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Networks are ubiquitous in biology and computational approaches have beenlargely investigated for their inference. In particular, supervised machinelearning methods can be used to complete a partially known network byintegrating various measurements. Two main supervised frameworks have beenproposed: the local approach, which trains a separate model for each networknode, and the global approach, which trains a single model over pairs of nodes.Here, we systematically investigate, theoretically and empirically, theexploitation of tree-based ensemble methods in the context of these twoapproaches for biological network inference. We first formalize the problem ofnetwork inference as classification of pairs, unifying in the processhomogeneous and bipartite graphs and discussing two main sampling schemes. Wethen present the global and the local approaches, extending the later for theprediction of interactions between two unseen network nodes, and discuss theirspecializations to tree-based ensemble methods, highlighting theirinterpretability and drawing links with clustering techniques. Extensivecomputational experiments are carried out with these methods on variousbiological networks that clearly highlight that these methods are competitivewith existing methods.

Classifying pairs with trees for supervised biological network inference