Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/113729
DC FieldValue
dc.titleExtraction by example: Induction of structural rules for the analysis of molecular sequence data from heterogeneous sources
dc.contributor.authorMiotto, O.
dc.contributor.authorTan, T.W.
dc.contributor.authorBrusic, V.
dc.date.accessioned2014-12-01T06:57:46Z
dc.date.available2014-12-01T06:57:46Z
dc.date.issued2005
dc.identifier.citationMiotto, O.,Tan, T.W.,Brusic, V. (2005). Extraction by example: Induction of structural rules for the analysis of molecular sequence data from heterogeneous sources. Lecture Notes in Computer Science 3578 : 398-405. ScholarBank@NUS Repository.
dc.identifier.issn03029743
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/113729
dc.description.abstractBiological research requires information from multiple data sources that use a variety of database-specific formats. Manual gathering of information is time consuming and error-prone, making automated data aggregation a compelling option for large studies. We describe a method for extracting information from diverse sources that involves structural rules specified by example. We developed a system for aggregation of biological knowledge (ABK) and used it to conduct an epidemiological study of dengue virus (DENV) sequences. Additional information on geographical origin and isolation date is critical for understanding evolutionary relationships, but this data is inconsistently structured in database entries. Using three public databases, we found that structural rules can be used successfully even when applied on inconsistently structured data that is distributed across multiple fields. High reusability, combined with the ability to integrate analysis tools, make this method suitable for a wide variety of large-scale studies involving viral sequences. © Springer-Verlag Berlin Heidelberg 2005.
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentINSTITUTE OF SYSTEMS SCIENCE
dc.contributor.departmentBIOCHEMISTRY
dc.description.sourcetitleLecture Notes in Computer Science
dc.description.volume3578
dc.description.page398-405
dc.identifier.isiutNOT_IN_WOS
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