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"Data mining", a term which appeared in the early 1990's [ 10], describes data-driven analysis without any a priori hypothesis about the structure or the potential relationships that could exist in the data.
Historically, the term 'structural bioinformatics' describes data-driven statistical, knowledge-based research of representative non-redundant ensembles of structures to understand the statistical behavior of the system under investigation.
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This combination of data-driven co-expression networks and knowledge-based functional interactions allowed us to make optimal use of the specific strengths of both approaches: analyze molecular data within the framework of available knowledge to interpret the patterns identified in the data (knowledge-based), but still allowing inclusion of novel interactions not described before (data-driven).
Statisticians do this by developing models to describe data.
In this chapter, we describe a set of novel data-driven, computationally efficient protocols that are capable of accelerating significantly the process of building the necessary microstructure informatics for a targeted application.
This paper describes the activity of a workshop on Data-Driven Tradespace Exploration and Analysis: A Key Technical Thrust of Engineered Resilient Systems (ERS).
This work is part of a larger project which empirical results here described are then used to inform data-driven models to evaluate the impact of different urban logistics initiatives at retail malls.
This paper describes the development of a tool, DialogStudio, to support the development and management of data-driven spoken dialog systems.
Data-driven decision-making.
Data-driven clustering.
See Also: Data-Driven Companies.
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