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Mining Biomedical Data Using MMTx and UMLS 163format input data using any commonly available spreadsheet program (such asExcel), provided the spreadsheet software can support all the lines of input datathat are required to be read in. For example, the geneRIF database is beingprocessed, an example of the format of being:139 2827859 15501399 2005-05-14 12:17 T-cell recognition of the outersurfaceprotein A (OspA) epitope is important in the induction of autoimmunityin treatment-resistant Lyme arthritis (OspA).The tabs can be substituted with “|”s in any text editor that supports find andreplace type operations so that MMTx will have an easier time with the data(see Note 3). In Microsoft Word the data could be transformed as follows:1. Open the database file (generif_basic) in Word.2. Use Ctrl-C to highlight and copy a single tab character.3. Select Edit → Find.4. Select the Replace tab.5. Select the “Find what” textbox and type Ctrl-V to paste in a tab.6. Select the “Replace with” textbox type “|” without quotes.7. Select “Replace All.”8. Save the newly formatted database.This will result in text in the default MMTx format as shown next. 139| 2827859|15501399|2005-05-14 12:17|T-cell recognition of the OspA epitope is important inthe induction of autoimmunity in treatment-resistant Lyme arthritis (OspA).3.6. Running and Handling Results From MMTxPerhaps the most difficult component of data mining with MMTx is handlingthe overwhelming amount of data that will be generated from the original inputdata. This problem is complicated by the fact that MMTx is not really designedto be an end-user program. MMTx is focused more on the production of machinereadabledata for analysis by software tools, and not for direct interruption by anend-user. Potential users must therefore overcome a fairly work-intensive initialbarrier before they can assess the utility of MMTx and UMLS. The simple exampleherein should avoid some of this but the focus on the generation of machinereadabledata means that the best way to handle MMTx generated data isprogrammatically, by either handling the output of MMTx directly or preferablythrough the Java API. Regardless of whether software tools are used to processand analyze the results, ultimately a human is needed for the final analysis.3.6.1. Choosing a Data ModelRegardless of whether the analysis will be software assisted, one considerationremains the same—choosing a data model. As discussed in Subheading 3.3.,there are three different data models. The default “strict” model utilizes the highest
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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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Modeling Transcription Factor Targe
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Page 362: 172 Ho et al.Fig. 1. Artificial exa
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Page 370: 176 Ho et al.A different measure is
Page 374: 178 Ho et al.3.1.3. LA and Generali
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Page 382: 182 Ho et al.In the special case of
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190 Ho et al.References1. Schena, M
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IIIEXPERIMENTAL METHODS
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194 Caldwell et al.for sequences th
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196 Caldwell et al.query because it
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198 Caldwell et al.Fig. 1. (A) Prot
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200 Caldwell et al.outside primer o
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202 Caldwell et al.5. Targeting scr
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204 Caldwell et al.will allow the s
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206 Caldwell et al.3.1.6. Plasmid P
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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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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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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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