Structuring heterogeneous biological information using fuzzy clustering of k-partite graphs

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. Results: Since entities in biological networks regularly have more than one function and hence participate in more than one cluster, we developed a k-partite graph partitioning algorithm that allows for overlapping (fuzzy) clusters. It determines for each node a degree of membership to each cluster. Moreover, the algorithm estimates a weighted k-partite graph that connects ...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Mara L. Hartsperger, Florian Blöchl, Volker Stümpflen, Fabian J. Theis
• Keywords: Biological Networks, BMCBI 2010, K-partite Graph, Node Type