Structuring heterogeneous biological information using fuzzy clustering of k-partite graphs |
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Authors: | Mara L Hartsperger Florian Blöchl Volker Stümpflen Fabian J Theis |
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Institution: | 1.Institute of Bioinformatics and Systems Biology (MIPS),Helmholtz Zentrum München - German Research Center for Environmental Health,Neuherberg,Germany;2.Department of Mathematical Science,Technische Universit?t München,Garching,Germany |
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Abstract: | Background Extensive and automated data integration in bioinformatics facilitates the construction of large, complex biological networks.
However, the challenge lies in the interpretation of these networks. While most research focuses on the unipartite or bipartite
case, we address the more general but common situation of k-partite graphs. These graphs contain k different node types and links are only allowed between nodes of different types. In order to reveal their structural organization
and describe the contained information in a more coarse-grained fashion, we ask how to detect clusters within each node type. |
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