A Heterogeneous Label Propagation Algorithm for Disease Gene Discovery

Abstract

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2010 SIAM International Conference on Data Mining (SDM)A Heterogeneous Label Propagation Algorithm for Disease Gene DiscoveryTaeHyun Hwang and Rui KuangTaeHyun Hwang and Rui Kuangpp.583 - 594Chapter DOI:https://doi.org/10.1137/1.9781611972801.51PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract 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 intutitive 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 heterogeneus 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 heterogenous network. Finally, MINProp discovered new disease-gene associations that are only reported recently. Previous chapter Next chapter RelatedDetails Published:2010ISBN:978-0-89871-703-7eISBN:978-1-61197-280-1 https://doi.org/10.1137/1.9781611972801Book Series Name:ProceedingsBook Code:PR136Book Pages:1-953Key words:Label propagation, Random walk, Semi-supervised learning, Data integration, Disease gene prioritization

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Affiliated Institutions

• University of Minnesota US
• Twin Cities Orthopedics US

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