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Mining Biomedical Data Using MMTx and UMLS 1593.1.1. Filtering Against a Specific VocabularyFiltering against a particular vocabulary set is advisable in cases wherein theuser has a very specific need to map to that vocabulary, such as mapping free textfrom clinical reports to a medical coding system such as SNOMED-CT. Unlessthere is a compelling reason (such as a license restriction), it is preferable to usethe entire Metathesaurus to give the best possible coverage. If a user is interestedin a particular vocabulary, the quickest way to check for its presence is to check:(http://www.nlm.nih.gov/research/umls/sources_by_categories.html), which listsa relatively current list of English vocabularies in UMLS.3.1.2. Filtering by Semantic TypeIt is worthwhile to take advantage of the classification of concepts in theMetathesaurus into semantic types when the data to be extracted falls into largecategories and there is no explicit requirement to map to a particular vocabulary.Semantic types (such as deformities, embryonic structures, chemicals, andso on) allow the data miner to capture data categories across vocabularies withoutknowing the details of either the vocabulary or the various concepts inUMLS. A webpage listing semantic types available in the UMLS can be foundat http://mmtx.nlm.nih.gov/semanticTypes.shtml, and the definitive listing ofsemantic types for a particular distribution of UMLS can be found in nls/mmtx/data/2004/mmtx/semdef. A typical use case may be to mine MEDLINE for statisticalassociations between semantic types or concepts.3.1.3. Filtering by Specific ConceptsIn this case, input data is filtered by a small group of manually selected conceptsthat span one or more semantic types and vocabularies. One such use case thatMMTx and UMLS have already been used for is mapping clinical reports to amanually selected set of 80 particular medical problems (6). If it is not knownwhether the UMLS Metathesaurus contains the specification of the desired concept/s,the UMLS Knowledge Source Server (UMLSKS) can be used to search forthe concepts in UMLS as shown in Fig. 3. The UMLSKS is located at http://umlsks.nlm.nih.gov/kss/servlet/Turbine/template/admin,user,KSS_login.vm and itsuse requires registration and signing of the UMLS license agreement (Note 1).3.1.4. UMLS Lacks the Required ContentIn cases wherein UMLS does not contain the required vocabulary, semanticnetwork, or concepts, it is best to directly contact the UMLS development team.The team is generally receptive to reasonable requests, although the inclusion ofany new vocabulary will have to be justified against the NLM mandate and willtake a considerable amount of time. Semantic types and concepts can be added
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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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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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