Cluster Discovery in Biological Networks - Erciyes, Kayhan (Distributed and Sequential Algorithms for Bioinformatics, 2015)
Biological networks can be represented by graphs and detection of modules in such networks is then reduced to finding clusters in graphs. Clustering in graphs is basically detecting dense regions with higher number of edges between the nodes in the clusters than to the rest of the graph. Clustering is NP-hard in the general case and the huge size of biological networks make this process more difficult. We start this chapter by defining parameters for the validation of the cluster quality. We then classify and describe clustering algorithms for biological networks. Although the general graph clustering algorithms can be used for biological networks, we present algorithms that are frequently used for networks in the cell. There are a number of reported distributed clustering algorithms as we describe and we also propose two new algorithms for this purpose.