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• W2344177430 startingPage "1086" @default.
• W2344177430 abstract "One aim of synthetic biologists is to create novel and predictable biological systems from simpler modular parts. This approach is currently hampered by a lack of well-defined and characterized parts and devices. However, there is a wealth of existing biological information, which can be used to identify and characterize biological parts, and their design constraints in the literature and numerous biological databases. However, this information is spread among these databases in many different formats. New computational approaches are required to make this information available in an integrated format that is more amenable to data mining. A tried and tested approach to this problem is to map disparate data sources into a single data set, with common syntax and semantics, to produce a data warehouse or knowledge base. Ontologies have been used extensively in the life sciences, providing this common syntax and semantics as a model for a given biological domain, in a fashion that is amenable to computational analysis and reasoning. Here, we present an ontology for applications in synthetic biology design, SyBiOnt, which facilitates the modeling of information about biological parts and their relationships. SyBiOnt was used to create the SyBiOntKB knowledge base, incorporating and building upon existing life sciences ontologies and standards. The reasoning capabilities of ontologies were then applied to automate the mining of biological parts from this knowledge base. We propose that this approach will be useful to speed up synthetic biology design and ultimately help facilitate the automation of the biological engineering life cycle." @default.
• W2344177430 created "2016-06-24" @default.
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• W2344177430 date "2016-06-20" @default.
• W2344177430 modified "2023-09-24" @default.
• W2344177430 title "Data Integration and Mining for Synthetic Biology Design" @default.
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• W2344177430 doi "https://doi.org/10.1021/acssynbio.5b00295" @default.
• W2344177430 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/27110921" @default.
• W2344177430 hasPublicationYear "2016" @default.
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