268
Self-organizing map      66 108 217 233 333
Self-organizing neural network      261
Sensitivity      159
Separability score method      301
Separation      281
Sequence data      48
Serial analysis of gene expression      7
Signal-to-noise      175 176 178
Signature      22
Significance analysis of microarrays      187 188
Significance threshold      62
Silhouette      232 237 241—243 281
Similarity      275—279 281
Simplified fuzzy ARTMAP      234
Single linkage clustering      246 247 250 251—259
Single slide normalization      83
Singular value      92 93 95 96 98 99 104
Singular value decomposition      65—67 91 214
Singular value spectrum      98 99
Smoothness      167 173
Soft margin      171
SOM      see “Self-organizing map”
Spatial effects      77
Spearman’s rho      291—294 299
Specific analysis software      330 333
Specificity      159
Spherical model      279 283 284 286
Spiking controls      57
splicing      5 6 19
Standardization      145 146
Statistical design      50
Statistical software      335
Statistics      111 112
Step size      208 209
Subsymbolic      43
Sum of squares      232
Supervised learning      111 133 135 136 217 218
Supervised network self-organizing map      268
Support      42 295 296 297
Support vector      172 217 218
Support vector machine      42 43 119 126 134 161 166 167 185 202 217
SVD      see “Singular value decomposition”
SVDimpute      66 67 68 71 72 73
SVDPACKC      107
Swiss-Prot      318 320 322
Symbolic      23 39 43 46
Systematic error      see “Bias”
Systems biology      94—96 100 233
t-test      187 188 190—192 195 199
tan      see “Tree-augmented naive Bayes classifier”
TARGET      5 10—12 14 19 34 39
Terminology      47 51 55
Test data      43
Test set      44 143—145 211
Tikhonov regularization principle      174
Time series      97 104—106
Top-down      246 252
Top-ranked genes      224—226
Total intensity methods      79
Total least squares      82
Total risk      138
Toxicogenomics      223
Training      111 112 114 119 121 123 125—127 155 156 159 160
Training data      43
Training set      44
Transcriptional response      92 95—97 100 101—106
Transformation      54 55 58—61 63
Tree-augmented naive Bayes classifier      159
Tukey’s compound covariate      217
Turnkey system      330 331
Two-sample t-test      217
Type I error      50
Uncertainty      244
Under-expressed      154 155 160
Univariate mixture model      113
Unsupervised learning      111 127 135 136 217
Validation      52 54
Validation data      27 43 44
Validation set      27 44 45 144 208 210 211
Variance      136 141 143 144 148
Variation      47 49 56 60
Voting Gibbs classifier      126
Ward’s method      251
Weight decay      208 209 211
Weight vector      125
Weighted flexible compound covariate method      188 193
Weighted gene analysis      187 188 200
Weighted voting average      179
Well-posed problem      167
WFCCM      see “Weighted flexible compound covariate method”
WGA      see “Weighted gene analysis”
Wilcoxon      218
Winnow      111 124 125
Work flow      348 349 351 353 358 359
Wrapper      111 119 122 123 128 142 176
Yeast      50 96 104 105 262 274 278 285 286
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