Because we expect the fraction of functional sites in our balanced training and testing sets to be relatively small (probably around 5 to 10%), we only plot PPVs for prediction thresholds resulting in up to 20% of the test examples being predicted positive.
Imputation is used on both the training set and on examples being predicted by AZOrange models.
This domain compatibility enhancement is also applied to examples being predicted, for compliance with the model domain.
In our simulations, both sequential and simultaneous substrate consumption examples are predicted, the accuracy of which remains to be experimentally validated.
The process "protein ubiquitination" (GO 0016567), for example, is predicted to be PPI-linked to processes related to protein catabolism while it is expression-linked to processes related to rRNA-processing and GI-linked mainly to cell cycle related processes.
When the selected node is a leaf, the value of the target variable for the new example is predicted according to the model in the leaf.
Examples are predicting travel time (Nakata and Takeuchi 2004), managing traffic signals (Zamani et al. 2010), traffic incident detection (Jin et al. 2006), analyzing traffic accident frequency (Beshah and Hill 2010; Chang and Chen 2005), and integrated systems for traffic information intelligent analysis (Hauser and Scherer 2001; Kianfar and Edara 2013; Y.-J. Wang et al. 2009).
Light, for example, was predicted to bend around the Sun by about five 10-thousandths of a degree.
According to some models of climate change, many grasslands in western North America, for example, are predicted to be at greater risk of drought due to projected increases in summer temperatures and changes to existing rainfall patterns.
AZOrange includes a module for calculating the Mahalanobis distance in descriptor space between an example being predicted and the training set.
