  One challenge in gene network inference is distinguishing between direct and
indirect regulation. Some algorithms, including ARACNE and Phixer, approach
this problem by using pruning methods to eliminate redundant edges in an
attempt to explain the observed data with the simplest possible network
structure. However, we hypothesize that there may be a cost in accuracy to
simplifying the predicted networks in this way, especially due to the
prevalence of redundant connections, such as feed forward loops, in gene
networks. In this paper, we evaluate the pruning methods of ARACNE and Phixer,
and score their accuracy using receiver operating characteristic curves and
precision-recall curves. Our results suggest that while pruning can be useful
in some situations, it may have a negative effect on overall accuracy that has
not been previously studied. Researchers should be aware of both the advantages
and disadvantages of pruning when inferring networks, in order to choose the
best inference strategy for their experimental context.
