Semantics of Multimodal Network Models
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Bioinformatics
Next generation software for functional trend analysis
Bioinformatics
Reconstructing the topology of protein complexes
RECOMB'07 Proceedings of the 11th annual international conference on Research in computational molecular biology
Bioinformatics
MFMS: maximal frequent module set mining from multiple human gene expression data sets
Proceedings of the 12th International Workshop on Data Mining in Bioinformatics
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Analysis of molecular interaction networks is pervasive in systems biology. This research relies almost entirely on graphs for modeling interactions. However, edges in graphs cannot represent multi-way interactions among molecules, which occur very often within cells. Hypergraphs may be better representations for such interactions, since hyperedges can naturally represent relationships among multiple molecules. Here we propose using hypergraphs to capture the uncertainty that is inherent in reverse engineering gene-gene networks from systems biology datasets. Some subsets of nodes may induce highly varying subgraphs across an ensemble of high-scoring networks inferred by a reverse engineering algorithm. We provide a novel formulation of hyperedges to capture this uncertainty in network topology. We propose a clustering-based approach to discover hyperedges. We show that our approach can recover hyperedges planted in synthetic datasets with high precision and recall. We apply our techniques to a published dataset of pathway structures inferred from quantitative genetic interaction data in S. cerevisiae related to the unfolded protein response in the endoplasmic reticulum (ER). Our approach discovers several hyperedges that capture the uncertain connectivity of genes in specific pathways and complexes related to the ER. Our work demonstrates that molecular interaction hypergraphs are powerful representations for capturing uncertainty in network structure. The hyperedges we discover directly suggest groups of genes for which further experiments may be required in order to precisely discover interaction patterns.