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9Mining Biomedical Data Using MetaMap Transfer(MMTx) and the Unified Medical Language System(UMLS)John D. Osborne, Simon Lin, Lihua (Julie) Zhu, and Warren A. KibbeSummaryDetailed instruction is described for mapping unstructured, free text data into common biomedicalconcepts (drugs, diseases, anatomy, and so on) found in the Unified Medical LanguageSystem using MetaMap Transfer (MMTx). MMTx can be used in applications including miningand inferring relationship between concepts in MEDLINE publications by transforming free textinto computable concepts. MMTx is in general not designed to be an end-user program; therefore,a simple analysis is described using MMTx for users without any programming knowledge. Inaddition, two Java template files are provided for automated processing of the output fromMMTx and users can adopt this with minimum Java program experience.Key Words: Analysis; biomedical; data mining; MMTx; NLP; parsing; UMLS.1. IntroductionThe explosion of biomedical information in electronic format has posed bothopportunities and challenges to researchers wishing to analyze that information.The easy accessibility and size of the information allow a wide range ofhypotheses to be tested and questions to be asked, but much of the data is in freetext format and consequently difficult to organize and compare. The field ofnatural language processing has a variety of tools to deal with these types ofproblems, one of which (MetaMap Transfer [MMTx]) (1) is of particular interestto biomedical researchers. MMTx is one of the tools used by the NationalLibrary of Medicine (NLM) to import medical and biological vocabularies into theUnified Medical Language System (UMLS) database. A total of 143 vocabulariesFrom: Methods in Molecular Biology, vol. 408: Gene Function AnalysisEdited by: M. Ochs © Humana Press Inc., Totowa, NJ153
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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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180 Ho et al.The ECF-statistic can
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182 Ho et al.In the special case of
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184 Ho et al.Fig. 5. An illustratio
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186 Ho et al.Fig. 7. The power curv
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188 Ho et al.this section were not
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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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