- Page 2 and 3: BIOINFORMATICS ALGORITHMS
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- Page 6 and 7: vi CONTENTS 6 A Survey of Seeding f
- Page 8 and 9: PREFACE Bioinformatics, broadly def
- Page 10 and 11: CONTRIBUTORS Sudha Balla, Departmen
- Page 12 and 13: CONTRIBUTORS xiii Steven Hecht Orza
- Page 14 and 15: 1 EDUCATING BIOLOGISTS IN THE 21ST
- Page 16 and 17: EDUCATING BIOLOGISTS IN THE 21ST CE
- Page 18 and 19: EDUCATING BIOLOGISTS IN THE 21ST CE
- Page 20 and 21: 2 DYNAMIC PROGRAMMING ALGORITHMS FO
- Page 22 and 23: SEQUENCE ALIGNMENT: GLOBAL, LOCAL,
- Page 24 and 25: ecurrence: SEQUENCE ALIGNMENT: GLOB
- Page 26 and 27: SEQUENCE ALIGNMENT: GLOBAL, LOCAL,
- Page 28 and 29: SEQUENCE ALIGNMENT: GLOBAL, LOCAL,
- Page 30 and 31: DYNAMIC PROGRAMMING ALGORITHMFOR RN
- Page 32 and 33: DYNAMIC PROGRAMMING ALGORITHMFOR RN
- Page 34 and 35: DYNAMIC PROGRAMMING ALGORITHMS FOR
- Page 36 and 37: REFERENCES 25 the flexible structur
- Page 40 and 41: 3 GRAPH THEORETICAL APPROACHES TO D
- Page 42 and 43: GRAPH THEORY BACKGROUND 31 beginnin
- Page 44 and 45: GRAPH THEORY BACKGROUND 33 FIGURE 3
- Page 46 and 47: GRAPH THEORY BACKGROUND 35 chordal
- Page 48 and 49: GRAPH THEORY BACKGROUND 37 decompos
- Page 50 and 51: RECONSTRUCTING PHYLOGENIES 39 are (
- Page 52 and 53: RECONSTRUCTING PHYLOGENIES 41 only
- Page 54 and 55: FORMATION OF MULTIPROTEIN COMPLEXES
- Page 56 and 57: 3.4.1 Ribosomal Assembly FORMATION
- Page 58 and 59: FORMATION OF MULTIPROTEIN COMPLEXES
- Page 60 and 61: FORMATION OF MULTIPROTEIN COMPLEXES
- Page 62 and 63: ACKNOWLEDGMENTS REFERENCES 51 This
- Page 64 and 65: REFERENCES 53 37. Golumbic MC, Hart
- Page 66 and 67: 4 ADVANCES IN HIDDEN MARKOV MODELS
- Page 68 and 69: HIDDEN MARKOV MODELS FOR SEQUENCE A
- Page 70 and 71: HIDDEN MARKOV MODELS FOR SEQUENCE A
- Page 72 and 73: HIDDEN MARKOV MODELS FOR SEQUENCE A
- Page 74 and 75: ALTERNATIVES TO VITERBI DECODING 63
- Page 76 and 77: Noncoding Coding Intron (a) Without
- Page 78 and 79: also have this same label). We get
- Page 80 and 81: change as follows: GENERALIZED HIDD
- Page 82 and 83: 0.00004 0.00002 0.00000 0 20000 400
- Page 84 and 85: HMMS WITH MULTIPLE OUTPUTS OR EXTER
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HMMS WITH MULTIPLE OUTPUTS OR EXTER
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HMMS WITH MULTIPLE OUTPUTS OR EXTER
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HMMS WITH MULTIPLE OUTPUTS OR EXTER
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TRAINING THE PARAMETERS OF AN HMM 8
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CONCLUSION 85 of parameters compare
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REFERENCES 87 4. Altun Y, Tsochanta
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REFERENCES 89 42. Krogh A. Using da
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REFERENCES 91 77. Xu EW, Kearney P,
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94 SORTING- AND FFT-BASED TECHNIQUE
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96 SORTING- AND FFT-BASED TECHNIQUE
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98 SORTING- AND FFT-BASED TECHNIQUE
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100 SORTING- AND FFT-BASED TECHNIQU
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102 SORTING- AND FFT-BASED TECHNIQU
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104 SORTING- AND FFT-BASED TECHNIQU
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106 SORTING- AND FFT-BASED TECHNIQU
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108 SORTING- AND FFT-BASED TECHNIQU
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110 SORTING- AND FFT-BASED TECHNIQU
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112 SORTING- AND FFT-BASED TECHNIQU
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114 SORTING- AND FFT-BASED TECHNIQU
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6 A SURVEY OF SEEDING FOR SEQUENCE
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ALIGNMENTS 119 6.2.1 Formal Definit
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TRADITIONAL APPROACHES TO HEURISTIC
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TRADITIONAL APPROACHES TO HEURISTIC
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TRADITIONAL APPROACHES TO HEURISTIC
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MORE CONTEMPORARY SEEDING APPROACHE
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MORE CONTEMPORARY SEEDING APPROACHE
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MORE CONTEMPORARY SEEDING APPROACHE
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MORE COMPLICATED SEED DESCRIPTIONS
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MORE COMPLICATED SEED DESCRIPTIONS
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MORE COMPLICATED SEED DESCRIPTIONS
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SOME THEORETICAL ISSUES IN ALIGNMEN
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REFERENCES 141 6. Brown DG. Optimiz
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7 THE COMPARISON OF PHYLOGENETIC NE
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INTRODUCTION 145 known phylogeny re
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BASIC DEFINITIONS 147 The undirecte
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BASIC DEFINITIONS 149 N1 displays N
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A B C A B C SUBTREES AND SUBNETWORK
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SUBTREES AND SUBNETWORKS 153 of x a
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SUBTREES AND SUBNETWORKS 155 1. it
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SUPERTREES AND SUPERNETWORKS 157 By
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SUPERTREES AND SUPERNETWORKS 159 Go
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SUPERTREES AND SUPERNETWORKS 161 Th
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RECONCILIATION OF GENE TREES AND SP
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RECONCILIATION OF GENE TREES AND SP
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RECONCILIATION OF GENE TREES AND SP
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RECONCILIATION OF GENE TREES AND SP
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RECONCILIATION OF GENE TREES AND SP
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REFERENCES 173 21. Gòrecki P, Tiur
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8 FORMAL MODELS OF GENE CLUSTERS An
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8.2 GENOME PLASTICITY 8.2.1 Genome
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GENOME PLASTICITY 181 FIGURE 8.2 An
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BASIC CONCEPTS 183 “more or less
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BASIC CONCEPTS 185 of {m, o, s}. On
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MODELS OF GENE CLUSTERS 187 Definit
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4, 2, 3, 1, 11, 10, 9, 8, 7, 6, 5 4
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MODELS OF GENE CLUSTERS 191 FIGURE
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MODELS OF GENE CLUSTERS 193 another
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MODELS OF GENE CLUSTERS 195 of gene
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MODELS OF GENE CLUSTERS 197 The two
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REFERENCES 199 flexibility by bound
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REFERENCES 201 28. Hoberman R, Dura
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9 INTEGER LINEAR PROGRAMMING TECHNI
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BASIC PROBLEM SPECIFICATION 205 a n
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INTEGER LINEAR PROGRAMMING FORMULAT
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INTEGER LINEAR PROGRAMMING FORMULAT
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9.4 EXTENSIONS AND VARIATIONS EXTEN
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i=1 EXTENSIONS AND VARIATIONS 213 H
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9.5 COMPUTATIONAL RESULTS COMPUTATI
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DISCUSSION 217 TABLE 9.2 Cluster Si
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DISCUSSION 219 FIGURE 9.5 Manually
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ACKNOWLEDGMENTS REFERENCES 221 We t
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224 EFFICIENT COMBINATORIAL ALGORIT
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226 EFFICIENT COMBINATORIAL ALGORIT
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228 EFFICIENT COMBINATORIAL ALGORIT
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230 EFFICIENT COMBINATORIAL ALGORIT
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232 EFFICIENT COMBINATORIAL ALGORIT
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234 EFFICIENT COMBINATORIAL ALGORIT
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236 EFFICIENT COMBINATORIAL ALGORIT
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238 EFFICIENT COMBINATORIAL ALGORIT
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11 ALGORITHMS FOR MULTIPLEX PCR PRI
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INTRODUCTION 243 problem: given a s
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1. p hybridizes at position t of f
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Thus, constraints 11.7 can be repla
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A GREEDY ALGORITHM 249 FIGURE 11.3
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EXPERIMENTAL RESULTS 251 11.5.1 Amp
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#primers/(2x#SNPs) (%) #primers/(2x
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TABLE 11.2 (Continued ) EXPERIMENTA
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REFERENCES 257 p, discard all candi
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12 RECENT DEVELOPMENTS IN ALIGNMENT
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12.2 MULTIPLE SEQUENCE ALIGNMENT 12
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MULTIPLE SEQUENCE ALIGNMENT 263 The
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MOTIF FINDING 265 Marsan and Sagot
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BIOLOGICAL NETWORK ANALYSIS 267 mul
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DISCUSSION 269 an interaction pair
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REFERENCES 271 13. Bucka-Lassen K,
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REFERENCES 273 52. Lee C, Grasso C,
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REFERENCES 275 90. Stormo GD, Hartz
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PART III MICROARRAY DESIGN AND DATA
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280 ALGORITHMS FOR OLIGONUCLEOTIDE
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282 ALGORITHMS FOR OLIGONUCLEOTIDE
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284 ALGORITHMS FOR OLIGONUCLEOTIDE
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286 ALGORITHMS FOR OLIGONUCLEOTIDE
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288 ALGORITHMS FOR OLIGONUCLEOTIDE
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290 ALGORITHMS FOR OLIGONUCLEOTIDE
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292 ALGORITHMS FOR OLIGONUCLEOTIDE
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294 ALGORITHMS FOR OLIGONUCLEOTIDE
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296 ALGORITHMS FOR OLIGONUCLEOTIDE
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298 ALGORITHMS FOR OLIGONUCLEOTIDE
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300 ALGORITHMS FOR OLIGONUCLEOTIDE
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14 CLASSIFICATION ACCURACY BASED MI
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INTRODUCTION 305 Decomposition (SVD
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METHODS 307 Note that in most of th
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estimated as K� 1 ai,j = aik,j. d
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METHODS 311 [7]. The KNN-classifier
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ROWimpute-KNN ROWimpute-SVM KNNimpu
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ROWimpute-KNN ROWimpute-SVM KNNimpu
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Classification accuracies of SRBCT
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ROWimpute-KNN ROWimpute-SVM KNNimpu
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ROWimpute-KNN ROWimpute-SVM KNNimpu
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Classification accuracies of SRBCT
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REFERENCES 325 From these two plots
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REFERENCES 327 18. Troyanskaya OG,
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330 META-ANALYSIS OF MICROARRAY DAT
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332 META-ANALYSIS OF MICROARRAY DAT
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334 META-ANALYSIS OF MICROARRAY DAT
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336 META-ANALYSIS OF MICROARRAY DAT
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338 META-ANALYSIS OF MICROARRAY DAT
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340 META-ANALYSIS OF MICROARRAY DAT
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342 META-ANALYSIS OF MICROARRAY DAT
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344 META-ANALYSIS OF MICROARRAY DAT
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346 META-ANALYSIS OF MICROARRAY DAT
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348 META-ANALYSIS OF MICROARRAY DAT
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350 META-ANALYSIS OF MICROARRAY DAT
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352 META-ANALYSIS OF MICROARRAY DAT
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16 PHASING GENOTYPES USING A HIDDEN
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A HIDDEN MARKOV MODEL FOR RECOMBINA
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LEARNING THE HMM FROM UNPHASED GENO
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LEARNING THE HMM FROM UNPHASED GENO
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LEARNING THE HMM FROM UNPHASED GENO
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EXPERIMENTAL RESULTS 365 It is also
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DISCUSSION 367 TABLE 16.1 Phasing A
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GERBIL PHASE fastPHASE 0.4 0.35 0.3
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REFERENCES 371 however, that direct
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17 ANALYTICAL AND ALGORITHMIC METHO
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INTRODUCTION 375 The use of real ha
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follows: X11 = 2N11 + N12 + N21 X21
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METHODS 379 FIGURE 17.1 The likelih
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METHODS 381 TABLE 17.3 Tests for Ha
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METHODS 383 The sixth stochastic al
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RESULTS 385 TABLE 17.4 The Distribu
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RESULTS 387 TABLE 17.6 The Distribu
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DISCUSSION 389 2SNP also produced r
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ACKNOWLEDGMENTS 391 haplotypes need
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REFERENCES 393 16. Hill WG. Estimat
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18 OPTIMIZATION METHODS FOR GENOTYP
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Tag-restricted haplotype n Complete
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INFORMATIVE SNP SELECTION 399 from
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DISEASE ASSOCIATION SEARCH 401 18.2
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DISEASE ASSOCIATION SEARCH 403 18.3
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Below is the formal description of
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RESULTS AND DISCUSSION 407 to decid
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RESULTS AND DISCUSSION 409 � Comp
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RESULTS AND DISCUSSION 411 TABLE 18
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RESULTS AND DISCUSSION 413 nonindex
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REFERENCES 415 20. Lee PH, Shatkay
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19 TOPOLOGICAL INDICES IN COMBINATO
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TOPOLOGICAL INDICES 421 The quantit
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Theorem 19.2 Let T = (V, E) be a tr
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HOSOYA POLYNOMIAL 425 The Laplacian
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H2(G, x) = � {u,v}⊆V INVERSE WI
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HEXAGONAL SYSTEMS 429 hexagonal sys
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C 2 HEXAGONAL SYSTEMS 431 FIGURE 19
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THE WIENER INDEX OF PEPTOIDS 433 Th
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if R ≥ L, then π(Lp) = i; Lp = L
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REFERENCES 437 19. Entringer RC, Me
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20 EFFICIENT ALGORITHMS FOR STRUCTU
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COMPOUND REPRESENTATION 441 FIGURE
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COMPOUND REPRESENTATION 443 breakag
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TABLE 20.1 Bond List of Aspirin Bon
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COMPOUND REPRESENTATION 447 20.2.5
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Initial class value for node A A 3
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CHEMICAL COMPOUND DATABASE 451 In c
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CHEMICAL COMPOUND DATABASE 453 taki
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CHEMICAL COMPOUND DATABASE 455 Othe
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REFERENCES 457 lab may take months
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REFERENCES 459 22. Curco D, Rodrigu
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REFERENCES 461 61. An J, Nakama T,
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REFERENCES 463 101. Shen J. HAD An
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466 COMPUTATIONAL APPROACHES TO PRE
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468 COMPUTATIONAL APPROACHES TO PRE
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470 COMPUTATIONAL APPROACHES TO PRE
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472 COMPUTATIONAL APPROACHES TO PRE
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474 COMPUTATIONAL APPROACHES TO PRE
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476 COMPUTATIONAL APPROACHES TO PRE
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478 COMPUTATIONAL APPROACHES TO PRE
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480 COMPUTATIONAL APPROACHES TO PRE
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482 COMPUTATIONAL APPROACHES TO PRE
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484 COMPUTATIONAL APPROACHES TO PRE
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486 COMPUTATIONAL APPROACHES TO PRE
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488 COMPUTATIONAL APPROACHES TO PRE
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490 COMPUTATIONAL APPROACHES TO PRE
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INDEX 2SNP computer program 383, 38
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degeneracy 101-104, 112 degenerate
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lowest p-value method 484-486 max-g
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pseudoknots 20 p-value 339-343, 347
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