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To tap into the multiple sources of knowledge that digital tools make available requires that companies begin thinking about work differently.
The article concludes with some of the perspectives and challenges ahead for EAFM in relation to the establishment of a co-creation process as a means to integrate and utilise multiple sources of knowledge (policy makers, scientists and stakeholders) achieving extended outcomes moving towards an EAFM.
Combining multiple sources of knowledge is useful to infer meaningful relationships in systems biology.
It incorporates multiple sources of knowledge that can influence policy and the delivery of health programs and services [ 30, 31].
This is consistent with our hypothesis that rhythmic entrainment relies on the integration of multiple sources of knowledge, and not just sensory cues.
Which is in turn underpinned by the practice of interpretive medicine: integrating multiple sources of knowledge (including biomedical, biographical and professional) in a dynamic exploration and interpretation of individual illness experience [ 31].
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Werhli et al. [ 9] have extended their work to integrate multiple sources of prior knowledge and for each source express the energy function as the absolute difference between the network structure and prior knowledge matrix.
We combined the multiple sources of biological knowledge, including the RankProd and limma analyses, to create parallel interaction networks relevant to BAV using STRING [16] and CANDID [17] (Figure 1).
In this article, we present a framework to incorporate multiple sources of prior knowledge, regardless of its type, into BN learning.
In this article, we describe a framework to incorporate multiple sources of prior knowledge, regardless of its type, into Bayesian network learning.
Werhli and Husmeier (2007) extended this approach to integrate multiple sources of prior knowledge into dynamic Bayesian network (DBN) learning via MCMC sampling.
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