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The first level of distinction between real and pseudo data science involves the difference between a query and an algorithm.
"Words" are represented in a multidimensional space and linear algebra is used to calculate the relationship between a query and a categorized set of words.
SimAttack leverages a similarity metric to capture the distance between a query and pre-built user profiles gathering preliminary information about the user interests.
As described in Section 4.1, the smoothing parameter α of SimAttack influences the similarity distance between a query and the user profile.
This study is conducted with SimAttack, an efficient attack that leverages a similarity metric to capture the distance between a query and user profiles.
In QGEE, the correlation between a query and a node is calculated by vector space model (VSM), and a query correlation indicator (QCI) is designed to quantify the priority of becoming active for individual nodes.
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There's going to be a blurring between typing in a query and getting a bunch of a links… People just want answers.
The screening process is in essence a bitwise comparison between the two fingerprints of a query and a candidate structure.
Innovative overlay-based approaches [14, 15] have been created to avoid brute-force comparison between a query conformer and each and every conformer in a 3D database.
Our approach searches for conserved coexpression between a query species and a reference species, and uses this information to increase confidence of annotation transfers between genes with borderline sequence similarity.
In this paper, we report on the first of these tools, called WENDI (Web Engine for Non-obvious Drug Informattemptshatoattempts to find non-obvious relationships between a query compound and scholarly publications, biological properties, genes and diseases using multiple information sources.
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