Classifying pairs with trees for supervised biological network inference.

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 a classification of pairs, unifying in the process homogeneous and bipartite graphs and discussing two

SUBMITTER: Schrynemackers M 

PROVIDER: S-EPMC4601289 | biostudies-literature | 2015 Aug

REPOSITORIES: biostudies-literature