A heterogeneous label propagation algorithm for disease gene discovery

TaeHyun Hwang, Rui Kuang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

42 Scopus citations

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 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 regular-ization 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.

Original languageEnglish (US)
Title of host publicationProceedings of the 10th SIAM International Conference on Data Mining, SDM 2010
Pages583-594
Number of pages12
StatePublished - 2010
Event10th SIAM International Conference on Data Mining, SDM 2010 - Columbus, OH, United States
Duration: Apr 29 2010May 1 2010

Other

Other10th SIAM International Conference on Data Mining, SDM 2010
CountryUnited States
CityColumbus, OH
Period4/29/105/1/10

Keywords

  • Data integration
  • Disease gene prioritization
  • Label propagation
  • Random walk
  • Semi-supervised learning

ASJC Scopus subject areas

  • Software

Fingerprint Dive into the research topics of 'A heterogeneous label propagation algorithm for disease gene discovery'. Together they form a unique fingerprint.

  • Cite this

    Hwang, T., & Kuang, R. (2010). A heterogeneous label propagation algorithm for disease gene discovery. In Proceedings of the 10th SIAM International Conference on Data Mining, SDM 2010 (pp. 583-594)