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Identifying Differentially Expressed Gene Combinations 185Fig. 6. Comparisons of power for detecting cross patterns as a function of the angle θbetween the correlations of the two classes. Methods are identified by different shades ofgray. Eigenvalues used to simulate the bivariate normal are listed at the top of each panel.Now consider separately the two versions of the ECF-statistic. The ECF y|xinvestigates how well one can predict class using y for a fixed x, whereas theECF x|yinvestigates how well one can predict class using x for a fixed y. Figure 7shows power of both versions in an additional scenario in which the ellipsoids areyet narrower than in Fig. 6. The ECF x|yand ECF y|xshow markedly differentbehavior, as the power of ECF y|xdecreases when the data of phenotypic class 2 is
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Gene Function Analysis
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METHODS IN MOLECULAR BIOLOGYGene Fu
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PrefaceThis volume of Methods in Mo
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Prefaceixcolleagues demonstrate how
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xiiContentsPART III EXPERIMENTAL ME
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ICOMPUTATIONAL METHODS I
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4 BidautTable 1Input File Format Us
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6 BidautTable 2Folder Layout to Use
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8 Bidaut• alphaA: this is the num
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10 Bidautcomputing the maximum corr
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12 BidautFig. 3. The complete Clutr
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Table 3Some Identified Patterns (5,
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16 BidautFig. 4. This is a comparis
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18 BidautReferences1. Hughes, T. R.
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20 Kirov et al.way to associate gen
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22 Kirov et al.based on a study ass
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24 Kirov et al.1. Retrieve the gene
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26Fig. 1. Functional associations f
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28 Kirov et al.Fig. 2. Pathway anal
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30 Kirov et al.3. Gene symbols usag
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32 Kirov et al.9. OBO_Team, Open Bi
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3Estimating Gene Function With Leas
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Estimating Gene Function With LS-NM
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50 Gonye et al.activity and problem
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52 Gonye et al.Currently, PAINT can
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54 Gonye et al.dynamic nature of th
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Prediction Using PAINT 57represente
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Prediction Using PAINT 59In PAINT,
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Prediction Using PAINT 6114. On the
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Prediction Using PAINT 634.2. Size
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65Fig. 4. Localization of enrichmen
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Prediction Using PAINT 673. Okubo,
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5Prediction of Intrinsic Disorder a
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Prediction of ID and Its Use in Fun
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Table 1Summary of the Web Servers O
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IICOMPUTATIONAL METHODS II
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94 Crabtree et al.genomes, which is
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96 Crabtree et al.Fig. 2. Sybil pro
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98 Crabtree et al.Fig. 3. Computing
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100 Crabtree et al.3.1.5.1. FILTER
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102 Crabtree et al.3. For the sake
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104 Crabtree et al.Fig. 5. Best bid
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106 Crabtree et al.17. Some cluster
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108 Crabtree et al.19. Chado—The
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110 Dateproducts prevents the under
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112 DateDetails of these tasks are
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114 DateThis step creates additiona
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116 Date>hsapiens|gi|20093443 >hsap
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118 DateBLAST score from the match
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Table 1A Sample of Results From Pro
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122 DateFig. 1. A network of functi
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124 Datedescribed by Verjovsky Marc
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126 Dateor contracts put forth by t
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8Bioinformatics Tools for Modeling
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Modeling Transcription Factor Targe
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VISTA Program to search for TFBSs H
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Ac 0 0 0 1 0 1 0 1 0 1 1 0 0 0 0 0
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154 Osborne et al.are included in t
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156 Osborne et al.Fig. 2. Flowchart
Page 336: 158 Osborne et al.UMLS source abbre
Page 340: 160 Osborne et al.Fig. 3. Querying
Page 344: 162 Osborne et al.3.4.2. Installati
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Page 352: 166 Osborne et al.public class MMTx
Page 356: 168 Osborne et al.Fig. 4. Input tes
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Page 390: 186 Ho et al.Fig. 7. The power curv
Page 394: 188 Ho et al.this section were not
Page 398: 190 Ho et al.References1. Schena, M
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Page 406: 194 Caldwell et al.for sequences th
Page 410: 196 Caldwell et al.query because it
Page 414: 198 Caldwell et al.Fig. 1. (A) Prot
Page 418: 200 Caldwell et al.outside primer o
Page 422: 202 Caldwell et al.5. Targeting scr
Page 426: 204 Caldwell et al.will allow the s
Page 430: 206 Caldwell et al.3.1.6. Plasmid P
Page 434: 208 Caldwell et al.PCR amplify the
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210 Caldwell et al.8. Thawing cells
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212 Zhang et al.Going one step beyo
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214 Zhang et al.Fig. 2. Generation
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216 Zhang et al.Perform PCR cycles,
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218 Zhang et al.Fig. 4. Schematic m
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220 Zhang et al.Fig. 5. Replacement
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13Construction of Simple and Effici
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DNA Vector-Based shRNA-Expression S
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244 Hust et al.overcome by two appr
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246 Hust et al.Fig. 1. Schematic de
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248 Hust et al.interaction during p
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250 Hust et al.3.4. Titering1. Inoc
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252 Hust et al.10. Shortly before u
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254 Hust et al.activity by preservi
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15A Bacterial/Yeast Merged Two-Hybr
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Screening in Yeast With a Bacterial
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16A Bacterial/Yeast Merged Two-Hybr
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Dual Bait-Compatible Bacterial Two-
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318 Thibodeau-Beganny and Joungbeen
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320 Thibodeau-Beganny and JoungFig.
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324 Thibodeau-Beganny and JoungTypi
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328 Thibodeau-Beganny and JoungPCR
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330 Thibodeau-Beganny and Joung16-1
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332 Thibodeau-Beganny and Joung2. P
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334 Thibodeau-Beganny and Joung11.
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336 IndexKknockin (gene knockin) 19
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