Further research is needed to refine predictive models that are needed to facilitate the development of more adequate, individual rehabilitation programs.
However, the paucity of below-ground tree root data globally still limits our ability to develop and refine predictive models for different species and to refine or enhance recommendations on tree density and pattern to achieve erosion control.
Recent progress in molecular psychiatry and the use of multiscale datasets coupled with machine learning techniques may help to refine predictive models of conversion to bipolar disorder in clinical and population samples (Kapczinski and Passos 2015).
Finally, the paucity of below-ground tree-root data globally still limits the ability of practitioners to develop and refine predictive models for different species and/or clones and to refine or enhance recommendations on tree density and pattern to achieve erosion control.
Such work is important to refine predictive models of rabies propagation that use rivers (or other discrete barriers such as mountain chains) and human density indices to predict the rate of propagation of rabies (e.g. Smith et al. 2002; Russell et al. 2005, 2006).
Incorporating these capabilities into a new clinical workflow that actively refines predictive models by adjusting to new data should improve case management, risk prediction for HAIs, detection of outbreaks, and antimicrobial stewardship.
Recently, there has been a number of works in the literature that focus on refining predictive models for individual TFBS by using a wide range of features that have been shown to correlate well with regulatory regions in general and with TFBSs in particular, without necessarily modeling the CRM structure (Narlikar et al., 2007; Naughton et al., 2006; Pudimat et al., 2004; Sharon and Segal, 2007).
Predictive modeling the physician assistant supply: 2010-2025.
An examination of specific sources and the extent of variability across these host-pathogen combinations would be needed in order to refine the predictive model.
This information would be useful for refining the predictive model.
One technique is called "predictive modeling".
