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Raychaudhury S. — Computational text analysis for functional genomics and bioinformatics
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Название: Computational text analysis for functional genomics and bioinformaticsАвтор: Raychaudhury S. Аннотация: This book brings together the two disparate worlds of computational text analysis and biology and presents some of the latest methods and applications to proteomics, sequence analysis and gene expression data. Modern genomics generates large and comprehensive data sets but their
Язык: Рубрика: Биология /Статус предметного указателя: Готов указатель с номерами страниц ed2k: ed2k stats Год издания: 2006Количество страниц: 312Добавлена в каталог: 11.12.2007Операции: Положить на полку |
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Tamames, J., Ouzounis, C. et al. 7 Tamayo, P., Slonim, D. et al. 69 Term frequency 85 91 Term frequency vectors 90—1 Term weighting schemes 91 Tertiary structure, proteins 25 Text classification algorithms 195 202—3 Text classification algorithms, assignment to functional categories 212—13 Text classification algorithms, feature selection 210—12 Text classification algorithms, maximum entropy modeling 205—10 207 Text classification algorithms, naive Bayes text classification scheme 204—5 Text classification algorithms, nearest neighbor classification 203—4 Text classification algorithms, use in gene name recognition 235—6 Text matrix (T) 143 Text mining 2 Text mining, potential uses, candidate gene identification 8 Text mining, potential uses, database building 5 7 Text mining, potential uses, genomic data set analysis 7—8 Text mining, relevance of sequence analysis 38 Text resources, electronic text 9 Text resources, genome databases 9—11 Text, use in sequence analysis 107—9 Text-based methods, advantages 12—13 Textual information, combination with sequence information 120—21 Textual profiles, use in organization of sequence hits 114 Textual representation of genes 94—6 Textual similarity as indicator of homology 114—15 Textual similarity, relationship to biological similarity 97—9 Thomas, J., Milward, D. et al. 7 262 Thompson, J.D., Higgins, D.G. et al. 48 Threonine 25 Thymine 18 19 Tokenize step, gene name finding algorithm 240—1 Traceable Author Statements (TAS) 188 198 199 Tracing back, dynamic programming 47 Training examples 202 208 213 214—16 Training examples, construction 224 Training hidden Markov models 59—61 Transcription factor proteins 23 245 247 Transcription factors 24 Transcription initiation sites 22—3 22 Transcription of DNA 21 22 Transcription stop sites 22 23 Transfer RNA (tRNA) 21 pl Transference of references 189—92 Transition probabilities, amino acids 59—60 Translation start sites 22 23 Translation stop sites 22 23 Translation, mRNA 22 Transport proteins 24 Transposon location identification 63 Tricarboxylic acid cycle (TCA), functional group 189 190 Trigger words, use in gene name recognition 235 236. Triose phosphate isomerase, structure pl 2.4 True positives and negatives 36 Truncation of words 90 Tryptophan 25 Tu, Q. Tang, H. et al. 107 112 Tu, Y., Stolovitzky, G. et al. 124 Tumor protector p53 gene 5 Tuteja, R. and Tuteja, N. 64 Twilight zone sequences 108 Two-state hidden Markov model 55 Tyrosine 25 Uetz, P. 248 Uetz, P., Giot, L. et al. 248 Unstudied genes, transference of references 189—91 Unsupervised machine learning algorithms see Clustering algorithms Unvisited state of nodes 180 Uracil 19 21 Valencia, A., Blaschke, C. et al. 195 Valine 25 Van Gool, A.J. et al. 149 Variance 34 35 Variance as function of latent dimension 94 Variation in gene names 228—29 230 231 232 Vascular endothelial growth factor 5 Velculescu, V.E. Zhang, L. et al. 64 141 Venter, J.C., Adams, M.D. et al. 1 Visited state of nodes 180 Viterbi algorithm 57—9 58 Vocabulary building 88—90 Vorbruggen, G., Onel, S. et al. 192 Walhout, A.J., Sordella, R. et al. 248 Weight matrices 51—3 83 Weight matrices, construction 52 Weighted word vectors 140 142 161 Weighted word vectors, uses 112 Weighted word-document matrix (W) 91 Weighting words 88 90—1 100 Weighting words, keywords for breathless and heartless 96 97 Weighting words, strategies 113 White, K.P., Rifkin, S.A. et al. 63 Whole-text mining 9 Wong, L. 7 Word appearance, use in name recognition 228 232—3 234 241—3 Word covariance matrix 92—4 Word distribution divergence (WDD) 157—60 Word distribution divergence (WDD), precision-recall plot 160 Word values in document similarity assessment 88 Word vector similarity, breathless and other Drosophila genes 97—8 Word vector similarity, relationship to gene expression similarity 99 100 Word vector similarity, relationship to sequence similarity (breathless) 99 Word vectors 14 84—6 157 Word vectors, creation for genes 95—6 140 Word vectors, selection of keywords 142 Word-document matrix (A) 85 Words, expression value 142—3 Words, independence assumption 204 205 Wormbase 9 11 184 Wormbase, synonym lists 229 Xie, H., Wasserman, A. et al. 188 Yandell, M.D. and Majoros, W.H. 2 7 Yang, Y. and Pederson, J.P. 89 Yeast data set, application of dendrogram pruning method 181—3 Yeast data set, cluster screening by NDPG 174—8 175 pl Yeast data set, cluster screening by NDPG, high scoring clusters 177 Yeast gene annotation, effectiveness of text classification algorithms 216—23 Yeast gene expression data, selforganizing map 70 Yeast genes, appearance of names 232 Yeast two hybrid assays 1 26 245 247—9 Yeast two hybrid assays, advantage of text-based approach 12 Yeast two hybrid assaysapplication of NEI scores 141 Yeast, assembly of functional groups 185—9 Yeast, distribution of NEI scores 133 Yeast, DNA-dependent ATPase genes 148—50 149 Yeast, expression log ratios 127 Yeast, gene expression study 126—7 Yeast, GO annotated genes 13 Yeast, keywords 144—5 Yeast, literature index 185 186 Yeast, literature-based scoring system 129—30 Yeast, NEI scores 136 Yeast, NEI scores of individual experiments 136—8 137 Yeast, neighbor expression information (NEI) scoring 132—6 Yeast, sensitivity of NDPG 187 Yeast, top fifteen genes 127—9 128 Yeast, transfer RNA structure pl 2.2 Yeast, tricarboxylic acid cycle (TCA) functional group 189 190 Yeung, K.Y. and Ruzzo, W.L. 67 Yoshida, M., Fukuda, K. et al. 238 Yu, H., Hripcsak, G. et al. 238 z-score 35 z-score, expression values of words 143—4 z-score, use in selection of key 0 z-score, use in selection of keywords 112—13 Zhang, Z. and Buchman, A.R. 149 Zhu, H., Bilgin, M. et al. 1 247 248 Zipf, G, K 88 “-ase” suffix 233 241 “-in” suffix 233 241 “autophagy” gene group, corruption study 166—7 168 “autophagy” gene group, NDPG score 167 “Cellular Location” terms, Gene Ontology 12 “ion homeostasis” gene group, corruption study 166—7 168 “ion homeostasis” gene group, NDPG score 167 “mitochondrial ribosome” gene cluster 169 “mRNA splicing” yeast genes 169 “spindle pole body assembly and function” gene cluster 169
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