Abstract
Identifying highly connected subgraphs in biological networks has become a powerful tool in computational biology. By definition a highly connected graph with n vertices can only be disconnected by removing more than \(\frac{n}{2}\) of its edges. This definition, however, is not suitable for bipartite graphs, which have various applications in biology, since such graphs cannot contain highly connected subgraphs. Here, we introduce a natural modification of highly connected graphs for bipartite graphs, and prove that the problem of finding such subgraphs with the maximum number of vertices in bipartite graphs is NP-hard. To address this problem, we provide an integer linear programming solution, as well as a local search heuristic. Finally, we demonstrate the applicability of our heuristic to predict protein function by identifying highly connected subgraphs in bipartite networks that connect proteins with their experimentally established functionality.
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Acknowledgments
IF and OE acknowledge support from the National Science Foundation award # DBI 1458359 and #GS 133814.
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Moon, J., Friedberg, I., Eulenstein, O. (2016). Highly Bi-Connected Subgraphs for Computational Protein Function Annotation. In: Dinh, T., Thai, M. (eds) Computing and Combinatorics . COCOON 2016. Lecture Notes in Computer Science(), vol 9797. Springer, Cham. https://doi.org/10.1007/978-3-319-42634-1_46
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DOI: https://doi.org/10.1007/978-3-319-42634-1_46
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