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In order to develop effective and efficient strategies more data driven approaches are needed.
However, the main drawback of data driven approaches is the requirement of huge historical database for model development.
Initial OpenTox work has focused on creating a Framework for the support of (Q SAR-based data driven approaches.
For modelling of such point processes, data driven approaches based on statistical techniques are usually employed to identify the stochasticity in the observed data [2].
While the end result is often similar to data driven approaches, it avoids some shortcomings of those methods.
This kind of fortuitous discovery is actually one of the goals and one of the benefits of data driven approaches.
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Subject-specific "second order" effects, associated with the individual anatomy and tracer binding, can only be corrected by subject-data driven approaches.
Data-driven approaches, which take impact factors as inputs, are prevalent in mid-term price forecasting.
Data-driven approaches to identifying future successes may work in other businesses but, Page suggests, there's something intangible that makes pop harder to plan.
Yet some of the more data-driven approaches are already seeing progress in Africa, where Brac now works in five countries.
Law-enforcement agencies around the world have since implemented their own data-driven approaches to tracking and adapting to crime trends.
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