An analysis of the predictability of subcellular locations is performed by using simple pattern recognition techniques in an attempt to capture the real dimensions of the problem at hand. Results show that there are some particular locations that does not need of high complexity classification models to be predicted with high accuracies, and some partial biological explanations are formulated. All the experiments were carried out over a set of Arabidopsis Thaliana proteins and classes were defined according to the plants GO slim.
