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Based on this knowledge, we next formulated a query to identify the kinases harboring arginine variants.
Utilizing this conceptual representation, we formulated a query to identify kinases with variants in the C- and R-spine positions.
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The existing concept-based query languages are similar in the sense that they require the user to specify the entire query path in formulating a query.
Another way would be creating a graphical user interface for Graph Search — a series of check boxes you could toggle to formulate a query.
To formulate a query while using the SFC the user has to provide at least one triple.
This is done through a combination of using the ontological models (that provide the meaning for the data that is stored and how it is all connected) and automated query generation that relieves the human of having to understand the mechanics of formulating a query.
For example, it is possible to formulate a query that selects all devices with required device properties, which have a transceiver with certain properties, and which contain a profile that realizes two specific functions with corresponding properties.
On the other hand, on the basis of results obtained for an experimental search system referred to as Semantic Focused Crawler, we claim that enhancing a SPARQL-compliant graph-based system by an entity-type recommendation feature may reduce the number of query elements necessary to formulate a query without compromising the quality of the results.
The difference in gene expression can be used to formulate a query gene signature based on pertinent genes retrieved by statistical differentiation.
The other functionality, which we have found extremely useful, is to allow a user to formulate a query on the fly by using visualization of pathway.
For example, to retrieve the sentences that mention a binding event between 'protein andand 'protein B', we formulate a query that has three key phrases: 'protein A'proteinein B' and 'bind' among which a semantic relation 'protein A binds to protein B' holds.
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