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Method 4 could be improved by incorporating confidence bands instead of pointwise CIs when presenting the results graphically.
It involves a range of sub-outcomes incorporating confidence in accessing care, and trust that clinicians will listen to them and help them.
Incorporating confidence and pathways into our diffusion-based distance metric DSD, we studied whether it was best to incorporate pathway information as edges or as controlled-access highways in an augmented graph.
These improvements consist of including a statistical language model for increasing the robustness against spontaneous speech, incorporating confidence measures for evaluating the performance of on-line the speech engines (better reliability), and a flexible response generation for improving the interface ergonomics.
We show that incorporating confidence values in this way improves performance over the basic DSD method (which in turn improved the performance compared to the corresponding method based on shortest-path distances (Cao et al., 2013)) in cross-validation on each of the classical network-based function prediction methods we consider.
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Now we extend the calculation of DSD to incorporate confidence, then confidence and directed and undirected pathway edges, then confidence, pathway edges and full biological pathways.
We find that diffusion state distance (DSD), our recent diffusion-based metric for measuring dissimilarity in PPI networks, has natural extensions that incorporate confidence, directions and can even express coherent pathways by calculating DSD on an augmented graph.
In this work, we use the same DSD-based methods as in Cao et al. (2013), but instead substitute the cDSD, caDSD and capDSD matrices to incorporate confidence measures and pathways.
We stress that in each of our experiments, the function prediction method is unchanged, and does not explicitly incorporate confidence or pathway information in any way, except in that it uses the values from the cDSD, caDSD or capDSD matrix instead of from the DSD (or ordinary shortest-path distance) matrix.
Results: We define three incremental versions of DSD which we term cDSD, caDSD and capDSD, where the capDSD matrix incorporates confidence, known directed edges, and pathways into the measure of how similar each pair of nodes is according to the structure of the PPI network.
As we are interested in incorporating useful confidence information into a classifier, we combine bagging with a generalization of traditional boosting algorithm that allows confidence information to be incorporated.
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