  Public repositories for genome and proteome annotations, such as the Gene
Ontology (GO), rarely stores negative annotations, i.e. proteins not possessing
a given function. This leaves undefined or ill defined the set of negative
examples, which is crucial for training the majority of machine learning
methods inferring proteins functions. Automated techniques to choose reliable
negative proteins are thereby required to train accurate function prediction
models. This study proposes the first extensive analysis of the temporal
evolution of protein annotations in the GO repository. Novel annotations
registered through the years have been analyzed to verify the presence of
annotation patterns in the GO hierarchy. Our research supplied fundamental
clues about proteins likely to be unreliable as negative examples, that we
verified into a novel algorithm of our own construction, validated on two
organisms in a genome wide fashion against approaches proposed to choose
negative examples in the context of functional prediction.
