Figure 1.

Illustration of how priors work: the priors assign initial weights to features (transcription factors) which influence how likely they are to be chosen as splitting elements in the trees of the Random Forest. As learning takes place, these weights can change, finally leading to a model that depends on both the time series data and on other data.