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. 2011;5 Suppl 3(Suppl 3):S5.
doi: 10.1186/1752-0509-5-S3-S5. Epub 2011 Dec 23.

Biological Network Motif Detection and Evaluation

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Free PMC article

Biological Network Motif Detection and Evaluation

Wooyoung Kim et al. BMC Syst Biol. .
Free PMC article

Abstract

Background: Molecular level of biological data can be constructed into system level of data as biological networks. Network motifs are defined as over-represented small connected subgraphs in networks and they have been used for many biological applications. Since network motif discovery involves computationally challenging processes, previous algorithms have focused on computational efficiency. However, we believe that the biological quality of network motifs is also very important.

Results: We define biological network motifs as biologically significant subgraphs and traditional network motifs are differentiated as structural network motifs in this paper. We develop five algorithms, namely, EDGEGO-BNM, EDGEBETWEENNESS-BNM, NMF-BNM, NMFGO-BNM and VOLTAGE-BNM, for efficient detection of biological network motifs, and introduce several evaluation measures including motifs included in complex, motifs included in functional module and GO term clustering score in this paper. Experimental results show that EDGEGO-BNM and EDGEBETWEENNESS-BNM perform better than existing algorithms and all of our algorithms are applicable to find structural network motifs as well.

Conclusion: We provide new approaches to finding network motifs in biological networks. Our algorithms efficiently detect biological network motifs and further improve existing algorithms to find high quality structural network motifs, which would be impossible using existing algorithms. The performances of the algorithms are compared based on our new evaluation measures in biological contexts. We believe that our work gives some guidelines of network motifs research for the biological networks.

Figures

Figure 1
Figure 1
Shapes and labels for 4-node subgraphs in an undirected network. There are six types for 4-node subgraph in an undirected network. Each type is labeled with Nauty as shown as a text accordingly.
Figure 2
Figure 2
DIP Core network: Search ratios based on the subgraph type. The ratio of frequency of each type is relatively preserved and it indicates that our algorithms can be used for the structural network motif discovery as well. Relative frequencies of each algorithm is plotted with different colors of line. The horizontal axis indicated each subgraph type for 4-node subgraphs. The vertical axis shows the relative frequency of each type. The values are shown in the table below the figure.
Figure 3
Figure 3
Y2k network: Search ratios based on the subgraph type. The ratio of frequency of each type is relatively preserved and it indicates that our algorithms can be used for the structural network motif discovery as well. The description of the plots and the table is same as in Figure 2.
Figure 4
Figure 4
After graph modify. Original network (left) and the modified network (right) after removing edges or clustering the graph, where a number of clusters and a list of removed edges are provided as a result.
Figure 5
Figure 5
GO DAG example. GO DAG example view, where the root node is a molecular function (MF) GO term.

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