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

Abstract

BackgroundExtensive 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.ResultsSince 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 the extracted clusters. Our method is fast and efficient, mimicking the multiplicative update rules commonly employed in algorithms for non-negative matrix factorization. It facilitates the decomposition of networks on a chosen scale and therefore allows for analysis and interpretation of structures on various resolution levels. Applying our algorithm to a tripartite disease-gene-protein complex network, we were able to structure this graph on a large scale into clusters that are functionally correlated and biologically meaningful. Locally, smaller clusters enabled reclassification or annotation of the clusters' elements. We exemplified this for the transcription factor MECP2.ConclusionsIn order to cope with the overwhelming amount of information available from biomedical literature, we need to tackle the challenge of finding structures in large networks with nodes of multiple types. To this end, we presented a novel fuzzy k-partite graph partitioning algorithm that allows the decomposition of these objects in a comprehensive fashion. We validated our approach both on artificial and real-world data. It is readily applicable to any further problem.

Navigating Traditional Chinese Medicine Network Pharmacology and Computational Tools

“…In general, given a network G = ( V , E ), where V is a set of nodes and E is a set of edges, if V has k subsets and no two nodes in the same subset are adjacent, G is called a k -partite network or k -mode network [49]. A network with two partitions is a bipartite network.…”

“…The proposed approach performed better than the nearest neighbor- and weight-based algorithms. Fuzzy clustering and spectral coclustering algorithms were applied for k -partite network analysis in network pharmacology [49, 51]. A tripartite disease-gene-protein complex network was decomposed by using the fuzzy clustering algorithm to determine structures in a network with multiple types of nodes.…”

Elucidation of the Mechanism of Indonesian Traditional Medicine (Jamu) Based on Case Studies of Type 2 Diabetes Networks

“…Graph used in this research consists of 3 components there are active compound, target protein and GO (Figure 1). According to [5] Tri-partite graph that is established between the active compound and target protein, target protein and GO biological process in the form of binary data (0 and 1). On further analysis Graph Tri-partite formed called matrix A. Matrix A in the graph is worth 1 score if the connection between the compounds with protein, or a protein with GO biological process.…”

Network-based metabolite ratios for an improved functional characterization of genome-wide association study results

“…ROC analysis [39] was used to quantify the separation of the two distributions. To assess the significance of this observed AUC score, we performed graph randomization by edge rewiring on the distance-weighted graph as described in [50]. During each randomization step the target nodes of two randomly chosen edges are exchanged.…”