View - ResearchGate

More documents

Recommendations

Info

156 Osborne et al.Fig. 2. Flowchart of typical MMTx workflow.use, it is powerful enough that results can be analyzed with nothing morecomplex than a spreadsheet program. For demonstration purposes free textgeneRIF data (found for most genes indexed on NCBI) will be mapped to theUMLS disease/disorder semantic network to infer relationships for testicularcancer. A flowchart showing the general usage of MMTx is shown in Fig. 2.
Page 2:
Gene Function Analysis
Page 6:
METHODS IN MOLECULAR BIOLOGYGene Fu
Page 12:
PrefaceThis volume of Methods in Mo
Page 16:
Prefaceixcolleagues demonstrate how
Page 20:
xiiContentsPART III EXPERIMENTAL ME
Page 26:
ICOMPUTATIONAL METHODS I
Page 34:
4 BidautTable 1Input File Format Us
Page 38:
6 BidautTable 2Folder Layout to Use
Page 42:
8 Bidaut• alphaA: this is the num
Page 46:
10 Bidautcomputing the maximum corr
Page 50:
12 BidautFig. 3. The complete Clutr
Page 54:
Table 3Some Identified Patterns (5,
Page 58:
16 BidautFig. 4. This is a comparis
Page 62:
18 BidautReferences1. Hughes, T. R.
Page 66:
20 Kirov et al.way to associate gen
Page 70:
22 Kirov et al.based on a study ass
Page 74:
24 Kirov et al.1. Retrieve the gene
Page 78:
26Fig. 1. Functional associations f
Page 82:
28 Kirov et al.Fig. 2. Pathway anal
Page 86:
30 Kirov et al.3. Gene symbols usag
Page 90:
32 Kirov et al.9. OBO_Team, Open Bi
Page 94:
3Estimating Gene Function With Leas
Page 98:
Estimating Gene Function With LS-NM
Page 102:
Page 106:
Page 110:
Page 114:
Page 118:
Page 122:
50 Gonye et al.activity and problem
Page 126:
52 Gonye et al.Currently, PAINT can
Page 130:
54 Gonye et al.dynamic nature of th
Page 136:
Prediction Using PAINT 57represente
Page 140:
Prediction Using PAINT 59In PAINT,
Page 144:
Prediction Using PAINT 6114. On the
Page 148:
Prediction Using PAINT 634.2. Size
Page 152:
65Fig. 4. Localization of enrichmen
Page 156:
Prediction Using PAINT 673. Okubo,
Page 160:
5Prediction of Intrinsic Disorder a
Page 164:
Prediction of ID and Its Use in Fun
Page 168:
Table 1Summary of the Web Servers O
Page 172:
Page 176:
Page 180:
Page 184:
Page 188:
Page 192:
Page 196:
Page 200:
Page 204:
Page 208:
IICOMPUTATIONAL METHODS II
Page 212:
94 Crabtree et al.genomes, which is
Page 216:
96 Crabtree et al.Fig. 2. Sybil pro
Page 220:
98 Crabtree et al.Fig. 3. Computing
Page 224:
100 Crabtree et al.3.1.5.1. FILTER
Page 228:
102 Crabtree et al.3. For the sake
Page 232:
104 Crabtree et al.Fig. 5. Best bid
Page 236:
106 Crabtree et al.17. Some cluster
Page 240:
108 Crabtree et al.19. Chado—The
Page 244:
110 Dateproducts prevents the under
Page 248:
112 DateDetails of these tasks are
Page 252:
114 DateThis step creates additiona
Page 256:
116 Date>hsapiens|gi|20093443 >hsap
Page 260:
118 DateBLAST score from the match
Page 264:
Table 1A Sample of Results From Pro
Page 268:
122 DateFig. 1. A network of functi
Page 272:
124 Datedescribed by Verjovsky Marc
Page 276:
126 Dateor contracts put forth by t
Page 280: 8Bioinformatics Tools for Modeling
Page 284: Modeling Transcription Factor Targe
Page 288: VISTA Program to search for TFBSs H
Page 308: Ac 0 0 0 1 0 1 0 1 0 1 1 0 0 0 0 0
Page 328: 154 Osborne et al.are included in t
Page 334: Mining Biomedical Data Using MMTx a
Page 362: 172 Ho et al.Fig. 1. Artificial exa
Page 366: 174 Ho et al.allowing for cases whe
Page 370: 176 Ho et al.A different measure is
Page 374: 178 Ho et al.3.1.3. LA and Generali
Page 378: 180 Ho et al.The ECF-statistic can
Page 382:
182 Ho et al.In the special case of
Page 386:
184 Ho et al.Fig. 5. An illustratio
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
Page 402:
IIIEXPERIMENTAL METHODS
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
Page 438:
210 Caldwell et al.8. Thawing cells
Page 442:
212 Zhang et al.Going one step beyo
Page 446:
214 Zhang et al.Fig. 2. Generation
Page 450:
216 Zhang et al.Perform PCR cycles,
Page 454:
218 Zhang et al.Fig. 4. Schematic m
Page 458:
220 Zhang et al.Fig. 5. Replacement
Page 462:
13Construction of Simple and Effici
Page 466:
DNA Vector-Based shRNA-Expression S
Page 470:
Page 474:
Page 478:
Page 482:
Page 486:
Page 490:
Page 494:
Page 498:
Page 502:
244 Hust et al.overcome by two appr
Page 506:
246 Hust et al.Fig. 1. Schematic de
Page 510:
248 Hust et al.interaction during p
Page 514:
250 Hust et al.3.4. Titering1. Inoc
Page 518:
252 Hust et al.10. Shortly before u
Page 522:
254 Hust et al.activity by preservi
Page 526:
15A Bacterial/Yeast Merged Two-Hybr
Page 530:
Screening in Yeast With a Bacterial
Page 534:
Page 538:
Page 542:
Page 546:
Page 550:
Page 554:
Page 558:
Page 562:
Page 566:
Page 570:
Page 574:
Page 578:
Page 582:
Page 586:
Page 590:
Page 594:
16A Bacterial/Yeast Merged Two-Hybr
Page 598:
Dual Bait-Compatible Bacterial Two-
Page 602:
Page 606:
Page 610:
Page 614:
Page 618:
Page 622:
Page 626:
Page 630:
Page 634:
Page 638:
Page 642:
Page 646:
318 Thibodeau-Beganny and Joungbeen
Page 650:
320 Thibodeau-Beganny and JoungFig.
Page 654:
Page 658:
324 Thibodeau-Beganny and JoungTypi
Page 662:
Page 666:
328 Thibodeau-Beganny and JoungPCR
Page 670:
330 Thibodeau-Beganny and Joung16-1
Page 674:
332 Thibodeau-Beganny and Joung2. P
Page 678:
334 Thibodeau-Beganny and Joung11.
Page 682:
336 IndexKknockin (gene knockin) 19
show all

View - ResearchGate

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?