Learning on Weighted Hypergraphs to Integrate Protein Interactions and Gene Expressions for Cancer Outcome Prediction

 

TAEHYUN HWANG¹, ZE TIAN¹,  JEAN-PIERRE KOCHER², AND RUI KUANG¹

 

¹Department of Computer Science and Engineering, University of Minnesota Twin Cities

²Bioinformatics Core, Mayo Clinic College of Medicine

Abstract

 

Building reliable models from multiple complimentary genomic data for cancer study is a crucial step towards successful cancer prognosis and a full understanding of the underlying biological principles. To tackle this challenging data integration problem, we propose a hypergraph-based machine learning algorithm called HyperGene to integrate microarray gene expressions and protein-protein interactions for cancer outcome prediction and biomarker identification. HyperGene is a robust two-step iterative method that alternatively finds the optimal outcome prediction and the optimal weighting of the marker genes guided by a protein-protein interaction network. Under the hypothesis that cancer-related genes tend to interact with each other, the HyperGene algorithm uses a protein-protein interaction network as prior knowledge by imposing a consistent weighting of interacting genes. Our experimental results on two large-scale breast cancer gene expression datasets show that HyperGene utilizing a curated protein-protein interaction network achieves significantly improved cancer outcome prediction. Moreover, HyperGene can also retrieve many known cancer genes as highly weighted marker genes.

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