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Bicliques in Graphs with Correlated Edges: From Artificial to Biological Networks

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Applications of Evolutionary Computation (EvoApplications 2016)

Abstract

Networks representing complex biological interactions are often very intricate and rely on algorithmic tools for thorough quantitative analysis. In bi-layered graphs, identifying subgraphs of potential biological meaning relies on identifying bicliques between two sets of associated nodes, or variables – for example, diseases and genetic variants. Researchers have developed multiple approaches for forming bicliques and it is important to understand the features of these models and their applicability to real-life problems. We introduce a novel algorithm specifically designed for finding maximal bicliques in large datasets. In this study, we applied this algorithm to a variety of networks, including artificially generated networks as well as biological networks based on phenotype-genotype and phenotype-pathway interactions. We analyzed performance with respect to network features including density, node degree distribution, and correlation between nodes, with density being the major contributor to computational complexity. We also examined sample bicliques and postulate that these bicliques could be useful in elucidating the genetic and biological underpinnings of shared disease etiologies and in guiding hypothesis generation. Moving forward, we propose additional features, such as weighted edges between nodes, that could enhance our study of biological networks.

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Acknowledgments

This work was supported by National Institutes of Health grants LM009012, LM010098, and EY022300.

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Correspondence to Jason H. Moore .

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Kershenbaum, A., Cutillo, A., Darabos, C., Murray, K., Schiaffino, R., Moore, J.H. (2016). Bicliques in Graphs with Correlated Edges: From Artificial to Biological Networks. In: Squillero, G., Burelli, P. (eds) Applications of Evolutionary Computation. EvoApplications 2016. Lecture Notes in Computer Science(), vol 9597. Springer, Cham. https://doi.org/10.1007/978-3-319-31204-0_10

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  • DOI: https://doi.org/10.1007/978-3-319-31204-0_10

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