In some applications of machine learning, predictive performance is all that matters. Indeed, in these cases it is often possible to build a large number of alternative models that, while different in structure, all make excellent near-optimal functional predictions. In biology, however, prediction is not enough. The key additional question is which of the many excellent predictive models is the one actually used by the living system, as optimized not by computation but by evolution. DCell provides proof-ofconcept of a system that, while optimizing functional prediction, respects biological structure.

A fast performing, dictionary-based tagger [1] constitutes EXTRACT's core. The tagger relies on a set of dictionaries that map biological names to corresponding terms in biological ontologies, or to pertinent records in public biological databases.