Learning Type Extension Trees for Metal Bonding State Prediction

Type Extension Trees (TET) have been recently introduced as an expressive

representation language allowing to encode complex combinatorial features

of relational entities. They can be efficiently learned with a greedy search

strategy driven by a generalized relational information gain and a discriminant

function. In predicting the metal bonding state of proteins, TET achieve significant

improvements over manually curated motifs, and the expressiveness of combinatorial

features significantly contributes to such performance. Preliminary collective

classification results seem to indicate it as a promising direction for further

research.