Abstract
We propose a computational framework to identify functional modules in multiple networks that can be constructed from different types of omics data through incorporating network topology with constitutional similarity between nodes representing biomolecules across networks. With the help of mixed integer programming, the framework outputs locally optimized modules, which can be either conserved or network-specific. We apply the framework to four yeast protein-protein interaction networks and compare with several state-of-the-art algorithms. The comparison results show that our framework outperforms the competing algorithms in term of protein complex prediction.
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