Cluster Discovery in Biological Networks

• Published in: Distributed and Sequential Algorithms for Bioinformatics Vol. 23; pp. 241 - 274
• Main Author: Erciyes, Kayhan
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2015; Springer International Publishing
• Series: Computational Biology
• Subjects: Algorithms & data structures; Biological Network; Cluster Algorithm; Edge Betweenness; Fiedler Vector; Life sciences: general issues; Maths for computer scientists; Unweighted Graph
• ISBN: 9783319249643; 3319249649
• ISSN: 1568-2684
• DOI: 10.1007/978-3-319-24966-7_11

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.