A
Heterogeneous Label Propagation for Disease Gene Discovery
TAEHYUN
HWANG, AND RUI KUANG*
Department of Computer Science and Engineering, University of Minnesota Twin Cities
Abstract
Label propagation is an effective and
efficient technique to utilize local and global features in a network for
semi-supervised learning. In the literature, one challenge is how to propagate
information in heterogeneous networks comprising several subnetworks,
each of which has its own cluster structures that need to be explored
independently. In this paper, we introduce an intuitive algorithm MINProp (Mutual Interaction-based Network Propagation) and
a simple regularization framework for propagating information between subnetworks in a heterogeneous network. MINProp
sequentially performs label propagation on each individual subnetwork
with the current label information derived from the other subnetworks
and repeats this step until convergence to the global optimal solution to the
convex objective function of the regularization framework. The independent
label propagation on each subnetwork explores the
cluster structure in the subnetwork. The label
information from the other subnetworks is used to
capture mutual interactions (bicluster structures)
between the vertices in each pair of the subnetworks.
MINProp algorithm is applied to disease gene
discovery from a heterogeneous network of disease phenotypes and genes. In the
experiments, MINProp significantly output-performed
the original label propagation algorithm on a single network and the
state-of-the-art methods for discovering disease genes. The results also
suggest that MINProp is more effective in utilizing
the modular structures in a heterogeneous network.
Finally, MINProp
discovered new disease-gene associations that are only reported recently.
Supplementary
Information and Source Code