Classifying pairs with trees for supervised biological network inference

TL;DR

This paper evaluates tree-based ensemble methods for supervised biological network inference, comparing local and global approaches, and demonstrates their effectiveness and interpretability through extensive experiments.

Contribution

It provides a systematic theoretical and empirical analysis of tree-based ensemble methods for pair classification in biological network inference, including new extensions for unseen node interactions.

Findings

Tree-based methods are competitive with existing approaches.

The paper extends local methods to predict interactions between unseen nodes.

Interpretability of tree-based models is highlighted.

Abstract

Networks are ubiquitous in biology and computational approaches have been largely investigated for their inference. In particular, supervised machine learning methods can be used to complete a partially known network by integrating various measurements. Two main supervised frameworks have been proposed: the local approach, which trains a separate model for each network node, and the global approach, which trains a single model over pairs of nodes. Here, we systematically investigate, theoretically and empirically, the exploitation of tree-based ensemble methods in the context of these two approaches for biological network inference. We first formalize the problem of network inference as classification of pairs, unifying in the process homogeneous and bipartite graphs and discussing two main sampling schemes. We then present the global and the local approaches, extending the later for the prediction of interactions between two unseen network nodes, and discuss their specializations to tree-based ensemble methods, highlighting their interpretability and drawing links with clustering techniques. Extensive computational experiments are carried out with these methods on various biological networks that clearly highlight that these methods are competitive with existing methods.