Workflow of Analysis Pipeline.

<p>Given a set of binding proteins and depending on the model type, the analysis pipeline predicts the functional identity of one of a TF binding partner. After a pair of binding proteins is given as the input, the pipeline calculates numerous amino acid physico-chemical properties for the pair and generates a matrix of feature vectors. This matrix of feature vectors is submitted to the Random Forest classifier that returns the predicted functional class assignment and the associated confidence score. The latter is a measure of how strongly the average feature-class probabilities of all decision trees pointed to each class based on the information gain of those decision trees.</p